Título :	24th International Conference, Strasbourg, France, September 27 – October 1, 2021, Proceedings, Part VII
Tipo de documento:	documento electrónico
Autores:	de Bruijne, Marleen, ; Cattin, Philippe C., ; Cotin, Stéphane, ; Padoy, Nicolas, ; Speidel, Stefanie, ; Zheng, Yefeng, ; Essert, Caroline,
Mención de edición:	1 ed.
Editorial:	[s.l.] : Springer
Fecha de publicación:	2021
Número de páginas:	XXXIX, 801 p. 277 ilustraciones, 258 ilustraciones en color.
ISBN/ISSN/DL:	978-3-030-87234-2
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  Bioinformática  Sistemas de reconocimiento de patrones  Informática Médica  Biología Computacional y de Sistemas  Reconocimiento de patrones automatizado  Informática de la Salud
Índice Dewey:	006.37 Visión artificial
Resumen:	El conjunto de ocho volúmenes LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907 y 12908 constituye las actas arbitradas de la 24.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2021, celebrada en Estrasburgo, Francia. en septiembre/octubre de 2021.* Los 531 artículos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1630 presentaciones en un proceso de revisión doble ciego. Los artículos están organizados en las siguientes secciones temáticas: Parte I: segmentación de imágenes Parte II: aprendizaje automático - aprendizaje autosupervisado; aprendizaje automático: aprendizaje semisupervisado; y aprendizaje automático: aprendizaje débilmente supervisado. Parte III: aprendizaje automático: avances en la teoría del aprendizaje automático; aprendizaje automático: modelos de atención; aprendizaje automático: adaptación de dominios; aprendizaje automático: aprendizaje federado; aprendizaje automático: interpretabilidad / explicabilidad; y aprendizaje automático - incertidumbre Parte IV: registro de imágenes; intervenciones y cirugía guiadas por imágenes; ciencia de datos quirúrgicos; planificación y simulación quirúrgica; análisis de habilidades quirúrgicas y flujo de trabajo; y visualización quirúrgica y realidad mixta, aumentada y virtual. Parte V: diagnóstico asistido por ordenador; integración de imágenes con biomarcadores sin imágenes; y predicción de resultados/enfermedades. Parte VI: reconstrucción de imágenes; aplicaciones clínicas - cardíacas; y aplicaciones clínicas - vasculares Parte VII: aplicaciones clínicas - abdomen; aplicaciones clínicas - mama; aplicaciones clínicas - dermatología; aplicaciones clínicas: imágenes fetales; aplicaciones clínicas - pulmón; aplicaciones clínicas - neuroimagen - desarrollo cerebral; aplicaciones clínicas - neuroimagen - DWI y tractografía; aplicaciones clínicas - neuroimagen - redes cerebrales funcionales; aplicaciones clínicas - neuroimagen - otras; y aplicaciones clínicas - oncología Parte VIII: aplicaciones clínicas - oftalmología; patología computacional (integrativa); modalidades - microscopía; modalidades - histopatología; y modalidades - ultrasonido *La conferencia se realizó de manera virtual.
Nota de contenido:	Clinical Applications – Abdomen -- Learning More for Free - A Multi Task Learning Approach for Improved Pathology Classification in Capsule Endoscopy -- Learning-based attenuation quantification in abdominal ultrasound -- Colorectal Polyp Classification from White-light Colonoscopy Images via Domain Alignment -- Non-invasive Assessment of Hepatic Venous Pressure Gradient (HVPG) Based on MR Flow Imaging and Computational Fluid Dynamics -- Deep-Cleansing: Deep-learning based Electronic Cleansing in Dual-energy CT Colonography -- Clinical Applications - Breast -- Interactive smoothing parameter optimization in DBT Reconstruction using Deep learning -- Synthesis of Contrast-enhanced Spectral Mammograms from Low-energy Mammograms Using cGAN-Based Synthesis Network -- Self-adversarial Learning for Detection of Clustered Microcalcifications in Mammograms -- Graph Transformers for Characterization and Interpretation of Surgical Margins -- Domain Generalization for Mammography Detection viaMulti-style and Multi-view Contrastive Learning -- Learned super resolution ultrasound for improved breast lesion characterization -- BI-RADS Classification of Calcification on Mammograms -- Supervised Contrastive Pre-Training for Mammographic Triage Screening Models -- Trainable summarization to improve breast tomosynthesis classification -- Clinical Applications - Dermatology -- Multi-level Relationship Capture Network for Automated Skin Lesion Recognition -- Culprit-Prune-Net: Efficient Continual Sequential Multi-Domain Learning with Application to Skin Lesion Classification -- End-to-end Ugly Duckling Sign Detection for Melanoma Identification with Transformers -- Automatic Severity Rating for Improved Psoriasis Treatment -- Clinical Applications - Fetal Imaging -- STRESS: Super-Resolution for Dynamic Fetal MRI using Self-Supervised Learning -- Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps -- EllipseNet: Anchor-Free Ellipse Detection for Automatic Cardiac Biometrics in Fetal Echocardiography -- AutoFB: Automating Fetal Biometry Estimation from Standard Ultrasound Planes -- Learning Spatiotemporal Probabilistic Atlas of Fetal Brains with Anatomically Constrained Registration Network -- Clinical Applications - Lung -- Leveraging Auxiliary Information from EMR for Weakly Supervised Pulmonary Nodule Detection -- M-SEAM-NAM: Multi-instance Self-supervised Equivalent Attention Mechanism with Neighborhood Affinity Module for Double Weakly Supervised Segmentation of COVID-19 -- Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs -- Beyond COVID-19 Diagnosis: Prognosis with Hierarchical Graph Representation Learning -- RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting -- Detecting when pre-trained nnU-Net models fail silently for Covid-19 lung lesion segmentation -- Perceptual Quality Assessment of Chest Radiograph -- Pristine annotations-based multi-modal trained artificial intelligence solution to triage chest X-Ray for COVID19 -- Determination of error in 3D CT to 2D fluoroscopy image registration for endobronchial guidance -- Chest Radiograph Disentanglement for COVID-19 Outcome Prediction -- Attention based CNN-LSTM Network for Pulmonary Embolism Prediction on Chest Computed Tomography Pulmonary Angiograms -- LuMiRa: An Integrated Lung Deformation Atlas and 3D-CNN model of Infiltrates for COVID-19 Prognosis -- Clinical Applications - Neuroimaging - Brain Development -- Multi-site Incremental Image Quality Assessment of Structural MRI via Consensus Adversarial Representation Adaptation -- Surface-Guided Image Fusion for Preserving Cortical Details in Human Brain Templates -- Longitudinal Correlation Analysis for Decoding Multi-Modal Brain Development -- ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities -- Covariate Correcting Networks for Identifying Associations between Socioeconomic Factors and Brain Outcomes inChildren -- Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non–Contrast CT Images -- Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma Segmentation -- Joint PVL Detection and Manual Ability Classification using Semi-Supervised Multi-task Learning -- Clinical Applications - Neuroimaging - DWI And Tractography -- Active Cortex Tractography -- Highly Reproducible Whole Brain Parcellation in Individuals via Voxel Annotation with Fiber Clusters -- Accurate parameter estimation in fetal diffusion-weighted MRI - learning from fetal and newborn data -- Deep Fiber Clustering: Anatomically Informed Unsupervised Deep Learning for Fast and Effective White Matter Parcellation -- Disentangled and Proportional Representation Learning for Multi-View Brain Connectomes -- Quantifying structural connectivity in brain tumor patients -- Q-space Conditioned Translation Networks for Directional Synthesis of Diffusion Weighted Imagesfrom Multi-modal Structural MRI -- Clinical Applications - Neuroimaging - Functional Brain Networks -- Detecting Brain State Changes by Geometric Deep Learning of Functional Dynamics on Riemannian Manifold -- From Brain to Body: Learning Low-Frequency Respiration and Cardiac Signals from fMRI Dynamics -- Multi-Head GAGNN: A Multi-Head Guided Attention Graph Neural Network for Modeling Spatio-Temporal Patterns of Holistic Brain Functional Networks -- Building Dynamic Hierarchical Brain Networks and Capturing Transient Meta-states for Early Mild Cognitive Impairment Diagnosis -- Recurrent Multigraph Integrator Network for Predicting the Evolution of Population-Driven Brain Connectivity Templates -- Efficient neural network approximation of robust PCA for automated analysis of calcium imaging data -- Text2Brain: Synthesis of Brain Activation Maps from Free-form Text Query -- Estimation of spontaneous neuronal activity using homomorphic filtering -- A Matrix Auto-encoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes -- Clinical Applications - Neuroimaging - Others -- Topological Receptive Field Model for Human Retinotopic Mapping -- SegRecon: Learning Joint Brain Surface Reconstruction and Segmentation from Images -- LG-Net: Lesion Gate Network for Multiple Sclerosis Lesion Inpainting -- Self-supervised Lesion Change Detection and Localisation in Longitudinal Multiple Sclerosis Brain Imaging -- SyNCCT: Synthetic Non-Contrast Images of the Brain from Single-Energy Computed Tomography Angiography -- Local Morphological Measures Confirm that Folding within Small Partitions of the Human Cortex Follows Universal Scaling Law -- Exploring the Functional Difference of Gyri/Sulci via Hierarchical Interpretable Autoencoder -- Personalized Matching and Analysis of Cortical Folding Patterns via Patch-Based Intrinsic Brain Mapping -- Clinical Applications - Oncology -- A Location Constrained Dual-branch Network for Reliable Diagnosis of Jaw Tumors and Cysts -- Motion Correction for Liver DCE-MRI with Time-Intensity Curve Constraint -- Parallel Capsule Networks for Classification of White Blood Cells -- Incorporating Isodose Lines and Gradient Information via Multi-task Learning for Dose Prediction in Radiotherapy -- Sequential Learning on Liver Tumor Boundary Semantics and Prognostic Biomarker Mining -- Do we need complex image features to personalize treatment of patients with locally advanced rectal cancer? -- Multiple Instance Learning with Auxiliary Task Weighting for Multiple Myeloma Classification.
En línea:	https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
Link:	https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i

24th International Conference, Strasbourg, France, September 27 – October 1, 2021, Proceedings, Part VII [documento electrónico] / de Bruijne, Marleen, ; Cattin, Philippe C., ; Cotin, Stéphane, ; Padoy, Nicolas, ; Speidel, Stefanie, ; Zheng, Yefeng, ; Essert, Caroline,  . -  1 ed.  . - [s.l.] : Springer, 2021 . - XXXIX, 801 p. 277 ilustraciones, 258 ilustraciones en color.
ISBN : 978-3-030-87234-2
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  Bioinformática  Sistemas de reconocimiento de patrones  Informática Médica  Biología Computacional y de Sistemas  Reconocimiento de patrones automatizado  Informática de la Salud
Índice Dewey:	006.37 Visión artificial
Resumen:	El conjunto de ocho volúmenes LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907 y 12908 constituye las actas arbitradas de la 24.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2021, celebrada en Estrasburgo, Francia. en septiembre/octubre de 2021.* Los 531 artículos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1630 presentaciones en un proceso de revisión doble ciego. Los artículos están organizados en las siguientes secciones temáticas: Parte I: segmentación de imágenes Parte II: aprendizaje automático - aprendizaje autosupervisado; aprendizaje automático: aprendizaje semisupervisado; y aprendizaje automático: aprendizaje débilmente supervisado. Parte III: aprendizaje automático: avances en la teoría del aprendizaje automático; aprendizaje automático: modelos de atención; aprendizaje automático: adaptación de dominios; aprendizaje automático: aprendizaje federado; aprendizaje automático: interpretabilidad / explicabilidad; y aprendizaje automático - incertidumbre Parte IV: registro de imágenes; intervenciones y cirugía guiadas por imágenes; ciencia de datos quirúrgicos; planificación y simulación quirúrgica; análisis de habilidades quirúrgicas y flujo de trabajo; y visualización quirúrgica y realidad mixta, aumentada y virtual. Parte V: diagnóstico asistido por ordenador; integración de imágenes con biomarcadores sin imágenes; y predicción de resultados/enfermedades. Parte VI: reconstrucción de imágenes; aplicaciones clínicas - cardíacas; y aplicaciones clínicas - vasculares Parte VII: aplicaciones clínicas - abdomen; aplicaciones clínicas - mama; aplicaciones clínicas - dermatología; aplicaciones clínicas: imágenes fetales; aplicaciones clínicas - pulmón; aplicaciones clínicas - neuroimagen - desarrollo cerebral; aplicaciones clínicas - neuroimagen - DWI y tractografía; aplicaciones clínicas - neuroimagen - redes cerebrales funcionales; aplicaciones clínicas - neuroimagen - otras; y aplicaciones clínicas - oncología Parte VIII: aplicaciones clínicas - oftalmología; patología computacional (integrativa); modalidades - microscopía; modalidades - histopatología; y modalidades - ultrasonido *La conferencia se realizó de manera virtual.
Nota de contenido:	Clinical Applications – Abdomen -- Learning More for Free - A Multi Task Learning Approach for Improved Pathology Classification in Capsule Endoscopy -- Learning-based attenuation quantification in abdominal ultrasound -- Colorectal Polyp Classification from White-light Colonoscopy Images via Domain Alignment -- Non-invasive Assessment of Hepatic Venous Pressure Gradient (HVPG) Based on MR Flow Imaging and Computational Fluid Dynamics -- Deep-Cleansing: Deep-learning based Electronic Cleansing in Dual-energy CT Colonography -- Clinical Applications - Breast -- Interactive smoothing parameter optimization in DBT Reconstruction using Deep learning -- Synthesis of Contrast-enhanced Spectral Mammograms from Low-energy Mammograms Using cGAN-Based Synthesis Network -- Self-adversarial Learning for Detection of Clustered Microcalcifications in Mammograms -- Graph Transformers for Characterization and Interpretation of Surgical Margins -- Domain Generalization for Mammography Detection viaMulti-style and Multi-view Contrastive Learning -- Learned super resolution ultrasound for improved breast lesion characterization -- BI-RADS Classification of Calcification on Mammograms -- Supervised Contrastive Pre-Training for Mammographic Triage Screening Models -- Trainable summarization to improve breast tomosynthesis classification -- Clinical Applications - Dermatology -- Multi-level Relationship Capture Network for Automated Skin Lesion Recognition -- Culprit-Prune-Net: Efficient Continual Sequential Multi-Domain Learning with Application to Skin Lesion Classification -- End-to-end Ugly Duckling Sign Detection for Melanoma Identification with Transformers -- Automatic Severity Rating for Improved Psoriasis Treatment -- Clinical Applications - Fetal Imaging -- STRESS: Super-Resolution for Dynamic Fetal MRI using Self-Supervised Learning -- Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps -- EllipseNet: Anchor-Free Ellipse Detection for Automatic Cardiac Biometrics in Fetal Echocardiography -- AutoFB: Automating Fetal Biometry Estimation from Standard Ultrasound Planes -- Learning Spatiotemporal Probabilistic Atlas of Fetal Brains with Anatomically Constrained Registration Network -- Clinical Applications - Lung -- Leveraging Auxiliary Information from EMR for Weakly Supervised Pulmonary Nodule Detection -- M-SEAM-NAM: Multi-instance Self-supervised Equivalent Attention Mechanism with Neighborhood Affinity Module for Double Weakly Supervised Segmentation of COVID-19 -- Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs -- Beyond COVID-19 Diagnosis: Prognosis with Hierarchical Graph Representation Learning -- RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting -- Detecting when pre-trained nnU-Net models fail silently for Covid-19 lung lesion segmentation -- Perceptual Quality Assessment of Chest Radiograph -- Pristine annotations-based multi-modal trained artificial intelligence solution to triage chest X-Ray for COVID19 -- Determination of error in 3D CT to 2D fluoroscopy image registration for endobronchial guidance -- Chest Radiograph Disentanglement for COVID-19 Outcome Prediction -- Attention based CNN-LSTM Network for Pulmonary Embolism Prediction on Chest Computed Tomography Pulmonary Angiograms -- LuMiRa: An Integrated Lung Deformation Atlas and 3D-CNN model of Infiltrates for COVID-19 Prognosis -- Clinical Applications - Neuroimaging - Brain Development -- Multi-site Incremental Image Quality Assessment of Structural MRI via Consensus Adversarial Representation Adaptation -- Surface-Guided Image Fusion for Preserving Cortical Details in Human Brain Templates -- Longitudinal Correlation Analysis for Decoding Multi-Modal Brain Development -- ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities -- Covariate Correcting Networks for Identifying Associations between Socioeconomic Factors and Brain Outcomes inChildren -- Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non–Contrast CT Images -- Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma Segmentation -- Joint PVL Detection and Manual Ability Classification using Semi-Supervised Multi-task Learning -- Clinical Applications - Neuroimaging - DWI And Tractography -- Active Cortex Tractography -- Highly Reproducible Whole Brain Parcellation in Individuals via Voxel Annotation with Fiber Clusters -- Accurate parameter estimation in fetal diffusion-weighted MRI - learning from fetal and newborn data -- Deep Fiber Clustering: Anatomically Informed Unsupervised Deep Learning for Fast and Effective White Matter Parcellation -- Disentangled and Proportional Representation Learning for Multi-View Brain Connectomes -- Quantifying structural connectivity in brain tumor patients -- Q-space Conditioned Translation Networks for Directional Synthesis of Diffusion Weighted Imagesfrom Multi-modal Structural MRI -- Clinical Applications - Neuroimaging - Functional Brain Networks -- Detecting Brain State Changes by Geometric Deep Learning of Functional Dynamics on Riemannian Manifold -- From Brain to Body: Learning Low-Frequency Respiration and Cardiac Signals from fMRI Dynamics -- Multi-Head GAGNN: A Multi-Head Guided Attention Graph Neural Network for Modeling Spatio-Temporal Patterns of Holistic Brain Functional Networks -- Building Dynamic Hierarchical Brain Networks and Capturing Transient Meta-states for Early Mild Cognitive Impairment Diagnosis -- Recurrent Multigraph Integrator Network for Predicting the Evolution of Population-Driven Brain Connectivity Templates -- Efficient neural network approximation of robust PCA for automated analysis of calcium imaging data -- Text2Brain: Synthesis of Brain Activation Maps from Free-form Text Query -- Estimation of spontaneous neuronal activity using homomorphic filtering -- A Matrix Auto-encoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes -- Clinical Applications - Neuroimaging - Others -- Topological Receptive Field Model for Human Retinotopic Mapping -- SegRecon: Learning Joint Brain Surface Reconstruction and Segmentation from Images -- LG-Net: Lesion Gate Network for Multiple Sclerosis Lesion Inpainting -- Self-supervised Lesion Change Detection and Localisation in Longitudinal Multiple Sclerosis Brain Imaging -- SyNCCT: Synthetic Non-Contrast Images of the Brain from Single-Energy Computed Tomography Angiography -- Local Morphological Measures Confirm that Folding within Small Partitions of the Human Cortex Follows Universal Scaling Law -- Exploring the Functional Difference of Gyri/Sulci via Hierarchical Interpretable Autoencoder -- Personalized Matching and Analysis of Cortical Folding Patterns via Patch-Based Intrinsic Brain Mapping -- Clinical Applications - Oncology -- A Location Constrained Dual-branch Network for Reliable Diagnosis of Jaw Tumors and Cysts -- Motion Correction for Liver DCE-MRI with Time-Intensity Curve Constraint -- Parallel Capsule Networks for Classification of White Blood Cells -- Incorporating Isodose Lines and Gradient Information via Multi-task Learning for Dose Prediction in Radiotherapy -- Sequential Learning on Liver Tumor Boundary Semantics and Prognostic Biomarker Mining -- Do we need complex image features to personalize treatment of patients with locally advanced rectal cancer? -- Multiple Instance Learning with Auxiliary Task Weighting for Multiple Myeloma Classification.
En línea:	https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
Link:	https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i