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Autor Speidel, Stefanie |
Documentos disponibles escritos por este autor (9)
Crear una solicitud de compra Refinar búsquedaMedical Image Computing and Computer Assisted Intervention – MICCAI 2021 / de Bruijne, Marleen ; Cattin, Philippe C. ; Cotin, Stéphane ; Padoy, Nicolas ; Speidel, Stefanie ; Zheng, Yefeng ; Essert, Caroline
TÃtulo : Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 : 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I / Tipo de documento: documento electrónico Autores: de Bruijne, Marleen, ; Cattin, Philippe C., ; Cotin, Stéphane, ; Padoy, Nicolas, ; Speidel, Stefanie, ; Zheng, Yefeng, ; Essert, Caroline, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2021 Número de páginas: XXXVII, 746 p. 252 ilustraciones ISBN/ISSN/DL: 978-3-030-87193-2 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Visión por computador Inteligencia artificial IngenierÃa Informática Red de computadoras Bioinformática Sistemas de reconocimiento de patrones IngenierÃa Informática y Redes BiologÃa Computacional y de Sistemas Reconocimiento de patrones automatizado Clasificación: 006.37 Resumen: El conjunto de ocho volúmenes LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907 y 12908 constituye las actas arbitradas de la 24.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2021, celebrada en Estrasburgo, Francia. en septiembre/octubre de 2021.* Los 531 artÃculos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1630 presentaciones en un proceso de revisión doble ciego. Los artÃculos están organizados en las siguientes secciones temáticas: Parte I: segmentación de imágenes Parte II: aprendizaje automático - aprendizaje autosupervisado; aprendizaje automático: aprendizaje semisupervisado; y aprendizaje automático: aprendizaje débilmente supervisado. Parte III: aprendizaje automático: avances en la teorÃa del aprendizaje automático; aprendizaje automático: modelos de atención; aprendizaje automático: adaptación de dominios; aprendizaje automático: aprendizaje federado; aprendizaje automático: interpretabilidad / explicabilidad; y aprendizaje automático - incertidumbre Parte IV: registro de imágenes; intervenciones y cirugÃa guiadas por imágenes; ciencia de datos quirúrgicos; planificación y simulación quirúrgica; análisis de habilidades quirúrgicas y flujo de trabajo; y visualización quirúrgica y realidad mixta, aumentada y virtual. Parte V: diagnóstico asistido por ordenador; integración de imágenes con biomarcadores sin imágenes; y predicción de resultados/enfermedades. Parte VI: reconstrucción de imágenes; aplicaciones clÃnicas - cardÃacas; y aplicaciones clÃnicas - vasculares Parte VII: aplicaciones clÃnicas - abdomen; aplicaciones clÃnicas - mama; aplicaciones clÃnicas - dermatologÃa; aplicaciones clÃnicas: imágenes fetales; aplicaciones clÃnicas - pulmón; aplicaciones clÃnicas - neuroimagen - desarrollo cerebral; aplicaciones clÃnicas - neuroimagen - DWI y tractografÃa; aplicaciones clÃnicas - neuroimagen - redes cerebrales funcionales; aplicaciones clÃnicas - neuroimagen - otras; y aplicaciones clÃnicas - oncologÃa Parte VIII: aplicaciones clÃnicas - oftalmologÃa; patologÃa computacional (integrativa); modalidades - microscopÃa; modalidades - histopatologÃa; y modalidades - ultrasonido *La conferencia se realizó de manera virtual. Nota de contenido: Image Segmentation -- Noisy Labels are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation -- TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation -- Pancreas CT Segmentation by Predictive Phenotyping -- Medical Transformer: Gated Axial-Attention for Medical Image Segmentation -- Anatomy-Constrained Contrastive Learning for Synthetic Segmentation without Ground-truth -- Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels -- Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting -- Convolution-Free Medical Image Segmentation using Transformer Networks -- Consistent Segmentation of Longitudinal Brain MR Images with Spatio-Temporal Constrained Networks -- A Multi-Branch Hybrid Transformer Network for Corneal Endothelial Cell Segmentation -- TransBTS: Multimodal Brain Tumor Segmentation Using Transformer -- Automatic Polyp Segmentation via Multi-scale Subtraction Network -- Patch-Free 3D Medical Image Segmentation Driven by Super-Resolution Technique and Self-Supervised Guidance -- Progressively Normalized Self-Attention Network for Video Polyp Segmentation -- SGNet: Structure-aware Graph-based Network for Airway Semantic Segmentation -- NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scale -- AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions -- Improved Brain Lesion Segmentation with Anatomical Priors from Healthy Subjects -- CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation -- Boundary-aware Transformers for Skin Lesion Segmentation -- A Topological-Attention ConvLSTM Network and Its Application to EM Images -- BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation -- Multi-Task, Multi-Domain Deep Segmentation with Shared Representations and Contrastive Regularization for Sparse Pediatric Datasets -- TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations -- Learning Consistency- and Discrepancy-Context for 2D Organ Segmentation -- Partial-supervised Learning for Vessel Segmentation in Ocular Images -- Unsupervised Network Learning for Cell Segmentation -- MT-UDA: Towards Unsupervised Cross-Modality Medical Image Segmentation with Limited Source Labels -- Context-aware virtual adversarial training for anatomically-plausible segmentation -- Interactive segmentation via deep learning and B-spline explicit active surfaces -- Multi-Compound Transformer for Accurate Biomedical Image Segmentation -- kCBAC-Net: Deeply Supervised Complete Bipartite Networks with Asymmetric Convolutions for Medical Image Segmentation -- Multi-frame Attention Network for Left Ventricle Segmentation in 3D Echocardiography -- Coarse-to-fine Segmentation of Organs at Risk in Nasopharyngeal Carcinoma Radiotherapy -- Joint Segmentation and Quantification of Main Coronary Vessels Using Dual-branch Multi-scale Attention Network -- A Spatial Guided Self-supervised Clustering Network for Medical Image Segmentation -- Comprehensive Importance-based Selective Regularization for Continual Segmentation Across Multiple Sites -- ReSGAN: Intracranial Hemorrhage Segmentation with Residuals of Synthetic Brain CT Scans -- Refined Local-imbalance-based Weight for Airway Segmentation in CT -- Selective Learning from External Data for CT Image Segmentation -- Projective Skip-Connections for Segmentation Along a Subset of Dimensions in Retinal OCT -- MouseGAN: GAN-Based Multiple MRI Modalities Synthesis and Segmentation for Mouse Brain Structures -- Style Curriculum Learning for Robust Medical Image Segmentation -- Towards Efficient Human-Machine Collaboration: Real-Time Correction Effort Prediction for Ultrasound Data Acquisition -- Residual Feedback Network for Breast Lesion Segmentation in Ultrasound Image -- Learning to Address Intra-segment Misclassification in Retinal Imaging -- Flip Learning: Erase to Segment -- DC-Net: Dual Context Network for 2D Medical Image Segmentation -- LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation -- Superpixel-guided Iterative Learning from Noisy Labels for Medical Image Segmentation -- A hybrid attention ensemble framework for zonal prostate segmentation -- 3D-UCaps: 3D Capsules Unet for Volumetric Image Segmentation -- HRENet: A Hard Region Enhancement Network for Polyp Segmentation -- A Novel Hybrid Convolutional Neural Network for Accurate Organ Segmentation in 3D Head and Neck CT Images -- TumorCP: A Simple but Effective Object-Level Data Augmentation for Tumor Segmentation -- Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation -- Hybrid graph convolutional neural networks for anatomical segmentation -- RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans -- Hierarchical Self-Supervised Learning for Medical Image Segmentation Based on Multi-Domain Data Aggregation -- CCBANet: Cascading Context and BalancingAttention for Polyp Segmentation -- Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentation -- TUN-Det: A Novel Network for Thyroid Ultrasound Nodule Detection -- Distilling effective supervision for robust medical image segmentation with noisy labels -- On the relationship between calibrated predictors and unbiased volume estimation -- High-resolution segmentation of lumbar vertebrae from conventional thick slice MRI -- Shallow Attention Network for Polyp Segmentation -- A Line to Align: Deep Dynamic Time Warping for Retinal OCT Segmentation -- Learnable Oriented-Derivative Network for Polyp Segmentation -- LambdaUNet: 2.5D Stroke Lesion Segmentation of Diffusion-weighted MR Images. Tipo de medio : Computadora Summary : The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in machine learning theory; machine learning - attention models; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging – others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 : 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I / [documento electrónico] / de Bruijne, Marleen, ; Cattin, Philippe C., ; Cotin, Stéphane, ; Padoy, Nicolas, ; Speidel, Stefanie, ; Zheng, Yefeng, ; Essert, Caroline, . - 1 ed. . - [s.l.] : Springer, 2021 . - XXXVII, 746 p. 252 ilustraciones.
ISBN : 978-3-030-87193-2
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Visión por computador Inteligencia artificial IngenierÃa Informática Red de computadoras Bioinformática Sistemas de reconocimiento de patrones IngenierÃa Informática y Redes BiologÃa Computacional y de Sistemas Reconocimiento de patrones automatizado Clasificación: 006.37 Resumen: El conjunto de ocho volúmenes LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907 y 12908 constituye las actas arbitradas de la 24.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2021, celebrada en Estrasburgo, Francia. en septiembre/octubre de 2021.* Los 531 artÃculos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1630 presentaciones en un proceso de revisión doble ciego. Los artÃculos están organizados en las siguientes secciones temáticas: Parte I: segmentación de imágenes Parte II: aprendizaje automático - aprendizaje autosupervisado; aprendizaje automático: aprendizaje semisupervisado; y aprendizaje automático: aprendizaje débilmente supervisado. Parte III: aprendizaje automático: avances en la teorÃa del aprendizaje automático; aprendizaje automático: modelos de atención; aprendizaje automático: adaptación de dominios; aprendizaje automático: aprendizaje federado; aprendizaje automático: interpretabilidad / explicabilidad; y aprendizaje automático - incertidumbre Parte IV: registro de imágenes; intervenciones y cirugÃa guiadas por imágenes; ciencia de datos quirúrgicos; planificación y simulación quirúrgica; análisis de habilidades quirúrgicas y flujo de trabajo; y visualización quirúrgica y realidad mixta, aumentada y virtual. Parte V: diagnóstico asistido por ordenador; integración de imágenes con biomarcadores sin imágenes; y predicción de resultados/enfermedades. Parte VI: reconstrucción de imágenes; aplicaciones clÃnicas - cardÃacas; y aplicaciones clÃnicas - vasculares Parte VII: aplicaciones clÃnicas - abdomen; aplicaciones clÃnicas - mama; aplicaciones clÃnicas - dermatologÃa; aplicaciones clÃnicas: imágenes fetales; aplicaciones clÃnicas - pulmón; aplicaciones clÃnicas - neuroimagen - desarrollo cerebral; aplicaciones clÃnicas - neuroimagen - DWI y tractografÃa; aplicaciones clÃnicas - neuroimagen - redes cerebrales funcionales; aplicaciones clÃnicas - neuroimagen - otras; y aplicaciones clÃnicas - oncologÃa Parte VIII: aplicaciones clÃnicas - oftalmologÃa; patologÃa computacional (integrativa); modalidades - microscopÃa; modalidades - histopatologÃa; y modalidades - ultrasonido *La conferencia se realizó de manera virtual. Nota de contenido: Image Segmentation -- Noisy Labels are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation -- TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation -- Pancreas CT Segmentation by Predictive Phenotyping -- Medical Transformer: Gated Axial-Attention for Medical Image Segmentation -- Anatomy-Constrained Contrastive Learning for Synthetic Segmentation without Ground-truth -- Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels -- Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting -- Convolution-Free Medical Image Segmentation using Transformer Networks -- Consistent Segmentation of Longitudinal Brain MR Images with Spatio-Temporal Constrained Networks -- A Multi-Branch Hybrid Transformer Network for Corneal Endothelial Cell Segmentation -- TransBTS: Multimodal Brain Tumor Segmentation Using Transformer -- Automatic Polyp Segmentation via Multi-scale Subtraction Network -- Patch-Free 3D Medical Image Segmentation Driven by Super-Resolution Technique and Self-Supervised Guidance -- Progressively Normalized Self-Attention Network for Video Polyp Segmentation -- SGNet: Structure-aware Graph-based Network for Airway Semantic Segmentation -- NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scale -- AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions -- Improved Brain Lesion Segmentation with Anatomical Priors from Healthy Subjects -- CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation -- Boundary-aware Transformers for Skin Lesion Segmentation -- A Topological-Attention ConvLSTM Network and Its Application to EM Images -- BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation -- Multi-Task, Multi-Domain Deep Segmentation with Shared Representations and Contrastive Regularization for Sparse Pediatric Datasets -- TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations -- Learning Consistency- and Discrepancy-Context for 2D Organ Segmentation -- Partial-supervised Learning for Vessel Segmentation in Ocular Images -- Unsupervised Network Learning for Cell Segmentation -- MT-UDA: Towards Unsupervised Cross-Modality Medical Image Segmentation with Limited Source Labels -- Context-aware virtual adversarial training for anatomically-plausible segmentation -- Interactive segmentation via deep learning and B-spline explicit active surfaces -- Multi-Compound Transformer for Accurate Biomedical Image Segmentation -- kCBAC-Net: Deeply Supervised Complete Bipartite Networks with Asymmetric Convolutions for Medical Image Segmentation -- Multi-frame Attention Network for Left Ventricle Segmentation in 3D Echocardiography -- Coarse-to-fine Segmentation of Organs at Risk in Nasopharyngeal Carcinoma Radiotherapy -- Joint Segmentation and Quantification of Main Coronary Vessels Using Dual-branch Multi-scale Attention Network -- A Spatial Guided Self-supervised Clustering Network for Medical Image Segmentation -- Comprehensive Importance-based Selective Regularization for Continual Segmentation Across Multiple Sites -- ReSGAN: Intracranial Hemorrhage Segmentation with Residuals of Synthetic Brain CT Scans -- Refined Local-imbalance-based Weight for Airway Segmentation in CT -- Selective Learning from External Data for CT Image Segmentation -- Projective Skip-Connections for Segmentation Along a Subset of Dimensions in Retinal OCT -- MouseGAN: GAN-Based Multiple MRI Modalities Synthesis and Segmentation for Mouse Brain Structures -- Style Curriculum Learning for Robust Medical Image Segmentation -- Towards Efficient Human-Machine Collaboration: Real-Time Correction Effort Prediction for Ultrasound Data Acquisition -- Residual Feedback Network for Breast Lesion Segmentation in Ultrasound Image -- Learning to Address Intra-segment Misclassification in Retinal Imaging -- Flip Learning: Erase to Segment -- DC-Net: Dual Context Network for 2D Medical Image Segmentation -- LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation -- Superpixel-guided Iterative Learning from Noisy Labels for Medical Image Segmentation -- A hybrid attention ensemble framework for zonal prostate segmentation -- 3D-UCaps: 3D Capsules Unet for Volumetric Image Segmentation -- HRENet: A Hard Region Enhancement Network for Polyp Segmentation -- A Novel Hybrid Convolutional Neural Network for Accurate Organ Segmentation in 3D Head and Neck CT Images -- TumorCP: A Simple but Effective Object-Level Data Augmentation for Tumor Segmentation -- Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation -- Hybrid graph convolutional neural networks for anatomical segmentation -- RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans -- Hierarchical Self-Supervised Learning for Medical Image Segmentation Based on Multi-Domain Data Aggregation -- CCBANet: Cascading Context and BalancingAttention for Polyp Segmentation -- Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentation -- TUN-Det: A Novel Network for Thyroid Ultrasound Nodule Detection -- Distilling effective supervision for robust medical image segmentation with noisy labels -- On the relationship between calibrated predictors and unbiased volume estimation -- High-resolution segmentation of lumbar vertebrae from conventional thick slice MRI -- Shallow Attention Network for Polyp Segmentation -- A Line to Align: Deep Dynamic Time Warping for Retinal OCT Segmentation -- Learnable Oriented-Derivative Network for Polyp Segmentation -- LambdaUNet: 2.5D Stroke Lesion Segmentation of Diffusion-weighted MR Images. Tipo de medio : Computadora Summary : The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in machine learning theory; machine learning - attention models; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging – others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 : 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part II / Tipo de documento: documento electrónico Autores: de Bruijne, Marleen, ; Cattin, Philippe C., ; Cotin, Stéphane, ; Padoy, Nicolas, ; Speidel, Stefanie, ; Zheng, Yefeng, ; Essert, Caroline, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2021 Número de páginas: XXXVII, 662 p. 181 ilustraciones, 175 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-87196-3 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Visión por computador Inteligencia artificial Sistemas de reconocimiento de patrones Bioinformática Informática Médica Reconocimiento de patrones automatizado BiologÃa Computacional y de Sistemas Informática de la Salud Clasificación: 006.37 Resumen: El conjunto de ocho volúmenes LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907 y 12908 constituye las actas arbitradas de la 24.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2021, celebrada en Estrasburgo, Francia. en septiembre/octubre de 2021.* Los 531 artÃculos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1630 presentaciones en un proceso de revisión doble ciego. Los artÃculos están organizados en las siguientes secciones temáticas: Parte I: segmentación de imágenes Parte II: aprendizaje automático - aprendizaje autosupervisado; aprendizaje automático: aprendizaje semisupervisado; y aprendizaje automático: aprendizaje débilmente supervisado. Parte III: aprendizaje automático: avances en la teorÃa del aprendizaje automático; aprendizaje automático: modelos de atención; aprendizaje automático: adaptación de dominios; aprendizaje automático: aprendizaje federado; aprendizaje automático: interpretabilidad / explicabilidad; y aprendizaje automático - incertidumbre Parte IV: registro de imágenes; intervenciones y cirugÃa guiadas por imágenes; ciencia de datos quirúrgicos; planificación y simulación quirúrgica; análisis de habilidades quirúrgicas y flujo de trabajo; y visualización quirúrgica y realidad mixta, aumentada y virtual. Parte V: diagnóstico asistido por ordenador; integración de imágenes con biomarcadores sin imágenes; y predicción de resultados/enfermedades. Parte VI: reconstrucción de imágenes; aplicaciones clÃnicas - cardÃacas; y aplicaciones clÃnicas - vasculares Parte VII: aplicaciones clÃnicas - abdomen; aplicaciones clÃnicas - mama; aplicaciones clÃnicas - dermatologÃa; aplicaciones clÃnicas: imágenes fetales; aplicaciones clÃnicas - pulmón; aplicaciones clÃnicas - neuroimagen - desarrollo cerebral; aplicaciones clÃnicas - neuroimagen - DWI y tractografÃa; aplicaciones clÃnicas - neuroimagen - redes cerebrales funcionales; aplicaciones clÃnicas - neuroimagen - otras; y aplicaciones clÃnicas - oncologÃa Parte VIII: aplicaciones clÃnicas - oftalmologÃa; patologÃa computacional (integrativa); modalidades - microscopÃa; modalidades - histopatologÃa; y modalidades - ultrasonido *La conferencia se realizó de manera virtual. Nota de contenido: Machine Learning - Self-Supervised Learning -- SSLP: Spatial Guided Self-supervised Learning on Pathological Images -- Segmentation of Left Atrial MR Images via Self-supervised Semi-supervised Meta-learning -- Deformed2Self: Self-Supervised Denoising for Dynamic Medical Imaging -- Imbalance-Aware Self-Supervised Learning for 3D Radiomic Representations -- Self-supervised visual representation learning for histopathological images -- Contrastive Learning with Continuous Proxy Meta-Data For 3D MRI Classification -- Sli2Vol: Annotate a 3D Volume from a Single Slice with Self-Supervised Learning -- Self-Supervised Longitudinal Neighbourhood Embedding -- Self-Supervised Multi-Modal Alignment For Whole Body Medical Imaging -- SimTriplet: Simple Triplet Representation Learning with a Single GPU -- Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images -- SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation -- Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation -- SpineGEM: A Hybrid-Supervised Model Generation Strategy Enabling Accurate Spine Disease Classification with a Small Training Dataset -- Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Images -- Topological Learning and Its Application to Multimodal Brain Network Integration -- One-Shot Medical Landmark Detection -- Implicit field learning for unsupervised anomaly detection in medical images -- Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images -- Contrastive Pre-training and Representation Distillation for Medical Visual Question Answering Based on Radiology Images -- Positional Contrastive Learning for Volumetric Medical Image Segmentation -- Longitudinal self-supervision to disentangle inter-patient variability from disease progression -- Self-Supervised Vessel Enhancement Using Flow-Based Consistencies -- Unsupervised Contrastive Learning of Radiomics and Deep Features for Label-Efficient Tumor Classification -- Learning 4D Infant Cortical Surface Atlas with Unsupervised Spherical Networks -- Multimodal Representation Learning via Maximization of Local Mutual Information -- Inter-Regional High-level Relation Learning from Functional Connectivity via Self-Supervision -- Machine Learning - Semi-Supervised Learning -- Semi-supervised Left Atrium Segmentation with Mutual Consistency Training -- Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation -- Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency -- Few-Shot Domain Adaptation with Polymorphic Transformers -- Lesion Segmentation and RECIST Diameter Prediction via Click-driven Attention and Dual-path Connection -- Reciprocal Learning for Semi-supervised Segmentation -- Disentangled Sequential Graph Autoencoder for Preclinical Alzheimer's Disease Characterizations from ADNI Study -- POPCORN: Progressive Pseudo-labeling with Consistency Regularization and Neighboring -- 3D Semantic Mapping from Arthroscopy using Out-of-distribution Pose and Depth and In-distribution Segmentation Training -- Semi-Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation -- Implicit Neural Distance Representation for Unsupervised and Supervised Classification of Complex Anatomies -- 3D Graph-S2Net: Shape-Aware Self-Ensembling Network for Semi-Supervised Segmentation with Bilateral Graph Convolution -- Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation -- Neighbor Matching for Semi-supervised Learning -- Tripled-uncertainty Guided Mean Teacher model for Semi-supervised Medical Image Segmentation -- Learning with Noise: Mask-guided Attention Model for Weakly Supervised Nuclei Segmentation -- Order-Guided Disentangled Representation Learning for Ulcerative Colitis Classification with Limited Labels -- Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation -- Functional Magnetic Resonance Imaging data augmentation through conditional ICA -- Scalable joint detection and segmentation of surgical instruments with weak supervision -- Machine Learning - Weakly Supervised Learning -- Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss -- Bounding Box Tightness Prior for Weakly Supervised Image Segmentation -- OXnet: Deep Omni-supervised Thoracic Disease Detection from Chest X-rays -- Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation -- Quality-Aware Memory Network for Interactive Volumetric Image Segmentation -- Improving Pneumonia Localization via Cross-Attention on Medical Images and Reports -- Combining Attention-based Multiple Instance Learning and Gaussian Processes for CT Hemorrhage Detection -- CPNet: Cycle Prototype Network for Weakly-supervised 3D Renal Chamber Segmentation -- Observational Supervision for Medical Image Classification using Gaze Data -- Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation -- Efficient and Generic Interactive Segmentation Framework to Correct Mispredictions during Clinical Evaluation of Medical Images -- Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs -- Labels-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation. Tipo de medio : Computadora Summary : The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in machine learning theory; machine learning - attention models; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging – others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 : 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part II / [documento electrónico] / de Bruijne, Marleen, ; Cattin, Philippe C., ; Cotin, Stéphane, ; Padoy, Nicolas, ; Speidel, Stefanie, ; Zheng, Yefeng, ; Essert, Caroline, . - 1 ed. . - [s.l.] : Springer, 2021 . - XXXVII, 662 p. 181 ilustraciones, 175 ilustraciones en color.
ISBN : 978-3-030-87196-3
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Visión por computador Inteligencia artificial Sistemas de reconocimiento de patrones Bioinformática Informática Médica Reconocimiento de patrones automatizado BiologÃa Computacional y de Sistemas Informática de la Salud Clasificación: 006.37 Resumen: El conjunto de ocho volúmenes LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907 y 12908 constituye las actas arbitradas de la 24.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2021, celebrada en Estrasburgo, Francia. en septiembre/octubre de 2021.* Los 531 artÃculos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1630 presentaciones en un proceso de revisión doble ciego. Los artÃculos están organizados en las siguientes secciones temáticas: Parte I: segmentación de imágenes Parte II: aprendizaje automático - aprendizaje autosupervisado; aprendizaje automático: aprendizaje semisupervisado; y aprendizaje automático: aprendizaje débilmente supervisado. Parte III: aprendizaje automático: avances en la teorÃa del aprendizaje automático; aprendizaje automático: modelos de atención; aprendizaje automático: adaptación de dominios; aprendizaje automático: aprendizaje federado; aprendizaje automático: interpretabilidad / explicabilidad; y aprendizaje automático - incertidumbre Parte IV: registro de imágenes; intervenciones y cirugÃa guiadas por imágenes; ciencia de datos quirúrgicos; planificación y simulación quirúrgica; análisis de habilidades quirúrgicas y flujo de trabajo; y visualización quirúrgica y realidad mixta, aumentada y virtual. Parte V: diagnóstico asistido por ordenador; integración de imágenes con biomarcadores sin imágenes; y predicción de resultados/enfermedades. Parte VI: reconstrucción de imágenes; aplicaciones clÃnicas - cardÃacas; y aplicaciones clÃnicas - vasculares Parte VII: aplicaciones clÃnicas - abdomen; aplicaciones clÃnicas - mama; aplicaciones clÃnicas - dermatologÃa; aplicaciones clÃnicas: imágenes fetales; aplicaciones clÃnicas - pulmón; aplicaciones clÃnicas - neuroimagen - desarrollo cerebral; aplicaciones clÃnicas - neuroimagen - DWI y tractografÃa; aplicaciones clÃnicas - neuroimagen - redes cerebrales funcionales; aplicaciones clÃnicas - neuroimagen - otras; y aplicaciones clÃnicas - oncologÃa Parte VIII: aplicaciones clÃnicas - oftalmologÃa; patologÃa computacional (integrativa); modalidades - microscopÃa; modalidades - histopatologÃa; y modalidades - ultrasonido *La conferencia se realizó de manera virtual. Nota de contenido: Machine Learning - Self-Supervised Learning -- SSLP: Spatial Guided Self-supervised Learning on Pathological Images -- Segmentation of Left Atrial MR Images via Self-supervised Semi-supervised Meta-learning -- Deformed2Self: Self-Supervised Denoising for Dynamic Medical Imaging -- Imbalance-Aware Self-Supervised Learning for 3D Radiomic Representations -- Self-supervised visual representation learning for histopathological images -- Contrastive Learning with Continuous Proxy Meta-Data For 3D MRI Classification -- Sli2Vol: Annotate a 3D Volume from a Single Slice with Self-Supervised Learning -- Self-Supervised Longitudinal Neighbourhood Embedding -- Self-Supervised Multi-Modal Alignment For Whole Body Medical Imaging -- SimTriplet: Simple Triplet Representation Learning with a Single GPU -- Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images -- SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation -- Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation -- SpineGEM: A Hybrid-Supervised Model Generation Strategy Enabling Accurate Spine Disease Classification with a Small Training Dataset -- Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Images -- Topological Learning and Its Application to Multimodal Brain Network Integration -- One-Shot Medical Landmark Detection -- Implicit field learning for unsupervised anomaly detection in medical images -- Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images -- Contrastive Pre-training and Representation Distillation for Medical Visual Question Answering Based on Radiology Images -- Positional Contrastive Learning for Volumetric Medical Image Segmentation -- Longitudinal self-supervision to disentangle inter-patient variability from disease progression -- Self-Supervised Vessel Enhancement Using Flow-Based Consistencies -- Unsupervised Contrastive Learning of Radiomics and Deep Features for Label-Efficient Tumor Classification -- Learning 4D Infant Cortical Surface Atlas with Unsupervised Spherical Networks -- Multimodal Representation Learning via Maximization of Local Mutual Information -- Inter-Regional High-level Relation Learning from Functional Connectivity via Self-Supervision -- Machine Learning - Semi-Supervised Learning -- Semi-supervised Left Atrium Segmentation with Mutual Consistency Training -- Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation -- Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency -- Few-Shot Domain Adaptation with Polymorphic Transformers -- Lesion Segmentation and RECIST Diameter Prediction via Click-driven Attention and Dual-path Connection -- Reciprocal Learning for Semi-supervised Segmentation -- Disentangled Sequential Graph Autoencoder for Preclinical Alzheimer's Disease Characterizations from ADNI Study -- POPCORN: Progressive Pseudo-labeling with Consistency Regularization and Neighboring -- 3D Semantic Mapping from Arthroscopy using Out-of-distribution Pose and Depth and In-distribution Segmentation Training -- Semi-Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation -- Implicit Neural Distance Representation for Unsupervised and Supervised Classification of Complex Anatomies -- 3D Graph-S2Net: Shape-Aware Self-Ensembling Network for Semi-Supervised Segmentation with Bilateral Graph Convolution -- Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation -- Neighbor Matching for Semi-supervised Learning -- Tripled-uncertainty Guided Mean Teacher model for Semi-supervised Medical Image Segmentation -- Learning with Noise: Mask-guided Attention Model for Weakly Supervised Nuclei Segmentation -- Order-Guided Disentangled Representation Learning for Ulcerative Colitis Classification with Limited Labels -- Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation -- Functional Magnetic Resonance Imaging data augmentation through conditional ICA -- Scalable joint detection and segmentation of surgical instruments with weak supervision -- Machine Learning - Weakly Supervised Learning -- Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss -- Bounding Box Tightness Prior for Weakly Supervised Image Segmentation -- OXnet: Deep Omni-supervised Thoracic Disease Detection from Chest X-rays -- Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation -- Quality-Aware Memory Network for Interactive Volumetric Image Segmentation -- Improving Pneumonia Localization via Cross-Attention on Medical Images and Reports -- Combining Attention-based Multiple Instance Learning and Gaussian Processes for CT Hemorrhage Detection -- CPNet: Cycle Prototype Network for Weakly-supervised 3D Renal Chamber Segmentation -- Observational Supervision for Medical Image Classification using Gaze Data -- Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation -- Efficient and Generic Interactive Segmentation Framework to Correct Mispredictions during Clinical Evaluation of Medical Images -- Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs -- Labels-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation. Tipo de medio : Computadora Summary : The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in machine learning theory; machine learning - attention models; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging – others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 : 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part III / Tipo de documento: documento electrónico Autores: de Bruijne, Marleen, ; Cattin, Philippe C., ; Cotin, Stéphane, ; Padoy, Nicolas, ; Speidel, Stefanie, ; Zheng, Yefeng, ; Essert, Caroline, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2021 Número de páginas: XXXVI, 648 p. 200 ilustraciones, 185 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-87199-4 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Visión por computador Inteligencia artificial Sistemas de reconocimiento de patrones Bioinformática Informática Médica Reconocimiento de patrones automatizado BiologÃa Computacional y de Sistemas Informática de la Salud Clasificación: 006.37 Resumen: El conjunto de ocho volúmenes LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907 y 12908 constituye las actas arbitradas de la 24.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2021, celebrada en Estrasburgo, Francia. en septiembre/octubre de 2021.* Los 531 artÃculos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1630 presentaciones en un proceso de revisión doble ciego. Los artÃculos están organizados en las siguientes secciones temáticas: Parte I: segmentación de imágenes Parte II: aprendizaje automático - aprendizaje autosupervisado; aprendizaje automático: aprendizaje semisupervisado; y aprendizaje automático: aprendizaje débilmente supervisado. Parte III: aprendizaje automático: avances en la teorÃa del aprendizaje automático; aprendizaje automático: modelos de atención; aprendizaje automático: adaptación de dominios; aprendizaje automático: aprendizaje federado; aprendizaje automático: interpretabilidad / explicabilidad; y aprendizaje automático - incertidumbre Parte IV: registro de imágenes; intervenciones y cirugÃa guiadas por imágenes; ciencia de datos quirúrgicos; planificación y simulación quirúrgica; análisis de habilidades quirúrgicas y flujo de trabajo; y visualización quirúrgica y realidad mixta, aumentada y virtual. Parte V: diagnóstico asistido por ordenador; integración de imágenes con biomarcadores sin imágenes; y predicción de resultados/enfermedades. Parte VI: reconstrucción de imágenes; aplicaciones clÃnicas - cardÃacas; y aplicaciones clÃnicas - vasculares Parte VII: aplicaciones clÃnicas - abdomen; aplicaciones clÃnicas - mama; aplicaciones clÃnicas - dermatologÃa; aplicaciones clÃnicas: imágenes fetales; aplicaciones clÃnicas - pulmón; aplicaciones clÃnicas - neuroimagen - desarrollo cerebral; aplicaciones clÃnicas - neuroimagen - DWI y tractografÃa; aplicaciones clÃnicas - neuroimagen - redes cerebrales funcionales; aplicaciones clÃnicas - neuroimagen - otras; y aplicaciones clÃnicas - oncologÃa Parte VIII: aplicaciones clÃnicas - oftalmologÃa; patologÃa computacional (integrativa); modalidades - microscopÃa; modalidades - histopatologÃa; y modalidades - ultrasonido *La conferencia se realizó de manera virtual. Nota de contenido: Machine Learning - Advances in Machine Learning Theory -- Towards Robust General Medical Image Segmentation -- Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation -- Targeted Gradient Descent: A Novel Method for Convolutional Neural Networks Fine-tuning and Online-learning -- A Hierarchical Feature Constraint to CamouflageMedical Adversarial Attacks -- Group Shift Pointwise Convolution for Volumetric Medical Image Segmentation -- Machine Learning - Attention models -- UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation -- AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generation -- Continuous-Time Deep Glioma Growth Models -- Spine-Transformers: Vertebra Detection and Localization in Arbitrary Field-of-View Spine CT with Transformers -- Multi-view analysis of unregistered medical images using cross-view transformers -- Machine Learning - Domain Adaptation -- Stain Mix-up: Unsupervised Domain Generalization for Histopathology Images -- A Unified Hyper-GAN Model for Unpaired Multi-contrast MR Image Translation -- Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis -- Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation -- Controllable cardiac synthesis via disentangled anatomy arithmetic -- CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation -- Harmonization with Flow-based Causal Inference -- Uncertainty-Aware Label Rectification for Domain Adaptive Mitochondria Segmentation -- Semantic Consistent Unsupervised Domain Adaptation for Cross-modality Medical Image Segmentation -- Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentation -- FoldIt: Haustral Folds Detection and Segmentation in Colonoscopy Videos -- Reference-Relation Guided Autoencoder with Deep CCA Restriction for Awake-to-Sleep Brain Functional Connectome Prediction -- Domain Composition and Attention for Unseen-Domain Generalizable Medical Image Segmentation -- Fully Test-time Adaptation for Image Segmentation -- OLVA: Optimal Latent Vector Alignment for Unsupervised Domain Adaptation in Medical Image Segmentation -- Prototypical Interaction Graph for Unsupervised Domain Adaptation in Surgical Instrument Segmentation -- Unsupervised Domain Adaptation for Small Bowel Segmentation using Disentangled Representation -- Data-driven mapping between functional connectomes using optimal transport -- EndoUDA: A modality independent segmentation approach for endoscopy imaging -- Style Transfer Using Generative Adversarial Networks for Multi-Site MRI Harmonization -- Machine Learning - Federated Learning -- Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching -- FedPerl: Semi-Supervised Peer Learning for Skin Lesion Classification -- Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning -- Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures -- Federated Contrastive Learning for Volumetric Medical Image Segmentation -- Federated Contrastive Learning for Decentralized Unlabeled Medical Images -- Machine Learning - Interpretability / Explainability -- Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features -- Demystifying T1-MRI to FDG18-PET Image Translation via Representational Similarity -- Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation -- An Interpretable Approach to Automated Severity Scoring in Pelvic Trauma -- Scalable, Axiomatic Explanations of Deep Alzheimer's Diagnosis from Heterogeneous Data -- SPARTA: An Integrated Stability, Discriminability, and Sparsity based Radiomic Feature Selection Approach -- The Power of Proxy Data and Proxy Networks for Hyper-Parameter Optimization for Medical Image Segmentation -- Fighting Class Imbalance with ContrastiveLearning -- Interpretable gender classification from retinal fundus images using BagNets -- Explainable Classification of Weakly Annotated Wireless Capsule Endoscopy Images based on a Fuzzy Bag-of-Colour Features Model and Brain Storm Optimization -- Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Models -- A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging -- Using Causal Analysis for Conceptual Deep Learning Explanation -- A spherical convolutional neural network for white matter structure imaging via diffusion MRI -- Sharpening Local Interpretable Model-agnostic Explanations for Histopathology: Improved Understandability and Reliability -- Improving the Explainability of Skin Cancer Diagnosis Using CBIR -- PAC Bayesian Performance Guarantees for (Stochastic) Deep Networks in Medical Imaging -- Machine Learning - Uncertainty -- Medical Matting: A New Perspective on Medical Segmentation with Uncertainty -- Confidence-aware Cascaded Network for Fetal Brain Segmentation on MR Images -- Orthogonal Ensemble Networks for Biomedical Image Segmentation -- Learning to Predict Error for MRI Reconstruction -- Uncertainty-Guided Progressive GANs for Medical Image Translation -- Variational Topic Inference for Chest X-Ray Report Generation -- Uncertainty Aware Deep Reinforcement Learning for Anatomical Landmark Detection in Medical Images. Tipo de medio : Computadora Summary : The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in machine learning theory; machine learning - attention models; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging – others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 : 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part III / [documento electrónico] / de Bruijne, Marleen, ; Cattin, Philippe C., ; Cotin, Stéphane, ; Padoy, Nicolas, ; Speidel, Stefanie, ; Zheng, Yefeng, ; Essert, Caroline, . - 1 ed. . - [s.l.] : Springer, 2021 . - XXXVI, 648 p. 200 ilustraciones, 185 ilustraciones en color.
ISBN : 978-3-030-87199-4
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Visión por computador Inteligencia artificial Sistemas de reconocimiento de patrones Bioinformática Informática Médica Reconocimiento de patrones automatizado BiologÃa Computacional y de Sistemas Informática de la Salud Clasificación: 006.37 Resumen: El conjunto de ocho volúmenes LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907 y 12908 constituye las actas arbitradas de la 24.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2021, celebrada en Estrasburgo, Francia. en septiembre/octubre de 2021.* Los 531 artÃculos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1630 presentaciones en un proceso de revisión doble ciego. Los artÃculos están organizados en las siguientes secciones temáticas: Parte I: segmentación de imágenes Parte II: aprendizaje automático - aprendizaje autosupervisado; aprendizaje automático: aprendizaje semisupervisado; y aprendizaje automático: aprendizaje débilmente supervisado. Parte III: aprendizaje automático: avances en la teorÃa del aprendizaje automático; aprendizaje automático: modelos de atención; aprendizaje automático: adaptación de dominios; aprendizaje automático: aprendizaje federado; aprendizaje automático: interpretabilidad / explicabilidad; y aprendizaje automático - incertidumbre Parte IV: registro de imágenes; intervenciones y cirugÃa guiadas por imágenes; ciencia de datos quirúrgicos; planificación y simulación quirúrgica; análisis de habilidades quirúrgicas y flujo de trabajo; y visualización quirúrgica y realidad mixta, aumentada y virtual. Parte V: diagnóstico asistido por ordenador; integración de imágenes con biomarcadores sin imágenes; y predicción de resultados/enfermedades. Parte VI: reconstrucción de imágenes; aplicaciones clÃnicas - cardÃacas; y aplicaciones clÃnicas - vasculares Parte VII: aplicaciones clÃnicas - abdomen; aplicaciones clÃnicas - mama; aplicaciones clÃnicas - dermatologÃa; aplicaciones clÃnicas: imágenes fetales; aplicaciones clÃnicas - pulmón; aplicaciones clÃnicas - neuroimagen - desarrollo cerebral; aplicaciones clÃnicas - neuroimagen - DWI y tractografÃa; aplicaciones clÃnicas - neuroimagen - redes cerebrales funcionales; aplicaciones clÃnicas - neuroimagen - otras; y aplicaciones clÃnicas - oncologÃa Parte VIII: aplicaciones clÃnicas - oftalmologÃa; patologÃa computacional (integrativa); modalidades - microscopÃa; modalidades - histopatologÃa; y modalidades - ultrasonido *La conferencia se realizó de manera virtual. Nota de contenido: Machine Learning - Advances in Machine Learning Theory -- Towards Robust General Medical Image Segmentation -- Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation -- Targeted Gradient Descent: A Novel Method for Convolutional Neural Networks Fine-tuning and Online-learning -- A Hierarchical Feature Constraint to CamouflageMedical Adversarial Attacks -- Group Shift Pointwise Convolution for Volumetric Medical Image Segmentation -- Machine Learning - Attention models -- UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation -- AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generation -- Continuous-Time Deep Glioma Growth Models -- Spine-Transformers: Vertebra Detection and Localization in Arbitrary Field-of-View Spine CT with Transformers -- Multi-view analysis of unregistered medical images using cross-view transformers -- Machine Learning - Domain Adaptation -- Stain Mix-up: Unsupervised Domain Generalization for Histopathology Images -- A Unified Hyper-GAN Model for Unpaired Multi-contrast MR Image Translation -- Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis -- Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation -- Controllable cardiac synthesis via disentangled anatomy arithmetic -- CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation -- Harmonization with Flow-based Causal Inference -- Uncertainty-Aware Label Rectification for Domain Adaptive Mitochondria Segmentation -- Semantic Consistent Unsupervised Domain Adaptation for Cross-modality Medical Image Segmentation -- Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentation -- FoldIt: Haustral Folds Detection and Segmentation in Colonoscopy Videos -- Reference-Relation Guided Autoencoder with Deep CCA Restriction for Awake-to-Sleep Brain Functional Connectome Prediction -- Domain Composition and Attention for Unseen-Domain Generalizable Medical Image Segmentation -- Fully Test-time Adaptation for Image Segmentation -- OLVA: Optimal Latent Vector Alignment for Unsupervised Domain Adaptation in Medical Image Segmentation -- Prototypical Interaction Graph for Unsupervised Domain Adaptation in Surgical Instrument Segmentation -- Unsupervised Domain Adaptation for Small Bowel Segmentation using Disentangled Representation -- Data-driven mapping between functional connectomes using optimal transport -- EndoUDA: A modality independent segmentation approach for endoscopy imaging -- Style Transfer Using Generative Adversarial Networks for Multi-Site MRI Harmonization -- Machine Learning - Federated Learning -- Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching -- FedPerl: Semi-Supervised Peer Learning for Skin Lesion Classification -- Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning -- Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures -- Federated Contrastive Learning for Volumetric Medical Image Segmentation -- Federated Contrastive Learning for Decentralized Unlabeled Medical Images -- Machine Learning - Interpretability / Explainability -- Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features -- Demystifying T1-MRI to FDG18-PET Image Translation via Representational Similarity -- Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation -- An Interpretable Approach to Automated Severity Scoring in Pelvic Trauma -- Scalable, Axiomatic Explanations of Deep Alzheimer's Diagnosis from Heterogeneous Data -- SPARTA: An Integrated Stability, Discriminability, and Sparsity based Radiomic Feature Selection Approach -- The Power of Proxy Data and Proxy Networks for Hyper-Parameter Optimization for Medical Image Segmentation -- Fighting Class Imbalance with ContrastiveLearning -- Interpretable gender classification from retinal fundus images using BagNets -- Explainable Classification of Weakly Annotated Wireless Capsule Endoscopy Images based on a Fuzzy Bag-of-Colour Features Model and Brain Storm Optimization -- Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Models -- A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging -- Using Causal Analysis for Conceptual Deep Learning Explanation -- A spherical convolutional neural network for white matter structure imaging via diffusion MRI -- Sharpening Local Interpretable Model-agnostic Explanations for Histopathology: Improved Understandability and Reliability -- Improving the Explainability of Skin Cancer Diagnosis Using CBIR -- PAC Bayesian Performance Guarantees for (Stochastic) Deep Networks in Medical Imaging -- Machine Learning - Uncertainty -- Medical Matting: A New Perspective on Medical Segmentation with Uncertainty -- Confidence-aware Cascaded Network for Fetal Brain Segmentation on MR Images -- Orthogonal Ensemble Networks for Biomedical Image Segmentation -- Learning to Predict Error for MRI Reconstruction -- Uncertainty-Guided Progressive GANs for Medical Image Translation -- Variational Topic Inference for Chest X-Ray Report Generation -- Uncertainty Aware Deep Reinforcement Learning for Anatomical Landmark Detection in Medical Images. Tipo de medio : Computadora Summary : The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in machine learning theory; machine learning - attention models; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging – others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 : 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part IV / Tipo de documento: documento electrónico Autores: de Bruijne, Marleen, ; Cattin, Philippe C., ; Cotin, Stéphane, ; Padoy, Nicolas, ; Speidel, Stefanie, ; Zheng, Yefeng, ; Essert, Caroline, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2021 Número de páginas: XXXVII, 682 p. 8 ilustraciones ISBN/ISSN/DL: 978-3-030-87202-1 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Visión por computador Inteligencia artificial Sistemas de reconocimiento de patrones Bioinformática Informática Médica Reconocimiento de patrones automatizado BiologÃa Computacional y de Sistemas Informática de la Salud Clasificación: 006.37 Resumen: El conjunto de ocho volúmenes LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907 y 12908 constituye las actas arbitradas de la 24.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2021, celebrada en Estrasburgo, Francia. en septiembre/octubre de 2021.* Los 531 artÃculos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1630 presentaciones en un proceso de revisión doble ciego. Los artÃculos están organizados en las siguientes secciones temáticas: Parte I: segmentación de imágenes Parte II: aprendizaje automático - aprendizaje autosupervisado; aprendizaje automático: aprendizaje semisupervisado; y aprendizaje automático: aprendizaje débilmente supervisado. Parte III: aprendizaje automático: avances en la teorÃa del aprendizaje automático; aprendizaje automático: modelos de atención; aprendizaje automático: adaptación de dominios; aprendizaje automático: aprendizaje federado; aprendizaje automático: interpretabilidad / explicabilidad; y aprendizaje automático - incertidumbre Parte IV: registro de imágenes; intervenciones y cirugÃa guiadas por imágenes; ciencia de datos quirúrgicos; planificación y simulación quirúrgica; análisis de habilidades quirúrgicas y flujo de trabajo; y visualización quirúrgica y realidad mixta, aumentada y virtual. Parte V: diagnóstico asistido por ordenador; integración de imágenes con biomarcadores sin imágenes; y predicción de resultados/enfermedades. Parte VI: reconstrucción de imágenes; aplicaciones clÃnicas - cardÃacas; y aplicaciones clÃnicas - vasculares Parte VII: aplicaciones clÃnicas - abdomen; aplicaciones clÃnicas - mama; aplicaciones clÃnicas - dermatologÃa; aplicaciones clÃnicas: imágenes fetales; aplicaciones clÃnicas - pulmón; aplicaciones clÃnicas - neuroimagen - desarrollo cerebral; aplicaciones clÃnicas - neuroimagen - DWI y tractografÃa; aplicaciones clÃnicas - neuroimagen - redes cerebrales funcionales; aplicaciones clÃnicas - neuroimagen - otras; y aplicaciones clÃnicas - oncologÃa Parte VIII: aplicaciones clÃnicas - oftalmologÃa; patologÃa computacional (integrativa); modalidades - microscopÃa; modalidades - histopatologÃa; y modalidades - ultrasonido *La conferencia se realizó de manera virtual. Nota de contenido: Image Registration -- Medical Image Registration Based on Uncoupled Learning and Accumulative Enhancement -- Atlas-Based Segmentation of Intracochlear Anatomy in Metal Artifact Affected CT Images of the Ear with Co-trained Deep Neural Networks -- Learning Unsupervised Parameter-specific Affine Transformation for Medical Images Registration -- Conditional Deformable Image Registration with Convolutional Neural Network -- A Deep Discontinuity-Preserving Image Registration Network -- End-to-end Ultrasound Frame to Volume Registration -- Cross-modal Attention for MRI and Ultrasound Volume Registration -- Bayesian Atlas Building with Hierarchical Priors for Subject-specific Regularization -- SAME: Deformable Image Registration based on Self-supervised Anatomical Embeddings -- Weakly Supervised Registration of Prostate MRI and Histopathology Images -- 4D-CBCT Registration with a FBCT-derived Plug-and-Play Feasibility Regularizer -- Unsupervised Diffeomorphic Surface Registration and Non-Linear Modelling -- Learning Dual Transformer Network for Diffeomorphic Registration -- Construction of Longitudinally Consistent 4D Infant Cerebellum Atlases based on Deep Learning -- Nesterov Accelerated ADMM for Fast Diffeomorphic Image Registration -- Spectral Embedding Approximation and Descriptor Learning for Craniofacial Volumetric Image Correspondence -- A Deep Network for Joint Registration and Parcellation of Cortical Surfaces -- 4D-Foot: A fully automated pipeline of four-dimensional analysis of the foot bones using bi-plane X-ray video and CT -- Equivariant Filters for Efficient Tracking in 3D Imaging -- Revisiting iterative highly efficient optimisation schemes in medical image registration -- Multi-scale Neural ODEs for 3D Medical Image Registration -- Image-Guided Interventions and Surgery -- Self-Supervised Generative Adversarial Network for Depth Estimation in Laparoscopic Images -- Personalized Respiratory Motion Model Using Conditional Generative Networks for MR-Guided Radiotherapy -- Multimodal Sensing Guidewire for C-arm Navigation with Random UV Enhanced Optical Sensors using Spatio-temporal Networks -- Image-to-Graph Convolutional Network for Deformable Shape Reconstruction from a Single Projection Image -- Class-Incremental Domain Adaptation with Smoothing and Calibration for Surgical Report Generation -- Real-Time Rotated Convolutional Descriptor for Surgical Environments -- Surgical Instruction Generation with Transformers -- Adversarial Domain Feature Adaptation for Bronchoscopic Depth Estimation -- 2.5D Thermometry Maps for MRI-guided Tumor Ablation -- Detection of critical structures in laparoscopic cholecystectomy using label relaxation and self-supervision -- EMDQ-SLAM: Real-time High-resolution Reconstruction of Soft Tissue Surface from Stereo Laparoscopy Videos -- Efficient Global-Local Memory for Real-time Instrument Segmentation of Robotic Surgical Video -- C-arm positioning for spinal standard projections in different intra-operative settings -- Quantitative Assessments for Ultrasound Probe Calibration -- Intra-operative Update of Boundary Conditions for Patient-specific Surgical Simulation -- Deep Iterative 2D/3D Registration -- hSDB-instrument: Instrument Localization Database for Laparoscopic and Robotic Surgeries -- Co-Generation and Segmentation for Generalized Surgical Instrument Segmentation on Unlabelled Data -- Surgical Data Science -- E-DSSR: Efficient Dynamic Surgical Scene Reconstruction with Transformer-based Stereoscopic Depth Perception -- CataNet: Predicting remaining cataract surgery duration -- Task Fingerprinting for Meta Learning in Biomedical Image Analysis -- Acoustic-based Spatio-temporal Learning for Press-fit Evaluation of Femoral Stem Implants -- Surgical Planning and Simulation -- Deep Simulation of Facial Appearance Changes Following Craniomaxillofacial Bony Movements in Orthognathic Surgical Planning -- A self-supervised deep framework for reference bony shape estimation in orthognathic surgical planning.-DLLNet: An Attention-based Deep Learning Method for Dental Landmark Localization on High-Resolution 3D Digital Dental Models -- Personalized CT Organ Dose Estimation from Scout Images -- High-particle simulation of Monte-Carlo dose distribution with 3D ConvLSTMs -- Effective semantic segmentation in Cataract surgery: What matters most? -- Facial and cochlear nerves characterization using deep reinforcement learning for landmark detection -- Patient-specific virtual spine straightening and vertebra inpainting: An automatic framework for osteoplasty planning -- A new Approach to Orthopedic Surgery Planning using Deep Reinforcement Learning and Simulation -- Whole Heart Mesh Generation For Image-Based Computational Simulations By Learning Free-From Deformations -- Automatic Path Planning for Safe Guide Pin Insertion in PCL Reconstruction Surgery -- Improving hexahedral-FEM-based plasticity in surgery simulation -- Rapid treatment planning for low-dose-rate prostate brachytherapy with TP-GAN -- Surgical Skill and Work Flow Analysis -- Trans-SVNet: Accurate Phase Recognition from Surgical Videos via Hybrid Embedding Aggregation Transformer -- OperA: Attention-Regularized Transformers for Surgical Phase Recognition -- Surgical Workflow Anticipation using Instrument Interaction -- Multi-View Surgical Video Action Detection via Mixed Global View Attention -- Interhemispheric functional connectivity in the primary motor cortex distinguishes between training on a physical and a virtual surgical simulator -- Surgical Visualization and Mixed, Augmented and Virtual Reality -- Image-based Incision Detection for Topological Intraoperative 3D Model Update in Augmented Reality Assisted Laparoscopic Surgery -- Using Multiple Images and Contours for Deformable 3D-2D Registration of a Preoperative CT in Laparoscopic Liver Surgery -- SurgeonAssist-Net: Towards Context-Aware Head-Mounted Display-Based Augmented Reality for Surgical Guidance. Tipo de medio : Computadora Summary : The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in machine learning theory; machine learning - attention models; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging – others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 : 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part IV / [documento electrónico] / de Bruijne, Marleen, ; Cattin, Philippe C., ; Cotin, Stéphane, ; Padoy, Nicolas, ; Speidel, Stefanie, ; Zheng, Yefeng, ; Essert, Caroline, . - 1 ed. . - [s.l.] : Springer, 2021 . - XXXVII, 682 p. 8 ilustraciones.
ISBN : 978-3-030-87202-1
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Visión por computador Inteligencia artificial Sistemas de reconocimiento de patrones Bioinformática Informática Médica Reconocimiento de patrones automatizado BiologÃa Computacional y de Sistemas Informática de la Salud Clasificación: 006.37 Resumen: El conjunto de ocho volúmenes LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907 y 12908 constituye las actas arbitradas de la 24.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2021, celebrada en Estrasburgo, Francia. en septiembre/octubre de 2021.* Los 531 artÃculos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1630 presentaciones en un proceso de revisión doble ciego. Los artÃculos están organizados en las siguientes secciones temáticas: Parte I: segmentación de imágenes Parte II: aprendizaje automático - aprendizaje autosupervisado; aprendizaje automático: aprendizaje semisupervisado; y aprendizaje automático: aprendizaje débilmente supervisado. Parte III: aprendizaje automático: avances en la teorÃa del aprendizaje automático; aprendizaje automático: modelos de atención; aprendizaje automático: adaptación de dominios; aprendizaje automático: aprendizaje federado; aprendizaje automático: interpretabilidad / explicabilidad; y aprendizaje automático - incertidumbre Parte IV: registro de imágenes; intervenciones y cirugÃa guiadas por imágenes; ciencia de datos quirúrgicos; planificación y simulación quirúrgica; análisis de habilidades quirúrgicas y flujo de trabajo; y visualización quirúrgica y realidad mixta, aumentada y virtual. Parte V: diagnóstico asistido por ordenador; integración de imágenes con biomarcadores sin imágenes; y predicción de resultados/enfermedades. Parte VI: reconstrucción de imágenes; aplicaciones clÃnicas - cardÃacas; y aplicaciones clÃnicas - vasculares Parte VII: aplicaciones clÃnicas - abdomen; aplicaciones clÃnicas - mama; aplicaciones clÃnicas - dermatologÃa; aplicaciones clÃnicas: imágenes fetales; aplicaciones clÃnicas - pulmón; aplicaciones clÃnicas - neuroimagen - desarrollo cerebral; aplicaciones clÃnicas - neuroimagen - DWI y tractografÃa; aplicaciones clÃnicas - neuroimagen - redes cerebrales funcionales; aplicaciones clÃnicas - neuroimagen - otras; y aplicaciones clÃnicas - oncologÃa Parte VIII: aplicaciones clÃnicas - oftalmologÃa; patologÃa computacional (integrativa); modalidades - microscopÃa; modalidades - histopatologÃa; y modalidades - ultrasonido *La conferencia se realizó de manera virtual. Nota de contenido: Image Registration -- Medical Image Registration Based on Uncoupled Learning and Accumulative Enhancement -- Atlas-Based Segmentation of Intracochlear Anatomy in Metal Artifact Affected CT Images of the Ear with Co-trained Deep Neural Networks -- Learning Unsupervised Parameter-specific Affine Transformation for Medical Images Registration -- Conditional Deformable Image Registration with Convolutional Neural Network -- A Deep Discontinuity-Preserving Image Registration Network -- End-to-end Ultrasound Frame to Volume Registration -- Cross-modal Attention for MRI and Ultrasound Volume Registration -- Bayesian Atlas Building with Hierarchical Priors for Subject-specific Regularization -- SAME: Deformable Image Registration based on Self-supervised Anatomical Embeddings -- Weakly Supervised Registration of Prostate MRI and Histopathology Images -- 4D-CBCT Registration with a FBCT-derived Plug-and-Play Feasibility Regularizer -- Unsupervised Diffeomorphic Surface Registration and Non-Linear Modelling -- Learning Dual Transformer Network for Diffeomorphic Registration -- Construction of Longitudinally Consistent 4D Infant Cerebellum Atlases based on Deep Learning -- Nesterov Accelerated ADMM for Fast Diffeomorphic Image Registration -- Spectral Embedding Approximation and Descriptor Learning for Craniofacial Volumetric Image Correspondence -- A Deep Network for Joint Registration and Parcellation of Cortical Surfaces -- 4D-Foot: A fully automated pipeline of four-dimensional analysis of the foot bones using bi-plane X-ray video and CT -- Equivariant Filters for Efficient Tracking in 3D Imaging -- Revisiting iterative highly efficient optimisation schemes in medical image registration -- Multi-scale Neural ODEs for 3D Medical Image Registration -- Image-Guided Interventions and Surgery -- Self-Supervised Generative Adversarial Network for Depth Estimation in Laparoscopic Images -- Personalized Respiratory Motion Model Using Conditional Generative Networks for MR-Guided Radiotherapy -- Multimodal Sensing Guidewire for C-arm Navigation with Random UV Enhanced Optical Sensors using Spatio-temporal Networks -- Image-to-Graph Convolutional Network for Deformable Shape Reconstruction from a Single Projection Image -- Class-Incremental Domain Adaptation with Smoothing and Calibration for Surgical Report Generation -- Real-Time Rotated Convolutional Descriptor for Surgical Environments -- Surgical Instruction Generation with Transformers -- Adversarial Domain Feature Adaptation for Bronchoscopic Depth Estimation -- 2.5D Thermometry Maps for MRI-guided Tumor Ablation -- Detection of critical structures in laparoscopic cholecystectomy using label relaxation and self-supervision -- EMDQ-SLAM: Real-time High-resolution Reconstruction of Soft Tissue Surface from Stereo Laparoscopy Videos -- Efficient Global-Local Memory for Real-time Instrument Segmentation of Robotic Surgical Video -- C-arm positioning for spinal standard projections in different intra-operative settings -- Quantitative Assessments for Ultrasound Probe Calibration -- Intra-operative Update of Boundary Conditions for Patient-specific Surgical Simulation -- Deep Iterative 2D/3D Registration -- hSDB-instrument: Instrument Localization Database for Laparoscopic and Robotic Surgeries -- Co-Generation and Segmentation for Generalized Surgical Instrument Segmentation on Unlabelled Data -- Surgical Data Science -- E-DSSR: Efficient Dynamic Surgical Scene Reconstruction with Transformer-based Stereoscopic Depth Perception -- CataNet: Predicting remaining cataract surgery duration -- Task Fingerprinting for Meta Learning in Biomedical Image Analysis -- Acoustic-based Spatio-temporal Learning for Press-fit Evaluation of Femoral Stem Implants -- Surgical Planning and Simulation -- Deep Simulation of Facial Appearance Changes Following Craniomaxillofacial Bony Movements in Orthognathic Surgical Planning -- A self-supervised deep framework for reference bony shape estimation in orthognathic surgical planning.-DLLNet: An Attention-based Deep Learning Method for Dental Landmark Localization on High-Resolution 3D Digital Dental Models -- Personalized CT Organ Dose Estimation from Scout Images -- High-particle simulation of Monte-Carlo dose distribution with 3D ConvLSTMs -- Effective semantic segmentation in Cataract surgery: What matters most? -- Facial and cochlear nerves characterization using deep reinforcement learning for landmark detection -- Patient-specific virtual spine straightening and vertebra inpainting: An automatic framework for osteoplasty planning -- A new Approach to Orthopedic Surgery Planning using Deep Reinforcement Learning and Simulation -- Whole Heart Mesh Generation For Image-Based Computational Simulations By Learning Free-From Deformations -- Automatic Path Planning for Safe Guide Pin Insertion in PCL Reconstruction Surgery -- Improving hexahedral-FEM-based plasticity in surgery simulation -- Rapid treatment planning for low-dose-rate prostate brachytherapy with TP-GAN -- Surgical Skill and Work Flow Analysis -- Trans-SVNet: Accurate Phase Recognition from Surgical Videos via Hybrid Embedding Aggregation Transformer -- OperA: Attention-Regularized Transformers for Surgical Phase Recognition -- Surgical Workflow Anticipation using Instrument Interaction -- Multi-View Surgical Video Action Detection via Mixed Global View Attention -- Interhemispheric functional connectivity in the primary motor cortex distinguishes between training on a physical and a virtual surgical simulator -- Surgical Visualization and Mixed, Augmented and Virtual Reality -- Image-based Incision Detection for Topological Intraoperative 3D Model Update in Augmented Reality Assisted Laparoscopic Surgery -- Using Multiple Images and Contours for Deformable 3D-2D Registration of a Preoperative CT in Laparoscopic Liver Surgery -- SurgeonAssist-Net: Towards Context-Aware Head-Mounted Display-Based Augmented Reality for Surgical Guidance. Tipo de medio : Computadora Summary : The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in machine learning theory; machine learning - attention models; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging – others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 : 24th International Conference, Strasbourg, France, September 27 – October 1, 2021, Proceedings, Part V / Tipo de documento: documento electrónico Autores: de Bruijne, Marleen, ; Cattin, Philippe C., ; Cotin, Stéphane, ; Padoy, Nicolas, ; Speidel, Stefanie, ; Zheng, Yefeng, ; Essert, Caroline, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2021 Número de páginas: XXXVIII, 839 p. 25 ilustraciones ISBN/ISSN/DL: 978-3-030-87240-3 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Visión por computador Inteligencia artificial Bioinformática Sistemas de reconocimiento de patrones Informática Médica BiologÃa Computacional y de Sistemas Reconocimiento de patrones automatizado Informática de la Salud Clasificación: 006.37 Resumen: El conjunto de ocho volúmenes LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907 y 12908 constituye las actas arbitradas de la 24.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2021, celebrada en Estrasburgo, Francia. en septiembre/octubre de 2021.* Los 531 artÃculos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1630 presentaciones en un proceso de revisión doble ciego. Los artÃculos están organizados en las siguientes secciones temáticas: Parte I: segmentación de imágenes Parte II: aprendizaje automático - aprendizaje autosupervisado; aprendizaje automático: aprendizaje semisupervisado; y aprendizaje automático: aprendizaje débilmente supervisado. Parte III: aprendizaje automático: avances en la teorÃa del aprendizaje automático; aprendizaje automático: modelos de atención; aprendizaje automático: adaptación de dominios; aprendizaje automático: aprendizaje federado; aprendizaje automático: interpretabilidad / explicabilidad; y aprendizaje automático - incertidumbre Parte IV: registro de imágenes; intervenciones y cirugÃa guiadas por imágenes; ciencia de datos quirúrgicos; planificación y simulación quirúrgica; análisis de habilidades quirúrgicas y flujo de trabajo; y visualización quirúrgica y realidad mixta, aumentada y virtual. Parte V: diagnóstico asistido por ordenador; integración de imágenes con biomarcadores sin imágenes; y predicción de resultados/enfermedades. Parte VI: reconstrucción de imágenes; aplicaciones clÃnicas - cardÃacas; y aplicaciones clÃnicas - vasculares Parte VII: aplicaciones clÃnicas - abdomen; aplicaciones clÃnicas - mama; aplicaciones clÃnicas - dermatologÃa; aplicaciones clÃnicas: imágenes fetales; aplicaciones clÃnicas - pulmón; aplicaciones clÃnicas - neuroimagen - desarrollo cerebral; aplicaciones clÃnicas - neuroimagen - DWI y tractografÃa; aplicaciones clÃnicas - neuroimagen - redes cerebrales funcionales; aplicaciones clÃnicas - neuroimagen - otras; y aplicaciones clÃnicas - oncologÃa Parte VIII: aplicaciones clÃnicas - oftalmologÃa; patologÃa computacional (integrativa); modalidades - microscopÃa; modalidades - histopatologÃa; y modalidades - ultrasonido *La conferencia se realizó de manera virtual. Nota de contenido: Computer Aided Diagnosis -- DeepStationing: Thoracic Lymph Node Station Parsing in CT Scans using Anatomical Context Encoding and Key Organ Auto-Search -- Hepatocellular Carcinoma Segmentation from Digital Subtraction Angiography Videos using Learnable Temporal Difference -- CA-Net: Leveraging Contextual Features for Lung Cancer Prediction -- Semi-Supervised Learning for Bone Mineral Density Estimation in Hip X-ray Images -- DAE-GCN: Identifying Disease-Related Features for Disease Prediction -- Enhanced Breast Lesion Classification via Knowledge Guided Cross-Modal and Semantic Data Augmentation -- Multiple Meta-model Quantifying for Medical Visual Question Answering -- mfTrans-Net: Quantitative Measurement of Hepatocellular Carcinoma via Multi-Function Transformer Regression Network -- You Only Learn Once: Universal Anatomical Landmark Detection -- A Coherent Cooperative Learning Framework Based on Transfer Learning for Unsupervised Cross-domain Classification -- Towards a non-invasive diagnosis of portal hypertension based on an Eulerian CFD model with diffuse boundary conditions -- A Segmentation-Assisted Model for Universal Lesion Detection with Partial Labels -- Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images -- Conditional Training with Bounding Map for Universal Lesion Detection -- Focusing on Clinically Interpretable Features: Selective Attention Regularization for Liver Biopsy Image Classification -- Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image Classification -- Tensor-based Multi-index Representation Learning for Major Depression Disorder Detection with Resting-state fMRI -- Region Ensemble Network for MCI Conversion Prediction With a Relation Regularized Loss -- Airway Anomaly Detection by Graph Neural Network -- Energy-Based Supervised Hashing for Multimorbidity Image Retrieval -- Stochastic 4D Flow Vector-Field Signatures: A new approach for comprehensive 4D Flow MRI quantification -- Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling -- ASC-Net: Adversarial-based Selective Network for Unsupervised Anomaly Segmentation -- Cost-Sensitive Meta-Learning for Progress Prediction of Subjective Cognitive Decline with Brain Structural MRI -- Effective Pancreatic Cancer Screening on Non-contrast CT Scans via Anatomy-Aware Transformers -- Learning from Subjective Ratings Using Auto-Decoded Deep Latent Embeddings -- VertNet: Accurate Vertebra Localization and Identification Network from CT Images -- VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographs -- Multi-frame Collaboration for Effective Endoscopic Video Polyp Detection via Spatial-Temporal Feature Transformation -- MBFF-Net: Multi-Branch Feature Fusion Network for Carotid Plaque Segmentation in Ultrasound -- Balanced-MixUp for highly imbalanced medical image classification -- Transfer Learning of Deep Spatiotemporal Networks to Model Arbitrarily Long Videos of Seizures -- Retina-Match: Ipsilateral Mammography Lesion Matching in a Single Shot Detection Pipeline -- Towards Robust Dual-view Transformation via Densifying Sparse Supervision for Mammography Lesion Matching -- DeepOPG: Improving Orthopantomogram Finding Summarization with Weak Supervision -- Joint Spinal Centerline Extraction and Curvature Estimation with Row-wise Classification and Curve Graph Network -- LDPolypVideo Benchmark: A Large-scale Colonoscopy Video Dataset of Diverse Polyps -- Continual Learning with Bayesian Model based on a Fixed Pre-trained Feature Extractor -- Alleviating Data Imbalance Issue with Perturbed Input during Inference -- A Deep Reinforced Tree-traversal Agent for Coronary Artery Centerline Extraction -- Sequential Gaussian Process Regression for Simultaneous Pathology Detection and Shape Reconstruction -- Predicting Symptoms from Multiphasic MRI via Multi-Instance Attention Learning for Hepatocellular Carcinoma Grading -- Triplet-Branch Network with Prior-Knowledge Embedding for Fatigue Fracture Grading -- DeepMitral: Fully Automatic 3D Echocardiography Segmentation for Patient Specific Mitral Valve Modelling -- Data Augmentation in Logit Space for Medical Image Classification with Limited Training Data -- Collaborative Image Synthesis and Disease Diagnosis for Classification of Neurodegenerative Disorders with Incomplete Multi-modal Neuroimages -- Seg4Reg+: A Local and Global ConsistencyLearning between Spine Segmentation and CobbAngle Regression -- Meta-Modulation Network for Domain Generalization in Multi-site fMRI Classification -- 3D Brain Midline Delineation for Hematoma Patients -- Unsupervised Representation Learning Meets Pseudo-Label Supervised Self-Distillation: A New Approach to Rare Disease Classification -- nnDetection: A Self-configuring Method for Medical Object Detection -- Automating Embryo Development Stage Detection in Time-Lapse Imaging with Synergic Loss and Temporal Learning -- Deep Neural Dynamic Bayesian Networks applied to EEG sleep spindles modeling -- Few Trust Data Guided Annotation Refinement for Upper Gastrointestinal Anatomy Recognition -- Asymmetric 3D Context Fusion for Universal Lesion Detection -- Detecting Outliers with Poisson Image Interpolation -- MG-NET: Leveraging Pseudo-Imaging for Multi-Modal Metagenome Analysis -- Multimodal Multitask Deep Learning for X-Ray Image Retrieval -- Linear Prediction Residual for Efficient Diagnosis of Parkinson's Disease from Gait -- Primary Tumor and Inter-Organ Augmentations for Supervised Lymph Node Colon Adenocarcinoma Metastasis Detection -- Radiomics-informed Deep Curriculum Learning for Breast Cancer Diagnosis -- Integration of Imaging with Non-Imaging Biomarkers -- Lung Cancer Risk Estimation with Incomplete Data: A Joint Missing Imputation Perspective -- Co-Graph Attention Reasoning based Imaging and Clinical Features Integration for Lymph Node Metastasis Prediction -- Deep Orthogonal Fusion: Multimodal Prognostic Biomarker Discovery Integrating Radiology, Pathology, Genomic, and Clinical Data -- A Novel Bayesian Semi-parametric Model for Learning Heritable Imaging Traits -- Combining 3D Image and Tabular Data via the Dynamic Affine Feature Map Transform -- Image-derived phenotype extraction for genetic discovery via unsupervised deep learning in CMR images -- GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference -- Outcome/Disease Prediction -- Predicting Esophageal Fistula Risks Using a Multimodal Self-Attention Network -- Hybrid Aggregation Network for Survival Analysis from Whole Slide Histopathological Images -- Intracerebral Haemorrhage Growth Prediction Based on Displacement Vector Field and Clinical Metadata -- AMINN: Autoencoder-based Multiple Instance Neural Network Improves Outcome Prediction of Multifocal Liver Metastases -- Survival Prediction Based on Histopathology Imaging and Clinical Data: A Novel, Whole Slide CNN Approach -- Beyond Non-Maximum Suppression - Detecting Lesions in Digital Breast Tomosynthesis Volumes -- A Structural Causal Model MR Images of Multiple Sclerosis -- EMA: Auditing Data Removal from Trained Models -- AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-ray -- Projection-wise Disentangling for Fair and Interpretable Representation Learning: Application to 3D Facial Shape Analysis -- Attention-based Multi-scale Gated Recurrent Encoder with Novel Correlation Loss for COVID-19 Progression Prediction. Tipo de medio : Computadora Summary : The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in machine learning theory; machine learning - attention models; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging – others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 : 24th International Conference, Strasbourg, France, September 27 – October 1, 2021, Proceedings, Part V / [documento electrónico] / de Bruijne, Marleen, ; Cattin, Philippe C., ; Cotin, Stéphane, ; Padoy, Nicolas, ; Speidel, Stefanie, ; Zheng, Yefeng, ; Essert, Caroline, . - 1 ed. . - [s.l.] : Springer, 2021 . - XXXVIII, 839 p. 25 ilustraciones.
ISBN : 978-3-030-87240-3
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Idioma : Inglés (eng)
Palabras clave: Visión por computador Inteligencia artificial Bioinformática Sistemas de reconocimiento de patrones Informática Médica BiologÃa Computacional y de Sistemas Reconocimiento de patrones automatizado Informática de la Salud Clasificación: 006.37 Resumen: El conjunto de ocho volúmenes LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907 y 12908 constituye las actas arbitradas de la 24.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2021, celebrada en Estrasburgo, Francia. en septiembre/octubre de 2021.* Los 531 artÃculos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1630 presentaciones en un proceso de revisión doble ciego. Los artÃculos están organizados en las siguientes secciones temáticas: Parte I: segmentación de imágenes Parte II: aprendizaje automático - aprendizaje autosupervisado; aprendizaje automático: aprendizaje semisupervisado; y aprendizaje automático: aprendizaje débilmente supervisado. Parte III: aprendizaje automático: avances en la teorÃa del aprendizaje automático; aprendizaje automático: modelos de atención; aprendizaje automático: adaptación de dominios; aprendizaje automático: aprendizaje federado; aprendizaje automático: interpretabilidad / explicabilidad; y aprendizaje automático - incertidumbre Parte IV: registro de imágenes; intervenciones y cirugÃa guiadas por imágenes; ciencia de datos quirúrgicos; planificación y simulación quirúrgica; análisis de habilidades quirúrgicas y flujo de trabajo; y visualización quirúrgica y realidad mixta, aumentada y virtual. Parte V: diagnóstico asistido por ordenador; integración de imágenes con biomarcadores sin imágenes; y predicción de resultados/enfermedades. Parte VI: reconstrucción de imágenes; aplicaciones clÃnicas - cardÃacas; y aplicaciones clÃnicas - vasculares Parte VII: aplicaciones clÃnicas - abdomen; aplicaciones clÃnicas - mama; aplicaciones clÃnicas - dermatologÃa; aplicaciones clÃnicas: imágenes fetales; aplicaciones clÃnicas - pulmón; aplicaciones clÃnicas - neuroimagen - desarrollo cerebral; aplicaciones clÃnicas - neuroimagen - DWI y tractografÃa; aplicaciones clÃnicas - neuroimagen - redes cerebrales funcionales; aplicaciones clÃnicas - neuroimagen - otras; y aplicaciones clÃnicas - oncologÃa Parte VIII: aplicaciones clÃnicas - oftalmologÃa; patologÃa computacional (integrativa); modalidades - microscopÃa; modalidades - histopatologÃa; y modalidades - ultrasonido *La conferencia se realizó de manera virtual. Nota de contenido: Computer Aided Diagnosis -- DeepStationing: Thoracic Lymph Node Station Parsing in CT Scans using Anatomical Context Encoding and Key Organ Auto-Search -- Hepatocellular Carcinoma Segmentation from Digital Subtraction Angiography Videos using Learnable Temporal Difference -- CA-Net: Leveraging Contextual Features for Lung Cancer Prediction -- Semi-Supervised Learning for Bone Mineral Density Estimation in Hip X-ray Images -- DAE-GCN: Identifying Disease-Related Features for Disease Prediction -- Enhanced Breast Lesion Classification via Knowledge Guided Cross-Modal and Semantic Data Augmentation -- Multiple Meta-model Quantifying for Medical Visual Question Answering -- mfTrans-Net: Quantitative Measurement of Hepatocellular Carcinoma via Multi-Function Transformer Regression Network -- You Only Learn Once: Universal Anatomical Landmark Detection -- A Coherent Cooperative Learning Framework Based on Transfer Learning for Unsupervised Cross-domain Classification -- Towards a non-invasive diagnosis of portal hypertension based on an Eulerian CFD model with diffuse boundary conditions -- A Segmentation-Assisted Model for Universal Lesion Detection with Partial Labels -- Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images -- Conditional Training with Bounding Map for Universal Lesion Detection -- Focusing on Clinically Interpretable Features: Selective Attention Regularization for Liver Biopsy Image Classification -- Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image Classification -- Tensor-based Multi-index Representation Learning for Major Depression Disorder Detection with Resting-state fMRI -- Region Ensemble Network for MCI Conversion Prediction With a Relation Regularized Loss -- Airway Anomaly Detection by Graph Neural Network -- Energy-Based Supervised Hashing for Multimorbidity Image Retrieval -- Stochastic 4D Flow Vector-Field Signatures: A new approach for comprehensive 4D Flow MRI quantification -- Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling -- ASC-Net: Adversarial-based Selective Network for Unsupervised Anomaly Segmentation -- Cost-Sensitive Meta-Learning for Progress Prediction of Subjective Cognitive Decline with Brain Structural MRI -- Effective Pancreatic Cancer Screening on Non-contrast CT Scans via Anatomy-Aware Transformers -- Learning from Subjective Ratings Using Auto-Decoded Deep Latent Embeddings -- VertNet: Accurate Vertebra Localization and Identification Network from CT Images -- VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographs -- Multi-frame Collaboration for Effective Endoscopic Video Polyp Detection via Spatial-Temporal Feature Transformation -- MBFF-Net: Multi-Branch Feature Fusion Network for Carotid Plaque Segmentation in Ultrasound -- Balanced-MixUp for highly imbalanced medical image classification -- Transfer Learning of Deep Spatiotemporal Networks to Model Arbitrarily Long Videos of Seizures -- Retina-Match: Ipsilateral Mammography Lesion Matching in a Single Shot Detection Pipeline -- Towards Robust Dual-view Transformation via Densifying Sparse Supervision for Mammography Lesion Matching -- DeepOPG: Improving Orthopantomogram Finding Summarization with Weak Supervision -- Joint Spinal Centerline Extraction and Curvature Estimation with Row-wise Classification and Curve Graph Network -- LDPolypVideo Benchmark: A Large-scale Colonoscopy Video Dataset of Diverse Polyps -- Continual Learning with Bayesian Model based on a Fixed Pre-trained Feature Extractor -- Alleviating Data Imbalance Issue with Perturbed Input during Inference -- A Deep Reinforced Tree-traversal Agent for Coronary Artery Centerline Extraction -- Sequential Gaussian Process Regression for Simultaneous Pathology Detection and Shape Reconstruction -- Predicting Symptoms from Multiphasic MRI via Multi-Instance Attention Learning for Hepatocellular Carcinoma Grading -- Triplet-Branch Network with Prior-Knowledge Embedding for Fatigue Fracture Grading -- DeepMitral: Fully Automatic 3D Echocardiography Segmentation for Patient Specific Mitral Valve Modelling -- Data Augmentation in Logit Space for Medical Image Classification with Limited Training Data -- Collaborative Image Synthesis and Disease Diagnosis for Classification of Neurodegenerative Disorders with Incomplete Multi-modal Neuroimages -- Seg4Reg+: A Local and Global ConsistencyLearning between Spine Segmentation and CobbAngle Regression -- Meta-Modulation Network for Domain Generalization in Multi-site fMRI Classification -- 3D Brain Midline Delineation for Hematoma Patients -- Unsupervised Representation Learning Meets Pseudo-Label Supervised Self-Distillation: A New Approach to Rare Disease Classification -- nnDetection: A Self-configuring Method for Medical Object Detection -- Automating Embryo Development Stage Detection in Time-Lapse Imaging with Synergic Loss and Temporal Learning -- Deep Neural Dynamic Bayesian Networks applied to EEG sleep spindles modeling -- Few Trust Data Guided Annotation Refinement for Upper Gastrointestinal Anatomy Recognition -- Asymmetric 3D Context Fusion for Universal Lesion Detection -- Detecting Outliers with Poisson Image Interpolation -- MG-NET: Leveraging Pseudo-Imaging for Multi-Modal Metagenome Analysis -- Multimodal Multitask Deep Learning for X-Ray Image Retrieval -- Linear Prediction Residual for Efficient Diagnosis of Parkinson's Disease from Gait -- Primary Tumor and Inter-Organ Augmentations for Supervised Lymph Node Colon Adenocarcinoma Metastasis Detection -- Radiomics-informed Deep Curriculum Learning for Breast Cancer Diagnosis -- Integration of Imaging with Non-Imaging Biomarkers -- Lung Cancer Risk Estimation with Incomplete Data: A Joint Missing Imputation Perspective -- Co-Graph Attention Reasoning based Imaging and Clinical Features Integration for Lymph Node Metastasis Prediction -- Deep Orthogonal Fusion: Multimodal Prognostic Biomarker Discovery Integrating Radiology, Pathology, Genomic, and Clinical Data -- A Novel Bayesian Semi-parametric Model for Learning Heritable Imaging Traits -- Combining 3D Image and Tabular Data via the Dynamic Affine Feature Map Transform -- Image-derived phenotype extraction for genetic discovery via unsupervised deep learning in CMR images -- GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference -- Outcome/Disease Prediction -- Predicting Esophageal Fistula Risks Using a Multimodal Self-Attention Network -- Hybrid Aggregation Network for Survival Analysis from Whole Slide Histopathological Images -- Intracerebral Haemorrhage Growth Prediction Based on Displacement Vector Field and Clinical Metadata -- AMINN: Autoencoder-based Multiple Instance Neural Network Improves Outcome Prediction of Multifocal Liver Metastases -- Survival Prediction Based on Histopathology Imaging and Clinical Data: A Novel, Whole Slide CNN Approach -- Beyond Non-Maximum Suppression - Detecting Lesions in Digital Breast Tomosynthesis Volumes -- A Structural Causal Model MR Images of Multiple Sclerosis -- EMA: Auditing Data Removal from Trained Models -- AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-ray -- Projection-wise Disentangling for Fair and Interpretable Representation Learning: Application to 3D Facial Shape Analysis -- Attention-based Multi-scale Gated Recurrent Encoder with Novel Correlation Loss for COVID-19 Progression Prediction. Tipo de medio : Computadora Summary : The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in machine learning theory; machine learning - attention models; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging – others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
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