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Autor Zhou, S. Kevin |
Documentos disponibles escritos por este autor (7)
Crear una solicitud de compra Refinar búsqueda23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I / Martel, Anne L. ; Abolmaesumi, Purang ; Stoyanov, Danail ; Mateus, Diana ; Zuluaga, Maria A. ; Zhou, S. Kevin ; Racoceanu, Daniel ; Joskowicz, Leo
TÃtulo : 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I Tipo de documento: documento electrónico Autores: Martel, Anne L., ; Abolmaesumi, Purang, ; Stoyanov, Danail, ; Mateus, Diana, ; Zuluaga, Maria A., ; Zhou, S. Kevin, ; Racoceanu, Daniel, ; Joskowicz, Leo, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2020 Número de páginas: XXXVII, 849 p. 257 ilustraciones ISBN/ISSN/DL: 978-3-030-59710-8 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Palabras clave: Visión por computador Inteligencia artificial Ciencias sociales Bioinformática Sistemas de reconocimiento de patrones Aplicación informática en ciencias sociales y del comportamiento Computadoras y Educación BiologÃa Computacional y de Sistemas Reconocimiento de patrones automatizado Clasificación: Resumen: El conjunto de siete volúmenes LNCS 12261, 12262, 12263, 12264, 12265, 12266 y 12267 constituye las actas arbitradas de la 23.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2020, celebrada en Lima, Perú, en octubre. 2020. La conferencia se realizó de manera virtual debido a la pandemia de COVID-19. Los 542 artÃculos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1809 presentaciones en un proceso de revisión doble ciego. Los artÃculos están organizados en las siguientes secciones temáticas: Parte I: metodologÃas de aprendizaje automático Parte II: reconstrucción de imágenes; predicción y diagnóstico; métodos y reconstrucción entre dominios; adaptación de dominio; aplicaciones de aprendizaje automático; redes generativas adversarias Parte III: aplicaciones CAI; registro de imagen; instrumentación y detección de fase quirúrgica; navegación y visualización; imágenes por ultrasonido; análisis de imágenes de vÃdeo Parte IV: segmentación; modelos de formas y detección de puntos de referencia Parte V: imágenes biológicas, ópticas y microscópicas; segmentación celular y normalización de tinciones; análisis de imágenes histopatológicas; oftalmologÃa Parte VI: angiografÃa y análisis de vasos; imágenes de mama; colonoscopia; dermatologÃa; imágenes fetales; imágenes del corazón y los pulmones; imágenes musculoesqueléticas Parte VI: desarrollo cerebral y atlas; DWI y tractografÃa; redes cerebrales funcionales; neuroimagen; TomografÃa de emisión de positrones. Nota de contenido: Machine Learning Methodologies -- Attention, Suggestion and Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation -- Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistency -- Are fast labeling methods reliable? A case study of computer-aided expert annotations on microscopy slides -- Deep Reinforcement Active Learning for Medical Image Classification -- An Effective Data Refinement Approach for Upper Gastrointestinal Anatomy Recognition -- Synthetic Sample Selection via Reinforcement Learning -- Dual-level Selective Transfer Learning for Intrahepatic Cholangiocarcinoma Segmentation in Non-enhanced Abdominal CT -- BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture -- Constrain Latent Space for Schizophrenia Classification via Dual Space Mapping Net -- Have you forgotten? A method to assess ifmachine learning models have forgotten data -- Learning and Exploiting Interclass Visual Correlations for Medical Image Classification -- Feature Preserving Smoothing Provides Simple and Effective Data Augmentation for Medical Image Segmentation -- Deep kNN for Medical Image Classification -- Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration -- DECAPS: Detail-oriented Capsule Networks -- Federated Simulation for Medical Imaging -- Continual Learning of New Diseases with Dual Distillation and Ensemble Strategy -- Learning to Segment When Experts Disagree -- Deep Disentangled Hashing with Momentum Triplets for Neuroimage Search -- Learning joint shape and appearance representations with metamorphic auto-encoders -- Collaborative Learning of Cross-channel Clinical Attention for Radiotherapy-related Esophageal Fistula Prediction from CT -- Learning Bronchiole-Sensitive Airway Segmentation CNNs by Feature Recalibration and Attention Distillation -- Learning Rich Attention for Pediatric Bone Age Assessment -- Weakly Supervised Organ Localization with Attention Maps Regularized by Local Area Reconstruction -- High-order Attention Networks for Medical Image Segmentation -- NAS-SCAM: Neural Architecture Search-based Spatial and Channel Joint Attention Module for Nuclei Semantic Segmentation and Classification -- Scientific Discovery by Generating Counterfactuals using Image Translation -- Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction -- Encoding Visual Attributes in Capsules for Explainable Medical Diagnoses -- Interpretability-guided Content-based Medical Image Retrieval -- Domain aware medical image classifier interpretation by counterfactual impact analysis -- Towards Emergent Language Symbolic Semantic Segmentation and Model Interpretability -- Meta Corrupted Pixels Mining for Medical Image Segmentation -- UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation -- Difficulty-aware Meta-learning for Rare Disease Diagnosis -- Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification -- Automatic Data Augmentation for 3D Medical Image Segmentation -- MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation -- Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations -- Dual-task Self-supervision for Cross-Modality Domain Adaptation -- Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation -- Test-time Unsupervised Domain Adaptation -- Self domain adapted network -- Entropy Guided Unsupervised Domain Adaptation for Cross-Center Hip Cartilage Segmentation from MRI -- User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation -- SALAD: Self-Supervised Aggregation Learning for Anomaly Detection on X-Rays -- Scribble-based Domain Adaptation via Deep Co-Segmentation -- Source-Relaxed Domain Adaptation for Image Segmentation -- Region-of-interest guided Supervoxel Inpainting for Self-supervision -- Harnessing Uncertainty in Domain Adaptation for MRI Prostate Lesion Segmentation -- Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation -- DMNet: Difference Minimization Network for Semi-supervised Segmentation in Medical Images -- Double-uncertainty Weighted Method for Semi-supervised Learning -- Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images -- Local and Global Structure-aware Entropy Regularized Mean Teacher Model for 3D Left Atrium segmentation -- Improving dense pixelwise prediction of epithelial density using unsupervised data augmentation for consistency regularization -- Knowledge-guided Pretext Learning for Utero-placental Interface Detection -- Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy -- Semi-supervised Medical Image Classification with Global Latent Mixing -- Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation -- Semi-Supervised Classification of Diagnostic Radiographs with NoTeacher: A Teacher that is not Mean -- Predicting Potential Propensity of Adolescents to Drugs via New Semi-Supervised Deep Ordinal Regression Model -- Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet -- Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation -- Realistic Adversarial Data Augmentation for MR Image Segmentation -- Learning to Segment Anatomical Structures Accurately from One Exemplar -- Uncertainty estimates as data selection criteria to boost omni-supervised learning -- Extreme Consistency: Overcoming Annotation Scarcity and Domain Shifts -- Spatio-temporal Consistency and Negative LabelTransfer for 3D freehand US Segmentation -- Characterizing Label Errors: Confident Learning for Noisy-labeled Image Segmentation -- Leveraging Undiagnosed Data for Glaucoma Classification with Teacher-Student Learning -- Difficulty-aware Glaucoma Classification with Multi-Rater Consensus Modeling -- Intra-operative Forecasting of Growth Modulation Spine Surgery Outcomes with Spatio-Temporal Dynamic Networks -- Self-supervision on Unlabelled OR Data for Multi-person 2D/3D Human Pose Estimation -- Knowledge distillation from multi-modal to mono-modal segmentation networks -- Heterogeneity Measurement of Cardiac Tissues Leveraging Uncertainty Information from Image Segmentation -- Efficient Shapley Explanation For Features Importance Estimation Under Uncertainty -- Cartilage Segmentation in High-Resolution 3D Micro-CT Images via Uncertainty-Guided Self-Training with Very Sparse Annotation -- Probabilistic 3D surface reconstruction from sparse MRI information -- Can you trust predictive uncertainty under real dataset shifts in digital pathology? -- Deep Generative Model for Synthetic-CT Generation with Uncertainty Predictions. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I [documento electrónico] / Martel, Anne L., ; Abolmaesumi, Purang, ; Stoyanov, Danail, ; Mateus, Diana, ; Zuluaga, Maria A., ; Zhou, S. Kevin, ; Racoceanu, Daniel, ; Joskowicz, Leo, . - 1 ed. . - [s.l.] : Springer, 2020 . - XXXVII, 849 p. 257 ilustraciones.
ISBN : 978-3-030-59710-8
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Palabras clave: Visión por computador Inteligencia artificial Ciencias sociales Bioinformática Sistemas de reconocimiento de patrones Aplicación informática en ciencias sociales y del comportamiento Computadoras y Educación BiologÃa Computacional y de Sistemas Reconocimiento de patrones automatizado Clasificación: Resumen: El conjunto de siete volúmenes LNCS 12261, 12262, 12263, 12264, 12265, 12266 y 12267 constituye las actas arbitradas de la 23.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2020, celebrada en Lima, Perú, en octubre. 2020. La conferencia se realizó de manera virtual debido a la pandemia de COVID-19. Los 542 artÃculos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1809 presentaciones en un proceso de revisión doble ciego. Los artÃculos están organizados en las siguientes secciones temáticas: Parte I: metodologÃas de aprendizaje automático Parte II: reconstrucción de imágenes; predicción y diagnóstico; métodos y reconstrucción entre dominios; adaptación de dominio; aplicaciones de aprendizaje automático; redes generativas adversarias Parte III: aplicaciones CAI; registro de imagen; instrumentación y detección de fase quirúrgica; navegación y visualización; imágenes por ultrasonido; análisis de imágenes de vÃdeo Parte IV: segmentación; modelos de formas y detección de puntos de referencia Parte V: imágenes biológicas, ópticas y microscópicas; segmentación celular y normalización de tinciones; análisis de imágenes histopatológicas; oftalmologÃa Parte VI: angiografÃa y análisis de vasos; imágenes de mama; colonoscopia; dermatologÃa; imágenes fetales; imágenes del corazón y los pulmones; imágenes musculoesqueléticas Parte VI: desarrollo cerebral y atlas; DWI y tractografÃa; redes cerebrales funcionales; neuroimagen; TomografÃa de emisión de positrones. Nota de contenido: Machine Learning Methodologies -- Attention, Suggestion and Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation -- Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistency -- Are fast labeling methods reliable? A case study of computer-aided expert annotations on microscopy slides -- Deep Reinforcement Active Learning for Medical Image Classification -- An Effective Data Refinement Approach for Upper Gastrointestinal Anatomy Recognition -- Synthetic Sample Selection via Reinforcement Learning -- Dual-level Selective Transfer Learning for Intrahepatic Cholangiocarcinoma Segmentation in Non-enhanced Abdominal CT -- BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture -- Constrain Latent Space for Schizophrenia Classification via Dual Space Mapping Net -- Have you forgotten? A method to assess ifmachine learning models have forgotten data -- Learning and Exploiting Interclass Visual Correlations for Medical Image Classification -- Feature Preserving Smoothing Provides Simple and Effective Data Augmentation for Medical Image Segmentation -- Deep kNN for Medical Image Classification -- Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration -- DECAPS: Detail-oriented Capsule Networks -- Federated Simulation for Medical Imaging -- Continual Learning of New Diseases with Dual Distillation and Ensemble Strategy -- Learning to Segment When Experts Disagree -- Deep Disentangled Hashing with Momentum Triplets for Neuroimage Search -- Learning joint shape and appearance representations with metamorphic auto-encoders -- Collaborative Learning of Cross-channel Clinical Attention for Radiotherapy-related Esophageal Fistula Prediction from CT -- Learning Bronchiole-Sensitive Airway Segmentation CNNs by Feature Recalibration and Attention Distillation -- Learning Rich Attention for Pediatric Bone Age Assessment -- Weakly Supervised Organ Localization with Attention Maps Regularized by Local Area Reconstruction -- High-order Attention Networks for Medical Image Segmentation -- NAS-SCAM: Neural Architecture Search-based Spatial and Channel Joint Attention Module for Nuclei Semantic Segmentation and Classification -- Scientific Discovery by Generating Counterfactuals using Image Translation -- Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction -- Encoding Visual Attributes in Capsules for Explainable Medical Diagnoses -- Interpretability-guided Content-based Medical Image Retrieval -- Domain aware medical image classifier interpretation by counterfactual impact analysis -- Towards Emergent Language Symbolic Semantic Segmentation and Model Interpretability -- Meta Corrupted Pixels Mining for Medical Image Segmentation -- UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation -- Difficulty-aware Meta-learning for Rare Disease Diagnosis -- Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification -- Automatic Data Augmentation for 3D Medical Image Segmentation -- MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation -- Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations -- Dual-task Self-supervision for Cross-Modality Domain Adaptation -- Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation -- Test-time Unsupervised Domain Adaptation -- Self domain adapted network -- Entropy Guided Unsupervised Domain Adaptation for Cross-Center Hip Cartilage Segmentation from MRI -- User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation -- SALAD: Self-Supervised Aggregation Learning for Anomaly Detection on X-Rays -- Scribble-based Domain Adaptation via Deep Co-Segmentation -- Source-Relaxed Domain Adaptation for Image Segmentation -- Region-of-interest guided Supervoxel Inpainting for Self-supervision -- Harnessing Uncertainty in Domain Adaptation for MRI Prostate Lesion Segmentation -- Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation -- DMNet: Difference Minimization Network for Semi-supervised Segmentation in Medical Images -- Double-uncertainty Weighted Method for Semi-supervised Learning -- Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images -- Local and Global Structure-aware Entropy Regularized Mean Teacher Model for 3D Left Atrium segmentation -- Improving dense pixelwise prediction of epithelial density using unsupervised data augmentation for consistency regularization -- Knowledge-guided Pretext Learning for Utero-placental Interface Detection -- Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy -- Semi-supervised Medical Image Classification with Global Latent Mixing -- Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation -- Semi-Supervised Classification of Diagnostic Radiographs with NoTeacher: A Teacher that is not Mean -- Predicting Potential Propensity of Adolescents to Drugs via New Semi-Supervised Deep Ordinal Regression Model -- Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet -- Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation -- Realistic Adversarial Data Augmentation for MR Image Segmentation -- Learning to Segment Anatomical Structures Accurately from One Exemplar -- Uncertainty estimates as data selection criteria to boost omni-supervised learning -- Extreme Consistency: Overcoming Annotation Scarcity and Domain Shifts -- Spatio-temporal Consistency and Negative LabelTransfer for 3D freehand US Segmentation -- Characterizing Label Errors: Confident Learning for Noisy-labeled Image Segmentation -- Leveraging Undiagnosed Data for Glaucoma Classification with Teacher-Student Learning -- Difficulty-aware Glaucoma Classification with Multi-Rater Consensus Modeling -- Intra-operative Forecasting of Growth Modulation Spine Surgery Outcomes with Spatio-Temporal Dynamic Networks -- Self-supervision on Unlabelled OR Data for Multi-person 2D/3D Human Pose Estimation -- Knowledge distillation from multi-modal to mono-modal segmentation networks -- Heterogeneity Measurement of Cardiac Tissues Leveraging Uncertainty Information from Image Segmentation -- Efficient Shapley Explanation For Features Importance Estimation Under Uncertainty -- Cartilage Segmentation in High-Resolution 3D Micro-CT Images via Uncertainty-Guided Self-Training with Very Sparse Annotation -- Probabilistic 3D surface reconstruction from sparse MRI information -- Can you trust predictive uncertainty under real dataset shifts in digital pathology? -- Deep Generative Model for Synthetic-CT Generation with Uncertainty Predictions. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part II Tipo de documento: documento electrónico Autores: Martel, Anne L., ; Abolmaesumi, Purang, ; Stoyanov, Danail, ; Mateus, Diana, ; Zuluaga, Maria A., ; Zhou, S. Kevin, ; Racoceanu, Daniel, ; Joskowicz, Leo, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2020 Número de páginas: XXXVII, 785 p. 258 ilustraciones, 228 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-59713-9 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Palabras clave: Visión por computador Inteligencia artificial Ciencias sociales Sistemas de reconocimiento de patrones Bioinformática Aplicación informática en ciencias sociales y del comportamiento Computadoras y Educación Reconocimiento de patrones automatizado BiologÃa Computacional y de Sistemas Clasificación: Resumen: El conjunto de siete volúmenes LNCS 12261, 12262, 12263, 12264, 12265, 12266 y 12267 constituye las actas arbitradas de la 23.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2020, celebrada en Lima, Perú, en octubre. 2020. La conferencia se realizó de manera virtual debido a la pandemia de COVID-19. Los 542 artÃculos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1809 presentaciones en un proceso de revisión doble ciego. Los artÃculos están organizados en las siguientes secciones temáticas: Parte I: metodologÃas de aprendizaje automático Parte II: reconstrucción de imágenes; predicción y diagnóstico; métodos y reconstrucción entre dominios; adaptación de dominio; aplicaciones de aprendizaje automático; redes generativas adversarias Parte III: aplicaciones CAI; registro de imagen; instrumentación y detección de fase quirúrgica; navegación y visualización; imágenes por ultrasonido; análisis de imágenes de vÃdeo Parte IV: segmentación; modelos de formas y detección de puntos de referencia Parte V: imágenes biológicas, ópticas y microscópicas; segmentación celular y normalización de tinciones; análisis de imágenes histopatológicas; oftalmologÃa Parte VI: angiografÃa y análisis de vasos; imágenes de mama; colonoscopia; dermatologÃa; imágenes fetales; imágenes del corazón y los pulmones; imágenes musculoesqueléticas Parte VI: desarrollo cerebral y atlas; DWI y tractografÃa; redes cerebrales funcionales; neuroimagen; TomografÃa de emisión de positrones. Nota de contenido: Image Reconstruction -- Improving Amide Proton Transfer-weighted MRI Reconstruction using T2-weighted Images -- Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations -- Active MR k-space Sampling with Reinforcement Learning -- Fast Correction of Eddy-Current and Susceptibility-Induced Distortions Using Rotation-Invariant Contrasts -- Joint reconstruction and bias field correction for undersampled MR imaging -- Joint Total Variation ESTATICS for Robust Multi-Parameter Mapping -- End-to-End Variational Networks for Accelerated MRI Reconstruction -- 3d-SMRnet: Achieving a new quality of MPI system matrix recovery by deep learning -- MRI Image Reconstruction via Learning Optimization Using Neural ODEs -- An evolutionary framework for microstructure-sensitive generalized diffusion gradient waveforms -- Lesion Mask-based Simultaneous Synthesis of Anatomic and Molecular MR Images using a GAN -- T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions -- Learned Proximal Networks for Quantitative Susceptibility Mapping -- Learning A Gradient Guidance for Spatially Isotropic MRI Super-Resolution Reconstruction -- Encoding Metal Mask Projection for Metal Artifact Reduction in Computed Tomography -- Acceleration of High-resolution 3D MR Fingerprinting via a Graph Convolutional Network -- Deep Attentive Wasserstein Generative Adversarial Network for MRI Reconstruction with Recurrent Context-Awareness -- Learning MRI $k$-Space Subsampling Pattern using Progressive Weight Pruning -- Model-driven Deep Attention Network for Ultra-fast Compressive Sensing MRI Guided by Cross-contrast MR Image -- Simultaneous Estimation of X-ray Back-Scatter and Forward-Scatter using Multi-Task Learning -- Prediction and Diagnosis -- MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response -- M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients -- Automatic Detection of Free Intra-Abdominal Air in Computed Tomography -- Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Deep Learning with Integrative Imaging, Molecular and Demographic Data -- Geodesically Smoothed Tensor Features for Pulmonary Hypertension Prognosis using the Heart and Surrounding Tissues -- Ovarian Cancer Prediction in Proteomic Data Using Stacked Asymmetric Convolution -- DeepPrognosis: Preoperative Prediction of Pancreatic Cancer Survival and Surgical Margin via Dynamic Contrast-Enhanced CT Imaging -- Holistic Analysis of Abdominal CT for Predicting the Grade of Dysplasia of Pancreatic Lesions -- Feature-enhanced Graph Networks for Genetic Mutational Prediction Using Histopathological Images in Colon cancer -- Spatial-And-Context aware (SpACe) "virtual biopsy'' radiogenomic maps to target tumor mutational status on structural MRI -- CorrSigNet: Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved ComputerAided Diagnosis -- Preoperative prediction of lymph node metastasis from clinical DCE MRI of the primary breast tumor using a 4D CNN -- Learning Differential Diagnosis of Skin Conditions with Co-occurrence Supervision using Graph Convolutional Networks -- Cross-Domain Methods and Reconstruction -- Unified cross-modality feature disentangler for unsupervised multi-domain MRI abdomen organs segmentation -- Dynamic memory to alleviate catastrophic forgetting in continuous learning settings -- Unlearning Scanner Bias for MRI Harmonisation -- Cross-Domain Image Translation by Shared Latent Gaussian Mixture Model -- Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy -- X2Teeth: 3D Teeth Reconstruction from a Single Panoramic Radiograph -- Domain Adaptation for Ultrasound Beamforming -- CDF-Net: Cross-Domain Fusion Network for accelerated MRI reconstruction -- Domain Adaptation -- Improve Unseen Domain Generalization via Enhanced Local Color Transformation and Augmentation -- Transport-based Joint Distribution Alignment for Multi-site Autism Spectrum Disorder Diagnosis using Resting-state fMRI -- Automatic and interpretable model for periodontitis diagnosis in panoramic radiographs -- Residual-CycleGAN based Camera Adaptation for Robust Diabetic Retinopathy Screening -- Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains -- Automatic Plane Adjustment of Orthopedic Intraoperative Flat Panel Detector CT-Volumes -- Unsupervised Graph Domain Adaptation for Neurodevelopmental Disorders Diagnosis -- JBFnet - Low Dose CT Denoising by Trainable Joint Bilateral Filtering -- MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint -- Machine Learning Applications -- Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment -- Domain-specific loss design for unsupervised physical training: A new approach to modeling medical MLsolutions -- Multiatlas Calibration of Biophysical Brain Tumor Growth Models with Mass Effect -- Chest X-ray Report Generation through Fine-Grained Label Learning -- Peri-Diagnostic Decision Support Through Cost-Efficient Feature Acquisition at Test-Time -- A Deep Bayesian Video Analysis Framework: Towards a More Robust Estimation of Ejection Fraction -- Distractor-Aware Neuron Intrinsic Learning for Generic 2D Medical Image Classifications -- Large-scale inference of liver fat with neural networks on UK Biobank body MRI -- BUNET: Blind Medical Image Segmentation Based on Secure UNET -- Temporal-consistent Segmentation of Echocardiography with Co-learning from Appearance and Shape -- Decision Support for Intoxication Prediction Using Graph Convolutional Networks -- Latent-Graph Learning for Disease Prediction -- Generative Adversarial Networks -- BR-GAN: Bilateral Residual Generating Adversarial Network for Mammogram Classification -- Cycle Structure and Illumination Constrained GAN for Medical Image Enhancement -- Generating Dual-Energy Subtraction Soft-Tissue Images from Chest Radiographs via Bone Edge-Guided GAN -- GANDALF: Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-tuning for Alzheimer's Disease Diagnosis from MRI -- Brain MR to PET Synthesis via Bidirectional Generative Adversarial Network -- AGAN: An Anatomy Corrector Conditional Generative Adversarial Network -- SteGANomaly: Inhibiting CycleGAN Steganography for Unsupervised Anomaly Detection in Brain MRI -- Flow-based Deformation Guidance for Unpaired Multi-Contrast MRI Image-to-Image Translation -- Interpretation of Disease Evidence for Medical Images Using Adversarial Deformation Fields -- Spatial-Intensity Transform GANs for High Fidelity Medical Image-to-Image Translation -- Graded Image Generation Using Stratified CycleGAN -- Prediction of Plantar Shear Stress Distribution by Conditional GAN with Attention Mechanism. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part II [documento electrónico] / Martel, Anne L., ; Abolmaesumi, Purang, ; Stoyanov, Danail, ; Mateus, Diana, ; Zuluaga, Maria A., ; Zhou, S. Kevin, ; Racoceanu, Daniel, ; Joskowicz, Leo, . - 1 ed. . - [s.l.] : Springer, 2020 . - XXXVII, 785 p. 258 ilustraciones, 228 ilustraciones en color.
ISBN : 978-3-030-59713-9
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Palabras clave: Visión por computador Inteligencia artificial Ciencias sociales Sistemas de reconocimiento de patrones Bioinformática Aplicación informática en ciencias sociales y del comportamiento Computadoras y Educación Reconocimiento de patrones automatizado BiologÃa Computacional y de Sistemas Clasificación: Resumen: El conjunto de siete volúmenes LNCS 12261, 12262, 12263, 12264, 12265, 12266 y 12267 constituye las actas arbitradas de la 23.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2020, celebrada en Lima, Perú, en octubre. 2020. La conferencia se realizó de manera virtual debido a la pandemia de COVID-19. Los 542 artÃculos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1809 presentaciones en un proceso de revisión doble ciego. Los artÃculos están organizados en las siguientes secciones temáticas: Parte I: metodologÃas de aprendizaje automático Parte II: reconstrucción de imágenes; predicción y diagnóstico; métodos y reconstrucción entre dominios; adaptación de dominio; aplicaciones de aprendizaje automático; redes generativas adversarias Parte III: aplicaciones CAI; registro de imagen; instrumentación y detección de fase quirúrgica; navegación y visualización; imágenes por ultrasonido; análisis de imágenes de vÃdeo Parte IV: segmentación; modelos de formas y detección de puntos de referencia Parte V: imágenes biológicas, ópticas y microscópicas; segmentación celular y normalización de tinciones; análisis de imágenes histopatológicas; oftalmologÃa Parte VI: angiografÃa y análisis de vasos; imágenes de mama; colonoscopia; dermatologÃa; imágenes fetales; imágenes del corazón y los pulmones; imágenes musculoesqueléticas Parte VI: desarrollo cerebral y atlas; DWI y tractografÃa; redes cerebrales funcionales; neuroimagen; TomografÃa de emisión de positrones. Nota de contenido: Image Reconstruction -- Improving Amide Proton Transfer-weighted MRI Reconstruction using T2-weighted Images -- Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations -- Active MR k-space Sampling with Reinforcement Learning -- Fast Correction of Eddy-Current and Susceptibility-Induced Distortions Using Rotation-Invariant Contrasts -- Joint reconstruction and bias field correction for undersampled MR imaging -- Joint Total Variation ESTATICS for Robust Multi-Parameter Mapping -- End-to-End Variational Networks for Accelerated MRI Reconstruction -- 3d-SMRnet: Achieving a new quality of MPI system matrix recovery by deep learning -- MRI Image Reconstruction via Learning Optimization Using Neural ODEs -- An evolutionary framework for microstructure-sensitive generalized diffusion gradient waveforms -- Lesion Mask-based Simultaneous Synthesis of Anatomic and Molecular MR Images using a GAN -- T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions -- Learned Proximal Networks for Quantitative Susceptibility Mapping -- Learning A Gradient Guidance for Spatially Isotropic MRI Super-Resolution Reconstruction -- Encoding Metal Mask Projection for Metal Artifact Reduction in Computed Tomography -- Acceleration of High-resolution 3D MR Fingerprinting via a Graph Convolutional Network -- Deep Attentive Wasserstein Generative Adversarial Network for MRI Reconstruction with Recurrent Context-Awareness -- Learning MRI $k$-Space Subsampling Pattern using Progressive Weight Pruning -- Model-driven Deep Attention Network for Ultra-fast Compressive Sensing MRI Guided by Cross-contrast MR Image -- Simultaneous Estimation of X-ray Back-Scatter and Forward-Scatter using Multi-Task Learning -- Prediction and Diagnosis -- MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response -- M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients -- Automatic Detection of Free Intra-Abdominal Air in Computed Tomography -- Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Deep Learning with Integrative Imaging, Molecular and Demographic Data -- Geodesically Smoothed Tensor Features for Pulmonary Hypertension Prognosis using the Heart and Surrounding Tissues -- Ovarian Cancer Prediction in Proteomic Data Using Stacked Asymmetric Convolution -- DeepPrognosis: Preoperative Prediction of Pancreatic Cancer Survival and Surgical Margin via Dynamic Contrast-Enhanced CT Imaging -- Holistic Analysis of Abdominal CT for Predicting the Grade of Dysplasia of Pancreatic Lesions -- Feature-enhanced Graph Networks for Genetic Mutational Prediction Using Histopathological Images in Colon cancer -- Spatial-And-Context aware (SpACe) "virtual biopsy'' radiogenomic maps to target tumor mutational status on structural MRI -- CorrSigNet: Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved ComputerAided Diagnosis -- Preoperative prediction of lymph node metastasis from clinical DCE MRI of the primary breast tumor using a 4D CNN -- Learning Differential Diagnosis of Skin Conditions with Co-occurrence Supervision using Graph Convolutional Networks -- Cross-Domain Methods and Reconstruction -- Unified cross-modality feature disentangler for unsupervised multi-domain MRI abdomen organs segmentation -- Dynamic memory to alleviate catastrophic forgetting in continuous learning settings -- Unlearning Scanner Bias for MRI Harmonisation -- Cross-Domain Image Translation by Shared Latent Gaussian Mixture Model -- Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy -- X2Teeth: 3D Teeth Reconstruction from a Single Panoramic Radiograph -- Domain Adaptation for Ultrasound Beamforming -- CDF-Net: Cross-Domain Fusion Network for accelerated MRI reconstruction -- Domain Adaptation -- Improve Unseen Domain Generalization via Enhanced Local Color Transformation and Augmentation -- Transport-based Joint Distribution Alignment for Multi-site Autism Spectrum Disorder Diagnosis using Resting-state fMRI -- Automatic and interpretable model for periodontitis diagnosis in panoramic radiographs -- Residual-CycleGAN based Camera Adaptation for Robust Diabetic Retinopathy Screening -- Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains -- Automatic Plane Adjustment of Orthopedic Intraoperative Flat Panel Detector CT-Volumes -- Unsupervised Graph Domain Adaptation for Neurodevelopmental Disorders Diagnosis -- JBFnet - Low Dose CT Denoising by Trainable Joint Bilateral Filtering -- MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint -- Machine Learning Applications -- Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment -- Domain-specific loss design for unsupervised physical training: A new approach to modeling medical MLsolutions -- Multiatlas Calibration of Biophysical Brain Tumor Growth Models with Mass Effect -- Chest X-ray Report Generation through Fine-Grained Label Learning -- Peri-Diagnostic Decision Support Through Cost-Efficient Feature Acquisition at Test-Time -- A Deep Bayesian Video Analysis Framework: Towards a More Robust Estimation of Ejection Fraction -- Distractor-Aware Neuron Intrinsic Learning for Generic 2D Medical Image Classifications -- Large-scale inference of liver fat with neural networks on UK Biobank body MRI -- BUNET: Blind Medical Image Segmentation Based on Secure UNET -- Temporal-consistent Segmentation of Echocardiography with Co-learning from Appearance and Shape -- Decision Support for Intoxication Prediction Using Graph Convolutional Networks -- Latent-Graph Learning for Disease Prediction -- Generative Adversarial Networks -- BR-GAN: Bilateral Residual Generating Adversarial Network for Mammogram Classification -- Cycle Structure and Illumination Constrained GAN for Medical Image Enhancement -- Generating Dual-Energy Subtraction Soft-Tissue Images from Chest Radiographs via Bone Edge-Guided GAN -- GANDALF: Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-tuning for Alzheimer's Disease Diagnosis from MRI -- Brain MR to PET Synthesis via Bidirectional Generative Adversarial Network -- AGAN: An Anatomy Corrector Conditional Generative Adversarial Network -- SteGANomaly: Inhibiting CycleGAN Steganography for Unsupervised Anomaly Detection in Brain MRI -- Flow-based Deformation Guidance for Unpaired Multi-Contrast MRI Image-to-Image Translation -- Interpretation of Disease Evidence for Medical Images Using Adversarial Deformation Fields -- Spatial-Intensity Transform GANs for High Fidelity Medical Image-to-Image Translation -- Graded Image Generation Using Stratified CycleGAN -- Prediction of Plantar Shear Stress Distribution by Conditional GAN with Attention Mechanism. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III Tipo de documento: documento electrónico Autores: Martel, Anne L., ; Abolmaesumi, Purang, ; Stoyanov, Danail, ; Mateus, Diana, ; Zuluaga, Maria A., ; Zhou, S. Kevin, ; Racoceanu, Daniel, ; Joskowicz, Leo, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2020 Número de páginas: XXXVI, 799 p. 23 ilustraciones ISBN/ISSN/DL: 978-3-030-59716-0 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Palabras clave: Visión por computador Sistemas de reconocimiento de patrones Inteligencia artificial Ciencias sociales Bioinformática Reconocimiento de patrones automatizado Aplicación informática en ciencias sociales y del comportamiento Computadoras y Educación BiologÃa Computacional y de Sistemas Clasificación: Resumen: El conjunto de siete volúmenes LNCS 12261, 12262, 12263, 12264, 12265, 12266 y 12267 constituye las actas arbitradas de la 23.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2020, celebrada en Lima, Perú, en octubre. 2020. La conferencia se realizó de manera virtual debido a la pandemia de COVID-19. Los 542 artÃculos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1809 presentaciones en un proceso de revisión doble ciego. Los artÃculos están organizados en las siguientes secciones temáticas: Parte I: metodologÃas de aprendizaje automático Parte II: reconstrucción de imágenes; predicción y diagnóstico; métodos y reconstrucción entre dominios; adaptación de dominio; aplicaciones de aprendizaje automático; redes generativas adversarias Parte III: aplicaciones CAI; registro de imagen; instrumentación y detección de fase quirúrgica; navegación y visualización; imágenes por ultrasonido; análisis de imágenes de vÃdeo Parte IV: segmentación; modelos de formas y detección de puntos de referencia Parte V: imágenes biológicas, ópticas y microscópicas; segmentación celular y normalización de tinciones; análisis de imágenes histopatológicas; oftalmologÃa Parte VI: angiografÃa y análisis de vasos; imágenes de mama; colonoscopia; dermatologÃa; imágenes fetales; imágenes del corazón y los pulmones; imágenes musculoesqueléticas Parte VI: desarrollo cerebral y atlas; DWI y tractografÃa; redes cerebrales funcionales; neuroimagen; TomografÃa de emisión de positrones. Nota de contenido: CAI Applications -- Reconstructing Sinus Anatomy from Endoscopic Video -- Towards a Radiation-free Approach for Quantitative Longitudinal Assessment -- Inertial Measurements for Motion Compensation in Weight-bearing Cone-beam CT of the Knee -- Feasibility check: can audio be a simple alternative to force-based feedback for needle guidance? -- A Graph-Based Method for Optimal Active Electrode Selection in Cochlear Implants -- Improved resection margins in surgical oncology using intraoperative mass spectrometry -- Self-Supervsied Domain Adaptation for Patient-Specific, Real-Time Tissue Tracking -- An Interactive Mixed Reality Platform for Bedside Surgical Procedures -- Ear Cartilage Inference for Reconstructive Surgery with Convolutional Mesh Autoencoders -- Robust Multi-modal 3D Patient Body Modeling -- A New Electromagnetic-Video Endoscope Tracking Method via Anatomical Constraints and Historically Observed Differential Evolution -- Malocclusion Treatment Planning via PointNet based Spatial Transformation Network -- Simulation of Brain Resection for Cavity Segmentation Using Self-Supervised and Semi-Supervised Learning -- Local Contractive Registration for Quantification of Tissue Shrinkage in Assessment of Microwave Ablation -- Reinforcement Learning of Musculoskeletal Control from Functional Simulations -- Image Registration -- MvMM-RegNet: A new image registration framework based on multivariate mixture model and neural network estimation -- Database Annotation with few Examples: An Atlas-based Framework using Diffeomorphic Registration of 3D trees -- Pair-wise and Group-wise Deformation Consistency in Deep Registration Network -- Semantic Hierarchy Guided Registration Networks for Intra-Subject Pulmonary CT Image Alignment -- Highly accurate and memory efficient unsupervised learning-based discrete CT registration using 2.5D displacement search -- Unsupervised Learning Model for Registration of Multi-Phase Ultra-Widefield Fluorescein Angiography -- Large DeformationDiffeomorphic Image Registration with Laplacian Pyramid Networks -- Adversarial Uni- and Multi-modal Stream Networks for Multimodal Image Registration -- Cross-Modality Multi-Atlas Segmentation Using Deep Neural Networks -- Longitudinal Image Registration with Temporal-order and Subject-specificity Discrimination -- Flexible Bayesian Modelling for Nonlinear Image Registration -- Are Registration Uncertainty and Error Monotonically Associated? -- MR-to-US registration using multiclass segmentation of hepatic vasculature with a reduced 3D U-Net -- Detecting Pancreatic Ductal Adenocarcinoma in Multi-phase CT Scans via Alignment Ensemble -- Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRI -- Fluid registration between lung CT and stationary chest tomosynthesis images -- Anatomical Data Augmentation via Fluid-based Image Registration -- Generliazing Spatial Transformers to Projective Geometry with Applications to 2D/3D Registration -- Instrumentation and Surgical Phase Detection -- TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks -- Surgical Video Motion Magnification with Suppression of Instrument Artefacts -- Recognition of Instrument-Tissue Interactions in Endoscopic Videos via Action Triplets -- AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation -- Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery -- Navigation and Visualization -- Can a hand-held navigation device reduce cognitive load? A user-centered approach evaluated by 18 surgeons -- Symmetric Dilated Convolution for Surgical Gesture Recognition -- Deep Selection: A Fully Supervised Camera Selection Network for Surgery Recordings -- Interacting with Medical Volume Data in Projective Augmented Reality -- VR Simulation of Novel Hands-free Interaction Concepts for Surgical Robotic Visualization Systems -- Spatially-Aware Displays for Computer Assisted Interventions -- Ultrasound Imaging.-Sensorless Freehand 3D Ultrasound Reconstruction via Deep Contextual Learning -- Ultra2Speech - A Deep Learning Framework for Formant Frequency Estimation and Tracking from Ultrasound Tongue Images -- Ultrasound Video Summarization using Deep Reinforcement Learning -- Predicting obstructive hydronephrosis based on ultrasound alone -- Semi-Supervised Training of Optical Flow Convolutional Neural Networks in Ultrasound Elastography -- Three-dimensional thyroid assessment from untracked 2D ultrasound clips -- Complex Cancer Detector: Complex Neural Networks on Non-stationary Time Series for Guiding Systematic Prostate Biopsy -- Self-supervised Contrastive Video-Speech Representation Learning for Ultrasound -- Directing Ultrasound Probe Placement for Image Guided Prostate Radiotherapy -- Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound -- Contrastive Rendering for Ultrasound Image Segmentation -- An Unsupervised Approach to Ultrasound Elastography with End-to-end Strain Regularisation -- Automatic Probe Movement Guidance for Freehand Obstetric Ultrasound -- Video Image Analysis -- ISINet: An Instance-Based Approach for Surgical Instrument Segmentation -- Reliable Liver Fibrosis Assessment from Ultrasound using Global Hetero-Image Fusion and View-Specific Parameterization -- Toward Rapid Stroke Diagnosis with Multimodal Deep Learning -- Learning and Reasoning with the Graph Structure Representation in Robotic Surgery -- Vision-based Estimation of MDS-UPDRS Gait Scores for Assessing Parkinson's Disease Motor Severity -- Searching for Efficient Architecture for Instrument Segmentation in Robotic Surgery -- Unsupervised Surgical Instrument Segmentation via Anchor Generation and Semantic Diffusion -- Towards Accurate and Interpretable Surgical Skill Assessment: A Video-Based Method Incorporating Recognized Surgical Gestures and Skill Levels -- Learning Motion Flows for Semi-supervised Instrument Segmentation from Robotic Surgical Video -- Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification -- Synthetic and Real Inputs for Tool Segmentation in Robotic Surgery -- Perfusion Quantification from Endoscopic Videos: Learning to Read Tumour Signatures -- Asynchronous in Parallel Detection and Tracking (AIPDT): Real-time Robust Polyp Detection -- OfGAN: Realistic Rendition of Synthetic Colonoscopy Videos -- Two-Stream Deep Feature Modelling for Automated Video Endoscopy Data Analysis -- Rethinking Anticipation Tasks: Uncertainty-aware Anticipation of Sparse Surgical Instrument Usage for Context-aware Assistance -- Deep Placental Vessel Segmentation for Fetoscopic Mosaicking -- Deep Multi-View Stereo for Dense 3D Reconstruction from Monocular Endoscopic Video -- Endo-Sim2Real: Consistency learning-based domain adaptation for instrument segmentation. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III [documento electrónico] / Martel, Anne L., ; Abolmaesumi, Purang, ; Stoyanov, Danail, ; Mateus, Diana, ; Zuluaga, Maria A., ; Zhou, S. Kevin, ; Racoceanu, Daniel, ; Joskowicz, Leo, . - 1 ed. . - [s.l.] : Springer, 2020 . - XXXVI, 799 p. 23 ilustraciones.
ISBN : 978-3-030-59716-0
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Palabras clave: Visión por computador Sistemas de reconocimiento de patrones Inteligencia artificial Ciencias sociales Bioinformática Reconocimiento de patrones automatizado Aplicación informática en ciencias sociales y del comportamiento Computadoras y Educación BiologÃa Computacional y de Sistemas Clasificación: Resumen: El conjunto de siete volúmenes LNCS 12261, 12262, 12263, 12264, 12265, 12266 y 12267 constituye las actas arbitradas de la 23.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2020, celebrada en Lima, Perú, en octubre. 2020. La conferencia se realizó de manera virtual debido a la pandemia de COVID-19. Los 542 artÃculos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1809 presentaciones en un proceso de revisión doble ciego. Los artÃculos están organizados en las siguientes secciones temáticas: Parte I: metodologÃas de aprendizaje automático Parte II: reconstrucción de imágenes; predicción y diagnóstico; métodos y reconstrucción entre dominios; adaptación de dominio; aplicaciones de aprendizaje automático; redes generativas adversarias Parte III: aplicaciones CAI; registro de imagen; instrumentación y detección de fase quirúrgica; navegación y visualización; imágenes por ultrasonido; análisis de imágenes de vÃdeo Parte IV: segmentación; modelos de formas y detección de puntos de referencia Parte V: imágenes biológicas, ópticas y microscópicas; segmentación celular y normalización de tinciones; análisis de imágenes histopatológicas; oftalmologÃa Parte VI: angiografÃa y análisis de vasos; imágenes de mama; colonoscopia; dermatologÃa; imágenes fetales; imágenes del corazón y los pulmones; imágenes musculoesqueléticas Parte VI: desarrollo cerebral y atlas; DWI y tractografÃa; redes cerebrales funcionales; neuroimagen; TomografÃa de emisión de positrones. Nota de contenido: CAI Applications -- Reconstructing Sinus Anatomy from Endoscopic Video -- Towards a Radiation-free Approach for Quantitative Longitudinal Assessment -- Inertial Measurements for Motion Compensation in Weight-bearing Cone-beam CT of the Knee -- Feasibility check: can audio be a simple alternative to force-based feedback for needle guidance? -- A Graph-Based Method for Optimal Active Electrode Selection in Cochlear Implants -- Improved resection margins in surgical oncology using intraoperative mass spectrometry -- Self-Supervsied Domain Adaptation for Patient-Specific, Real-Time Tissue Tracking -- An Interactive Mixed Reality Platform for Bedside Surgical Procedures -- Ear Cartilage Inference for Reconstructive Surgery with Convolutional Mesh Autoencoders -- Robust Multi-modal 3D Patient Body Modeling -- A New Electromagnetic-Video Endoscope Tracking Method via Anatomical Constraints and Historically Observed Differential Evolution -- Malocclusion Treatment Planning via PointNet based Spatial Transformation Network -- Simulation of Brain Resection for Cavity Segmentation Using Self-Supervised and Semi-Supervised Learning -- Local Contractive Registration for Quantification of Tissue Shrinkage in Assessment of Microwave Ablation -- Reinforcement Learning of Musculoskeletal Control from Functional Simulations -- Image Registration -- MvMM-RegNet: A new image registration framework based on multivariate mixture model and neural network estimation -- Database Annotation with few Examples: An Atlas-based Framework using Diffeomorphic Registration of 3D trees -- Pair-wise and Group-wise Deformation Consistency in Deep Registration Network -- Semantic Hierarchy Guided Registration Networks for Intra-Subject Pulmonary CT Image Alignment -- Highly accurate and memory efficient unsupervised learning-based discrete CT registration using 2.5D displacement search -- Unsupervised Learning Model for Registration of Multi-Phase Ultra-Widefield Fluorescein Angiography -- Large DeformationDiffeomorphic Image Registration with Laplacian Pyramid Networks -- Adversarial Uni- and Multi-modal Stream Networks for Multimodal Image Registration -- Cross-Modality Multi-Atlas Segmentation Using Deep Neural Networks -- Longitudinal Image Registration with Temporal-order and Subject-specificity Discrimination -- Flexible Bayesian Modelling for Nonlinear Image Registration -- Are Registration Uncertainty and Error Monotonically Associated? -- MR-to-US registration using multiclass segmentation of hepatic vasculature with a reduced 3D U-Net -- Detecting Pancreatic Ductal Adenocarcinoma in Multi-phase CT Scans via Alignment Ensemble -- Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRI -- Fluid registration between lung CT and stationary chest tomosynthesis images -- Anatomical Data Augmentation via Fluid-based Image Registration -- Generliazing Spatial Transformers to Projective Geometry with Applications to 2D/3D Registration -- Instrumentation and Surgical Phase Detection -- TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks -- Surgical Video Motion Magnification with Suppression of Instrument Artefacts -- Recognition of Instrument-Tissue Interactions in Endoscopic Videos via Action Triplets -- AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation -- Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery -- Navigation and Visualization -- Can a hand-held navigation device reduce cognitive load? A user-centered approach evaluated by 18 surgeons -- Symmetric Dilated Convolution for Surgical Gesture Recognition -- Deep Selection: A Fully Supervised Camera Selection Network for Surgery Recordings -- Interacting with Medical Volume Data in Projective Augmented Reality -- VR Simulation of Novel Hands-free Interaction Concepts for Surgical Robotic Visualization Systems -- Spatially-Aware Displays for Computer Assisted Interventions -- Ultrasound Imaging.-Sensorless Freehand 3D Ultrasound Reconstruction via Deep Contextual Learning -- Ultra2Speech - A Deep Learning Framework for Formant Frequency Estimation and Tracking from Ultrasound Tongue Images -- Ultrasound Video Summarization using Deep Reinforcement Learning -- Predicting obstructive hydronephrosis based on ultrasound alone -- Semi-Supervised Training of Optical Flow Convolutional Neural Networks in Ultrasound Elastography -- Three-dimensional thyroid assessment from untracked 2D ultrasound clips -- Complex Cancer Detector: Complex Neural Networks on Non-stationary Time Series for Guiding Systematic Prostate Biopsy -- Self-supervised Contrastive Video-Speech Representation Learning for Ultrasound -- Directing Ultrasound Probe Placement for Image Guided Prostate Radiotherapy -- Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound -- Contrastive Rendering for Ultrasound Image Segmentation -- An Unsupervised Approach to Ultrasound Elastography with End-to-end Strain Regularisation -- Automatic Probe Movement Guidance for Freehand Obstetric Ultrasound -- Video Image Analysis -- ISINet: An Instance-Based Approach for Surgical Instrument Segmentation -- Reliable Liver Fibrosis Assessment from Ultrasound using Global Hetero-Image Fusion and View-Specific Parameterization -- Toward Rapid Stroke Diagnosis with Multimodal Deep Learning -- Learning and Reasoning with the Graph Structure Representation in Robotic Surgery -- Vision-based Estimation of MDS-UPDRS Gait Scores for Assessing Parkinson's Disease Motor Severity -- Searching for Efficient Architecture for Instrument Segmentation in Robotic Surgery -- Unsupervised Surgical Instrument Segmentation via Anchor Generation and Semantic Diffusion -- Towards Accurate and Interpretable Surgical Skill Assessment: A Video-Based Method Incorporating Recognized Surgical Gestures and Skill Levels -- Learning Motion Flows for Semi-supervised Instrument Segmentation from Robotic Surgical Video -- Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification -- Synthetic and Real Inputs for Tool Segmentation in Robotic Surgery -- Perfusion Quantification from Endoscopic Videos: Learning to Read Tumour Signatures -- Asynchronous in Parallel Detection and Tracking (AIPDT): Real-time Robust Polyp Detection -- OfGAN: Realistic Rendition of Synthetic Colonoscopy Videos -- Two-Stream Deep Feature Modelling for Automated Video Endoscopy Data Analysis -- Rethinking Anticipation Tasks: Uncertainty-aware Anticipation of Sparse Surgical Instrument Usage for Context-aware Assistance -- Deep Placental Vessel Segmentation for Fetoscopic Mosaicking -- Deep Multi-View Stereo for Dense 3D Reconstruction from Monocular Endoscopic Video -- Endo-Sim2Real: Consistency learning-based domain adaptation for instrument segmentation. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part IV Tipo de documento: documento electrónico Autores: Martel, Anne L., ; Abolmaesumi, Purang, ; Stoyanov, Danail, ; Mateus, Diana, ; Zuluaga, Maria A., ; Zhou, S. Kevin, ; Racoceanu, Daniel, ; Joskowicz, Leo, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2020 Número de páginas: XXXVII, 831 p. 22 ilustraciones ISBN/ISSN/DL: 978-3-030-59719-1 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Palabras clave: Visión por computador Inteligencia artificial Ciencias sociales Sistemas de reconocimiento de patrones Bioinformática Aplicación informática en ciencias sociales y del comportamiento Computadoras y Educación Reconocimiento de patrones automatizado BiologÃa Computacional y de Sistemas Clasificación: Resumen: El conjunto de siete volúmenes LNCS 12261, 12262, 12263, 12264, 12265, 12266 y 12267 constituye las actas arbitradas de la 23.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2020, celebrada en Lima, Perú, en octubre. 2020. La conferencia se realizó de manera virtual debido a la pandemia de COVID-19. Los 542 artÃculos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1809 presentaciones en un proceso de revisión doble ciego. Los artÃculos están organizados en las siguientes secciones temáticas: Parte I: metodologÃas de aprendizaje automático Parte II: reconstrucción de imágenes; predicción y diagnóstico; métodos y reconstrucción entre dominios; adaptación de dominio; aplicaciones de aprendizaje automático; redes generativas adversarias Parte III: aplicaciones CAI; registro de imagen; instrumentación y detección de fase quirúrgica; navegación y visualización; imágenes por ultrasonido; análisis de imágenes de vÃdeo Parte IV: segmentación; modelos de formas y detección de puntos de referencia Parte V: imágenes biológicas, ópticas y microscópicas; segmentación celular y normalización de tinciones; análisis de imágenes histopatológicas; oftalmologÃa Parte VI: angiografÃa y análisis de vasos; imágenes de mama; colonoscopia; dermatologÃa; imágenes fetales; imágenes del corazón y los pulmones; imágenes musculoesqueléticas Parte VI: desarrollo cerebral y atlas; DWI y tractografÃa; redes cerebrales funcionales; neuroimagen; TomografÃa de emisión de positrones. Nota de contenido: Segmentation -- Deep Volumetric Universal Lesion Detection using Light-Weight Pseudo 3D Convolution and Surface Point Regression -- DeScarGAN: Disease-Specific Anomaly Detection with Weak Supervision -- KISEG: A Three-Stage Segmentation Framework for Multi-level Acceleration of Chest CT Scans from COVID-19 Patients -- CircleNet: Anchor-free Glomerulus Detection with Circle Representation -- Weakly supervised one-stage vision and language disease detection using large scale pneumonia and pneumothorax studies -- Diagnostic Assessment of Deep Learning Algorithms for Detection and Segmentation of Lesion in Mammographic images -- Efficient and Phase-aware Video Super-resolution for Cardiac MRI -- ImageCHD: A 3D Computed Tomography Image Dataset for Classification of Congenital Heart Disease -- Deep Generative Model-based Quality Control for Cardiac MRI Segmentation -- DeU-Net: Deformable U-Net for 3D Cardiac MRI Video Segmentation -- Learning Directional Feature Maps for Cardiac MRI Segmentation -- Joint Left Atrial Segmentation and Scar Quantification Based on a DNN with Spatial Encoding and Shape Attention -- XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms -- TexNet: Texture Loss Based Network for Gastric Antrum Segmentation in Ultrasound -- Multi-organ Segmentation via Co-training Weight-averaged Models from Few-organ Datasets -- Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling -- Pay More Attention to Discontinuity for Medical Image Segmentation -- Learning 3D Features with 2D CNNs via Surface Projection for CT Volume Segmentation -- Deep Class-specific Affinity-Guided Convolutional Network for Multimodal Unpaired Image Segmentation -- Memory-efficient Automatic Kidney and Tumor Segmentation Based on Non-local Context Guided 3D U-Net -- Deep Small Bowel Segmentation with Cylindrical Topological Constraints -- Learning Sample-adaptive Intensity Lookup Table for Brain Tumor Segmentation -- Superpixel-Guided Label Softening for Medical Image Segmentation -- Revisiting Rubik's Cube: Self-supervised Learning with Volume-wise Transformation for 3D Medical Image Segmentation -- Robust Medical Image Segmentation from Non-expert Annotations with Tri-network -- Robust Fusion of Probability Maps -- Calibrated Surrogate Maximization of Dice -- Uncertainty-Guided Efficient Interactive Refinement of Fetal Brain Segmentation from Stacks of MRI Slices -- Widening the focus: biomedical image segmentation challenges and the underestimated role of patch sampling and inference strategies -- Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data -- Unsupervised Learning for CT Image Segmentation via Adversarial Redrawing -- Deep Active Contour Network for Medical Image Segmentation -- Learning Crisp Edge Detector Using Logical Refinement Network -- Defending Deep Learning-based Biomedical Image Segmentation from Adversarial Attacks: A Low-cost Frequency Refinement Approach -- CNN-GCN Aggregation Enabled Boundary Regression for Biomedical Image Segmentation -- KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representations -- LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation -- INSIDE: Steering Spatial Attention with Non-Imaging Information in CNNs -- SiamParseNet: Joint Body Parsing and Label Propagation in Infant Movement Videos -- Orchestrating Medical Image Compression and Remote Segmentation Networks -- Bounding Maps for Universal Lesion Detection -- Multimodal Priors Guided Segmentation of Liver Lesions in MRI Using Mutual Information Based Graph Co-Attention Networks -- Mt-UcGAN: Multi-task uncertainty-constrained GAN for joint segmentation, quantification and uncertainty estimation of renal tumors on CT -- Weakly Supervised Deep Learning for Breast Cancer Segmentation with Coarse Annotations -- Multi-phase and Multi-level Selective Feature Fusion for Automated Pancreas Segmentation from CT Images -- Asymmetrical Multi-Task Attention U-Net for the Segmentation of Prostate Bed in CT Image -- Learning High-Resolution and Efficient Non-local Features for Brain Glioma Segmentation in MR Images -- Robust Pancreatic Ductal Adenocarcinoma Segmentation with Multi-Institutional Multi-Phase Partially-Annotated CT Scans -- Generation of Annotated Brain Tumor MRIs with Tumor-induced Tissue Deformations for Training and Assessment of Neural Networks -- E2Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans -- Universal loss reweighting to balance lesion size inequality in 3D medical image segmentation -- Brain tumor segmentation with missing modalities via latent multi-source correlation representation -- Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices -- Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI -- AlignShift: Bridging the Gap of Imaging Thickness in 3D Anisotropic Volumes -- One Click Lesion RECIST Measurement and Segmentation on CT Scans -- Automated Detection of Cortical Lesions in Multiple Sclerosis Patients with 7T MRI -- Deep Attentive Panoptic Model for Prostate Cancer Detection Using Biparametric MRI Scans -- Shape Models and Landmark Detection -- Graph Reasoning and Shape Constraints for Cardiac Segmentation in Congenital Heart Defect -- Nonlinear Regression on Manifolds for Shape Analysis using Intrinsic Bézier Splines -- Self-Supervised Discovery of Anatomical Shape Landmarks -- Shape Mask Generator: Learning to Refine Shape Priors for Segmenting Overlapping Cervical Cytoplasms -- Prostate motion modelling using biomechanically-trained deep neural networks on unstructured nodes -- Deep Learning Assisted Automatic Intra-operative 3D Aortic Deformation Reconstruction -- Landmarks Detection with Anatomical Constraints for Total Hip Arthroplasty Preoperative Measurements -- Instantiation-Net: 3D Mesh Reconstruction from Single 2D Image for Right Ventricle -- Miss the point: Targeted adversarial attack on multiple landmark detection -- Automatic Tooth Segmentation and Dense Correspondence of 3D Dental Model -- Move over there: One-click deformation correction for image fusion during endovascular aortic repair -- Non-Rigid Volume to Surface Registration using a Data-Driven Biomechanical Model -- Deformation Aware Augmented Reality for Craniotomy using 3D/2D Non-rigid Registration of Cortical Vessels -- Skip-StyleGAN: Skip-connected Generative Adversarial Networks for Generating 3D Rendered Image of Hand Bone Complex -- Dynamic multi-object Gaussian process models -- A kernelized multi-level localization method for flexible shape modeling with few training data -- Unsupervised Learning and Statistical Shape Modeling of the Morphometry and Hemodynamics of Coarctation of the Aorta -- Convolutional Bayesian Models for Anatomical Landmarking on Multi-Dimensional Shapes -- SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation -- Multi-Task Dynamic Transformer Network for Concurrent Bone Segmentation and Large-Scale Landmark Localization with Dental CBCT -- Automatic Localization of Landmarks in Craniomaxillofacial CBCT Images using a Local Attention-based Graph Convolution Network. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part IV [documento electrónico] / Martel, Anne L., ; Abolmaesumi, Purang, ; Stoyanov, Danail, ; Mateus, Diana, ; Zuluaga, Maria A., ; Zhou, S. Kevin, ; Racoceanu, Daniel, ; Joskowicz, Leo, . - 1 ed. . - [s.l.] : Springer, 2020 . - XXXVII, 831 p. 22 ilustraciones.
ISBN : 978-3-030-59719-1
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Palabras clave: Visión por computador Inteligencia artificial Ciencias sociales Sistemas de reconocimiento de patrones Bioinformática Aplicación informática en ciencias sociales y del comportamiento Computadoras y Educación Reconocimiento de patrones automatizado BiologÃa Computacional y de Sistemas Clasificación: Resumen: El conjunto de siete volúmenes LNCS 12261, 12262, 12263, 12264, 12265, 12266 y 12267 constituye las actas arbitradas de la 23.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2020, celebrada en Lima, Perú, en octubre. 2020. La conferencia se realizó de manera virtual debido a la pandemia de COVID-19. Los 542 artÃculos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1809 presentaciones en un proceso de revisión doble ciego. Los artÃculos están organizados en las siguientes secciones temáticas: Parte I: metodologÃas de aprendizaje automático Parte II: reconstrucción de imágenes; predicción y diagnóstico; métodos y reconstrucción entre dominios; adaptación de dominio; aplicaciones de aprendizaje automático; redes generativas adversarias Parte III: aplicaciones CAI; registro de imagen; instrumentación y detección de fase quirúrgica; navegación y visualización; imágenes por ultrasonido; análisis de imágenes de vÃdeo Parte IV: segmentación; modelos de formas y detección de puntos de referencia Parte V: imágenes biológicas, ópticas y microscópicas; segmentación celular y normalización de tinciones; análisis de imágenes histopatológicas; oftalmologÃa Parte VI: angiografÃa y análisis de vasos; imágenes de mama; colonoscopia; dermatologÃa; imágenes fetales; imágenes del corazón y los pulmones; imágenes musculoesqueléticas Parte VI: desarrollo cerebral y atlas; DWI y tractografÃa; redes cerebrales funcionales; neuroimagen; TomografÃa de emisión de positrones. Nota de contenido: Segmentation -- Deep Volumetric Universal Lesion Detection using Light-Weight Pseudo 3D Convolution and Surface Point Regression -- DeScarGAN: Disease-Specific Anomaly Detection with Weak Supervision -- KISEG: A Three-Stage Segmentation Framework for Multi-level Acceleration of Chest CT Scans from COVID-19 Patients -- CircleNet: Anchor-free Glomerulus Detection with Circle Representation -- Weakly supervised one-stage vision and language disease detection using large scale pneumonia and pneumothorax studies -- Diagnostic Assessment of Deep Learning Algorithms for Detection and Segmentation of Lesion in Mammographic images -- Efficient and Phase-aware Video Super-resolution for Cardiac MRI -- ImageCHD: A 3D Computed Tomography Image Dataset for Classification of Congenital Heart Disease -- Deep Generative Model-based Quality Control for Cardiac MRI Segmentation -- DeU-Net: Deformable U-Net for 3D Cardiac MRI Video Segmentation -- Learning Directional Feature Maps for Cardiac MRI Segmentation -- Joint Left Atrial Segmentation and Scar Quantification Based on a DNN with Spatial Encoding and Shape Attention -- XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms -- TexNet: Texture Loss Based Network for Gastric Antrum Segmentation in Ultrasound -- Multi-organ Segmentation via Co-training Weight-averaged Models from Few-organ Datasets -- Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling -- Pay More Attention to Discontinuity for Medical Image Segmentation -- Learning 3D Features with 2D CNNs via Surface Projection for CT Volume Segmentation -- Deep Class-specific Affinity-Guided Convolutional Network for Multimodal Unpaired Image Segmentation -- Memory-efficient Automatic Kidney and Tumor Segmentation Based on Non-local Context Guided 3D U-Net -- Deep Small Bowel Segmentation with Cylindrical Topological Constraints -- Learning Sample-adaptive Intensity Lookup Table for Brain Tumor Segmentation -- Superpixel-Guided Label Softening for Medical Image Segmentation -- Revisiting Rubik's Cube: Self-supervised Learning with Volume-wise Transformation for 3D Medical Image Segmentation -- Robust Medical Image Segmentation from Non-expert Annotations with Tri-network -- Robust Fusion of Probability Maps -- Calibrated Surrogate Maximization of Dice -- Uncertainty-Guided Efficient Interactive Refinement of Fetal Brain Segmentation from Stacks of MRI Slices -- Widening the focus: biomedical image segmentation challenges and the underestimated role of patch sampling and inference strategies -- Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data -- Unsupervised Learning for CT Image Segmentation via Adversarial Redrawing -- Deep Active Contour Network for Medical Image Segmentation -- Learning Crisp Edge Detector Using Logical Refinement Network -- Defending Deep Learning-based Biomedical Image Segmentation from Adversarial Attacks: A Low-cost Frequency Refinement Approach -- CNN-GCN Aggregation Enabled Boundary Regression for Biomedical Image Segmentation -- KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representations -- LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation -- INSIDE: Steering Spatial Attention with Non-Imaging Information in CNNs -- SiamParseNet: Joint Body Parsing and Label Propagation in Infant Movement Videos -- Orchestrating Medical Image Compression and Remote Segmentation Networks -- Bounding Maps for Universal Lesion Detection -- Multimodal Priors Guided Segmentation of Liver Lesions in MRI Using Mutual Information Based Graph Co-Attention Networks -- Mt-UcGAN: Multi-task uncertainty-constrained GAN for joint segmentation, quantification and uncertainty estimation of renal tumors on CT -- Weakly Supervised Deep Learning for Breast Cancer Segmentation with Coarse Annotations -- Multi-phase and Multi-level Selective Feature Fusion for Automated Pancreas Segmentation from CT Images -- Asymmetrical Multi-Task Attention U-Net for the Segmentation of Prostate Bed in CT Image -- Learning High-Resolution and Efficient Non-local Features for Brain Glioma Segmentation in MR Images -- Robust Pancreatic Ductal Adenocarcinoma Segmentation with Multi-Institutional Multi-Phase Partially-Annotated CT Scans -- Generation of Annotated Brain Tumor MRIs with Tumor-induced Tissue Deformations for Training and Assessment of Neural Networks -- E2Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans -- Universal loss reweighting to balance lesion size inequality in 3D medical image segmentation -- Brain tumor segmentation with missing modalities via latent multi-source correlation representation -- Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices -- Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI -- AlignShift: Bridging the Gap of Imaging Thickness in 3D Anisotropic Volumes -- One Click Lesion RECIST Measurement and Segmentation on CT Scans -- Automated Detection of Cortical Lesions in Multiple Sclerosis Patients with 7T MRI -- Deep Attentive Panoptic Model for Prostate Cancer Detection Using Biparametric MRI Scans -- Shape Models and Landmark Detection -- Graph Reasoning and Shape Constraints for Cardiac Segmentation in Congenital Heart Defect -- Nonlinear Regression on Manifolds for Shape Analysis using Intrinsic Bézier Splines -- Self-Supervised Discovery of Anatomical Shape Landmarks -- Shape Mask Generator: Learning to Refine Shape Priors for Segmenting Overlapping Cervical Cytoplasms -- Prostate motion modelling using biomechanically-trained deep neural networks on unstructured nodes -- Deep Learning Assisted Automatic Intra-operative 3D Aortic Deformation Reconstruction -- Landmarks Detection with Anatomical Constraints for Total Hip Arthroplasty Preoperative Measurements -- Instantiation-Net: 3D Mesh Reconstruction from Single 2D Image for Right Ventricle -- Miss the point: Targeted adversarial attack on multiple landmark detection -- Automatic Tooth Segmentation and Dense Correspondence of 3D Dental Model -- Move over there: One-click deformation correction for image fusion during endovascular aortic repair -- Non-Rigid Volume to Surface Registration using a Data-Driven Biomechanical Model -- Deformation Aware Augmented Reality for Craniotomy using 3D/2D Non-rigid Registration of Cortical Vessels -- Skip-StyleGAN: Skip-connected Generative Adversarial Networks for Generating 3D Rendered Image of Hand Bone Complex -- Dynamic multi-object Gaussian process models -- A kernelized multi-level localization method for flexible shape modeling with few training data -- Unsupervised Learning and Statistical Shape Modeling of the Morphometry and Hemodynamics of Coarctation of the Aorta -- Convolutional Bayesian Models for Anatomical Landmarking on Multi-Dimensional Shapes -- SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation -- Multi-Task Dynamic Transformer Network for Concurrent Bone Segmentation and Large-Scale Landmark Localization with Dental CBCT -- Automatic Localization of Landmarks in Craniomaxillofacial CBCT Images using a Local Attention-based Graph Convolution Network. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part V Tipo de documento: documento electrónico Autores: Martel, Anne L., ; Abolmaesumi, Purang, ; Stoyanov, Danail, ; Mateus, Diana, ; Zuluaga, Maria A., ; Zhou, S. Kevin, ; Racoceanu, Daniel, ; Joskowicz, Leo, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2020 Número de páginas: XXXVII, 811 p. 11 ilustraciones ISBN/ISSN/DL: 978-3-030-59722-1 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Palabras clave: Visión por computador Inteligencia artificial Sistemas de reconocimiento de patrones Ciencias sociales Reconocimiento de patrones automatizado Aplicación informática en ciencias sociales y del comportamiento Computadoras y Educación Clasificación: Resumen: El conjunto de siete volúmenes LNCS 12261, 12262, 12263, 12264, 12265, 12266 y 12267 constituye las actas arbitradas de la 23.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2020, celebrada en Lima, Perú, en octubre. 2020. La conferencia se realizó de manera virtual debido a la pandemia de COVID-19. Los 542 artÃculos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1809 presentaciones en un proceso de revisión doble ciego. Los artÃculos están organizados en las siguientes secciones temáticas: Parte I: metodologÃas de aprendizaje automático Parte II: reconstrucción de imágenes; predicción y diagnóstico; métodos y reconstrucción entre dominios; adaptación de dominio; aplicaciones de aprendizaje automático; redes generativas adversarias Parte III: aplicaciones CAI; registro de imagen; instrumentación y detección de fase quirúrgica; navegación y visualización; imágenes por ultrasonido; análisis de imágenes de vÃdeo Parte IV: segmentación; modelos de formas y detección de puntos de referencia Parte V: imágenes biológicas, ópticas y microscópicas; segmentación celular y normalización de tinciones; análisis de imágenes histopatológicas; oftalmologÃa Parte VI: angiografÃa y análisis de vasos; imágenes de mama; colonoscopia; dermatologÃa; imágenes fetales; imágenes del corazón y los pulmones; imágenes musculoesqueléticas Parte VI: desarrollo cerebral y atlas; DWI y tractografÃa; redes cerebrales funcionales; neuroimagen; TomografÃa de emisión de positrones. Nota de contenido: Biological, Optical, Microscopic Imaging -- Channel Embedding for Informative Protein Identification from Highly Multiplexed Images -- Demixing Calcium Imaging Data in C. elegans via Deformable Non-negative Matrix Factorization -- Automated Measurements of Key Morphological Features of Human Embryos for IVF -- A Novel Approach to Tongue Standardization and Feature Extraction -- Patch-based Non-Local Bayesian Networks for Blind Confocal Microscopy Denoising -- Attention-guided Quality Assessment for Automated Cryo-EM Grid Screening -- MitoEM Dataset: Large-scale 3D Mitochondria Instance Segmentation from EM Images -- Learning Guided Electron Microscopy with Active Acquisition -- Neuronal Subcompartment Classification and Merge Error Correction -- Microtubule Tracking in Electron Microscopy Volumes -- Leveraging Tools from Autonomous Navigation for Rapid, Robust Neuron Connectivity -- Statistical Atlas of C.elegans Neurons -- Probabilistic Segmentation and Labeling of C. elegans Neurons -- Segmenting Continuous but Sparsely-Labeled Structures in Super-Resolution Microscopy Using Perceptual Grouping -- DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging -- Isotropic Reconstruction of 3D EM Images with Unsupervised Degradation Learning -- Background and illumination correction for time-lapse microscopy data with correlated foreground -- Joint Spatial-Wavelet Dual-Stream Network for Super-Resolution -- Towards Neuron Segmentation from Macaque Brain Images: A Weakly Supervised Approach -- 3D Reconstruction and Segmentation of Dissection Photographs for MRI-free Neuropathology -- DistNet: Deep Tracking by displacement regression: application to bacteria growing in the Mother Machine -- A weakly supervised deep learning approach for detecting malaria and sickle cell anemia in blood films -- Imaging Scattering Characteristics of Tissue in Transmitted Microscopy -- Attention based multiple instance learning for classification of blood cell disorders -- A generative modeling approach for interpreting population-level variability in brain structure -- Processing-Aware Real-Time Rendering for Optimized Tissue Visualization in Intraoperative 4D OCT -- Cell Segmentation and Stain Normalization -- Boundary-assisted Region Proposal Networks for Nucleus Segmentation -- BCData: A Large-Scale Dataset and Benchmark for Cell Detection and Counting -- Weakly-Supervised Nucleus Segmentation Based on Point Annotations: A Coarse-to-Fine Self-Stimulated Learning Strategy -- Structure Preserving Stain Normalization of Histopathology Images Using Self Supervised Semantic Guidance -- A Novel Loss Calibration Strategy for Object Detection Networks Training on Sparsely Annotated Pathological Datasets -- Histopathological Stain Transfer Using Style Transfer Network With Adversarial Loss -- Instance-aware Self-supervised Learning for Nuclei Segmentation -- StyPath: Style-Transfer Data Augmentation For Robust Histology Image Classification -- Multimarginal Wasserstein Barycenter for Stain Normalization and Augmentation -- Corruption-Robust Enhancement of Deep Neural Networks for Classification of Peripheral Blood Smear Images -- Multi-Field of View Aggregation and Context Encoding for Single-Stage Nucleus Recognition -- Self-Supervised Nuclei Segmentation in Histopathological Images Using Attention -- FocusLiteNN: High Efficiency Focus Quality Assessment for Digital Pathology -- Histopathology Image Analysis -- Pairwise Relation Learning for Semi-supervised Gland Segmentation -- Ranking-Based Survival Prediction on Histopathological Whole-Slide Images -- Renal Cell Carcinoma Detection and Subtyping with Minimal Point-Based Annotation in Whole-Slide Images -- Censoring-Aware Deep Ordinal Regression for Survival Prediction from Pathological Images -- Tracing Diagnosis Paths on Histopathology WSIs for Diagnostically Relevant Case Recommendation -- Weakly supervised multiple instance learning histopathological tumor segmentation -- Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer -- Microscopic fine-grained instance classification through deep attention -- A Deformable CRF Model for Histopathology Whole-slide Image Classification -- Deep Active Learning for Breast Cancer Segmentation on Immunohistochemistry Images -- Multiple Instance Learning with Center Embeddings for Histopathology Classification -- Graph Attention Multi-instance Learning for Accurate Colorectal Cancer Staging -- Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment -- Modeling Histological Patterns for Differential Diagnosis of Atypical Breast Lesions -- Foveation for Segmentation of Mega-pixel Histology Images -- Multimodal Latent Semantic Alignment for Automated Prostate Tissue Classification and Retrieval -- Opthalmology -- GREEN: a Graph REsidual rE-ranking Network for Grading Diabetic Retinopathy -- Combining Fundus Images and Fluorescein Angiographyfor Artery/Vein Classification Using the Hierarchical Vessel Graph Network -- Adaptive Dictionary Learning Based Multimodal Branch Retinal Vein Occlusion Fusion -- TR-GAN: Topology Ranking GAN with Triplet Loss for Retinal Artery/Vein Classification -- DeepGF: Glaucoma Forecast Using Sequential Fundus Images -- Single-Shot Retinal Image Enhancement Using Deep Image Prior -- Robust Layer Segmentation against Complex Retinal Abnormalities for en face OCTA Generation -- Anterior Segment Eye Lesion Segmentation with Advanced Fusion Strategies and Auxiliary Tasks -- Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images -- Disentanglement Network for Unpsupervised Speckle Reduction of Optical Coherence Tomography Images -- Positive-Aware Lesion Detection Network with Cross-scale Feature Pyramid for OCT Images -- Retinal Layer Segmentation Reformulated as OCT Language Processing -- Reconstruction and Quantification of 3D Iris Surface for Angle-Closure Glaucoma Detection in Anterior Segment OCT -- Open-Appositional-Synechial Anterior Chamber Angle Classification in AS-OCT Sequences -- A Macro-Micro Weakly-supervised Framework for AS-OCT Tissue Segmentation -- Macular Hole and Cystoid Macular Edema Joint Segmentation by Two-Stage Network and Entropy Minimization -- Retinal Nerve Fiber Layer Defect Detection With Position Guidance -- An Elastic Interaction Based-Loss Function for Medical Image Segmentation -- Retinal Image Segmentation with a Structure-Texture Demixing Network -- BEFD: Boundary Enhancement and Feature Denoising for Vessel Segmentation -- Boosting Connectivity in Retinal Vessel Segmentation via a Recursive Semantics-Guided Network -- RVSeg-Net: an Efficient Feature Pyramid Cascade Network for Retinal Vessel Segmentation-. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part V [documento electrónico] / Martel, Anne L., ; Abolmaesumi, Purang, ; Stoyanov, Danail, ; Mateus, Diana, ; Zuluaga, Maria A., ; Zhou, S. Kevin, ; Racoceanu, Daniel, ; Joskowicz, Leo, . - 1 ed. . - [s.l.] : Springer, 2020 . - XXXVII, 811 p. 11 ilustraciones.
ISBN : 978-3-030-59722-1
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Palabras clave: Visión por computador Inteligencia artificial Sistemas de reconocimiento de patrones Ciencias sociales Reconocimiento de patrones automatizado Aplicación informática en ciencias sociales y del comportamiento Computadoras y Educación Clasificación: Resumen: El conjunto de siete volúmenes LNCS 12261, 12262, 12263, 12264, 12265, 12266 y 12267 constituye las actas arbitradas de la 23.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2020, celebrada en Lima, Perú, en octubre. 2020. La conferencia se realizó de manera virtual debido a la pandemia de COVID-19. Los 542 artÃculos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1809 presentaciones en un proceso de revisión doble ciego. Los artÃculos están organizados en las siguientes secciones temáticas: Parte I: metodologÃas de aprendizaje automático Parte II: reconstrucción de imágenes; predicción y diagnóstico; métodos y reconstrucción entre dominios; adaptación de dominio; aplicaciones de aprendizaje automático; redes generativas adversarias Parte III: aplicaciones CAI; registro de imagen; instrumentación y detección de fase quirúrgica; navegación y visualización; imágenes por ultrasonido; análisis de imágenes de vÃdeo Parte IV: segmentación; modelos de formas y detección de puntos de referencia Parte V: imágenes biológicas, ópticas y microscópicas; segmentación celular y normalización de tinciones; análisis de imágenes histopatológicas; oftalmologÃa Parte VI: angiografÃa y análisis de vasos; imágenes de mama; colonoscopia; dermatologÃa; imágenes fetales; imágenes del corazón y los pulmones; imágenes musculoesqueléticas Parte VI: desarrollo cerebral y atlas; DWI y tractografÃa; redes cerebrales funcionales; neuroimagen; TomografÃa de emisión de positrones. Nota de contenido: Biological, Optical, Microscopic Imaging -- Channel Embedding for Informative Protein Identification from Highly Multiplexed Images -- Demixing Calcium Imaging Data in C. elegans via Deformable Non-negative Matrix Factorization -- Automated Measurements of Key Morphological Features of Human Embryos for IVF -- A Novel Approach to Tongue Standardization and Feature Extraction -- Patch-based Non-Local Bayesian Networks for Blind Confocal Microscopy Denoising -- Attention-guided Quality Assessment for Automated Cryo-EM Grid Screening -- MitoEM Dataset: Large-scale 3D Mitochondria Instance Segmentation from EM Images -- Learning Guided Electron Microscopy with Active Acquisition -- Neuronal Subcompartment Classification and Merge Error Correction -- Microtubule Tracking in Electron Microscopy Volumes -- Leveraging Tools from Autonomous Navigation for Rapid, Robust Neuron Connectivity -- Statistical Atlas of C.elegans Neurons -- Probabilistic Segmentation and Labeling of C. elegans Neurons -- Segmenting Continuous but Sparsely-Labeled Structures in Super-Resolution Microscopy Using Perceptual Grouping -- DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging -- Isotropic Reconstruction of 3D EM Images with Unsupervised Degradation Learning -- Background and illumination correction for time-lapse microscopy data with correlated foreground -- Joint Spatial-Wavelet Dual-Stream Network for Super-Resolution -- Towards Neuron Segmentation from Macaque Brain Images: A Weakly Supervised Approach -- 3D Reconstruction and Segmentation of Dissection Photographs for MRI-free Neuropathology -- DistNet: Deep Tracking by displacement regression: application to bacteria growing in the Mother Machine -- A weakly supervised deep learning approach for detecting malaria and sickle cell anemia in blood films -- Imaging Scattering Characteristics of Tissue in Transmitted Microscopy -- Attention based multiple instance learning for classification of blood cell disorders -- A generative modeling approach for interpreting population-level variability in brain structure -- Processing-Aware Real-Time Rendering for Optimized Tissue Visualization in Intraoperative 4D OCT -- Cell Segmentation and Stain Normalization -- Boundary-assisted Region Proposal Networks for Nucleus Segmentation -- BCData: A Large-Scale Dataset and Benchmark for Cell Detection and Counting -- Weakly-Supervised Nucleus Segmentation Based on Point Annotations: A Coarse-to-Fine Self-Stimulated Learning Strategy -- Structure Preserving Stain Normalization of Histopathology Images Using Self Supervised Semantic Guidance -- A Novel Loss Calibration Strategy for Object Detection Networks Training on Sparsely Annotated Pathological Datasets -- Histopathological Stain Transfer Using Style Transfer Network With Adversarial Loss -- Instance-aware Self-supervised Learning for Nuclei Segmentation -- StyPath: Style-Transfer Data Augmentation For Robust Histology Image Classification -- Multimarginal Wasserstein Barycenter for Stain Normalization and Augmentation -- Corruption-Robust Enhancement of Deep Neural Networks for Classification of Peripheral Blood Smear Images -- Multi-Field of View Aggregation and Context Encoding for Single-Stage Nucleus Recognition -- Self-Supervised Nuclei Segmentation in Histopathological Images Using Attention -- FocusLiteNN: High Efficiency Focus Quality Assessment for Digital Pathology -- Histopathology Image Analysis -- Pairwise Relation Learning for Semi-supervised Gland Segmentation -- Ranking-Based Survival Prediction on Histopathological Whole-Slide Images -- Renal Cell Carcinoma Detection and Subtyping with Minimal Point-Based Annotation in Whole-Slide Images -- Censoring-Aware Deep Ordinal Regression for Survival Prediction from Pathological Images -- Tracing Diagnosis Paths on Histopathology WSIs for Diagnostically Relevant Case Recommendation -- Weakly supervised multiple instance learning histopathological tumor segmentation -- Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer -- Microscopic fine-grained instance classification through deep attention -- A Deformable CRF Model for Histopathology Whole-slide Image Classification -- Deep Active Learning for Breast Cancer Segmentation on Immunohistochemistry Images -- Multiple Instance Learning with Center Embeddings for Histopathology Classification -- Graph Attention Multi-instance Learning for Accurate Colorectal Cancer Staging -- Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment -- Modeling Histological Patterns for Differential Diagnosis of Atypical Breast Lesions -- Foveation for Segmentation of Mega-pixel Histology Images -- Multimodal Latent Semantic Alignment for Automated Prostate Tissue Classification and Retrieval -- Opthalmology -- GREEN: a Graph REsidual rE-ranking Network for Grading Diabetic Retinopathy -- Combining Fundus Images and Fluorescein Angiographyfor Artery/Vein Classification Using the Hierarchical Vessel Graph Network -- Adaptive Dictionary Learning Based Multimodal Branch Retinal Vein Occlusion Fusion -- TR-GAN: Topology Ranking GAN with Triplet Loss for Retinal Artery/Vein Classification -- DeepGF: Glaucoma Forecast Using Sequential Fundus Images -- Single-Shot Retinal Image Enhancement Using Deep Image Prior -- Robust Layer Segmentation against Complex Retinal Abnormalities for en face OCTA Generation -- Anterior Segment Eye Lesion Segmentation with Advanced Fusion Strategies and Auxiliary Tasks -- Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images -- Disentanglement Network for Unpsupervised Speckle Reduction of Optical Coherence Tomography Images -- Positive-Aware Lesion Detection Network with Cross-scale Feature Pyramid for OCT Images -- Retinal Layer Segmentation Reformulated as OCT Language Processing -- Reconstruction and Quantification of 3D Iris Surface for Angle-Closure Glaucoma Detection in Anterior Segment OCT -- Open-Appositional-Synechial Anterior Chamber Angle Classification in AS-OCT Sequences -- A Macro-Micro Weakly-supervised Framework for AS-OCT Tissue Segmentation -- Macular Hole and Cystoid Macular Edema Joint Segmentation by Two-Stage Network and Entropy Minimization -- Retinal Nerve Fiber Layer Defect Detection With Position Guidance -- An Elastic Interaction Based-Loss Function for Medical Image Segmentation -- Retinal Image Segmentation with a Structure-Texture Demixing Network -- BEFD: Boundary Enhancement and Feature Denoising for Vessel Segmentation -- Boosting Connectivity in Retinal Vessel Segmentation via a Recursive Semantics-Guided Network -- RVSeg-Net: an Efficient Feature Pyramid Cascade Network for Retinal Vessel Segmentation-. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
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