Título :	24th International Conference, Strasbourg, France, September 27 – October 1, 2021, Proceedings, Part VIII
Tipo de documento:	documento electrónico
Autores:	de Bruijne, Marleen, ; Cattin, Philippe C., ; Cotin, Stéphane, ; Padoy, Nicolas, ; Speidel, Stefanie, ; Zheng, Yefeng, ; Essert, Caroline,
Mención de edición:	1 ed.
Editorial:	[s.l.] : Springer
Fecha de publicación:	2021
Número de páginas:	XXXVIII, 704 p. 227 ilustraciones, 213 ilustraciones en color.
ISBN/ISSN/DL:	978-3-030-87237-3
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  Informática Médica  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 - Ophthalmology -- Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition -- Cross-domain Depth Estimation Network for 3D Vessel Reconstruction in OCT Angiography -- Distinguishing Differences Matters: Focal Contrastive Network for Peripheral Anterior Synechiae Recognition -- RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network -- MIL-VT: Multiple Instance Learning Enhanced Vision Transformer for Fundus Image Classification -- Local-global Dual Perception based Deep Multiple Instance Learning for Retinal Disease Classification -- BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images -- LensID: A CNN-RNN-Based Framework Towards Lens Irregularity Detection in Cataract Surgery Videos -- I-SECRET: Importance-guided fundus image enhancement via semi-supervised contrastive constraining -- Few-shot Transfer Learning for Hereditary Retinal Diseases Recognition -- Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retina OCT Images -- Computational (Integrative) Pathology -- GQ-GCN: Group Quadratic Graph Convolutional Network for Classification of Histopathological Images -- Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network -- Prototypical models for classifying high-risk atypical breast lesions -- Hierarchical Attention Guided Framework for Multi-resolution Collaborative Whole Slide Image Segmentation -- Hierarchical Phenotyping and Graph Modeling of Spatial Architecture in Lymphoid Neoplasms -- A computational geometry approach for modeling neuronal fiber pathways -- TransPath: Transformer-based Self-supervised Learning for Histopathological Image Classification -- From Pixel to Whole Slide: Automatic Detection of Microvascular Invasion in Hepatocellular Carcinoma on Histopathological Image via Cascaded Networks -- DT-MIL: Deformable Transformer for Multi-instance Learning on Histopathological Image -- Early Detection of Liver Fibrosis Using Graph Convolutional Networks -- Hierarchical graph pathomic network for progression free survival prediction -- Increasing Consistency of Evoked Response in Thalamic Nuclei During Repetitive Burst Stimulation of Peripheral Nerve in Humans -- Weakly supervised pan-cancer segmentation tool -- Structure-Preserving Multi-Domain Stain Color Augmentation using Style-Transfer with Disentangled Representations -- MetaCon: Meta Contrastive Learning for Microsatellite Instability Detection -- Generalizing Nucleus Recognition Model in Multi-source Ki67 Immunohistochemistry Stained Images via Domain-specific Pruning -- Cells are Actors: Social Network Analysis with Classical ML for SOTA Histology Image Classification -- Instance-based Vision Transformer for Subtyping of Papillary Renal Cell Carcinoma in Histopathological Image -- Hybrid Supervision Learning for Whole Slide Image Classification -- MorphSet: Improving Renal Histopathology Case Assessment Through Learned Prognostic Vectors -- Accounting for Dependencies in Deep Learning based Multiple Instance Learning for Whole Slide Imaging -- Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks -- Pay Attention with Focus: A Novel Learning Scheme for Classification of Whole Slide Images -- Modalities - Microscopy -- Developmental Stage Classification of Embryos Using Two-Stream Neural Network with Linear-Chain Conditional Random Field -- Semi-supervised Cell Detection in Time-lapse Images Using Temporal Consistency -- Cell Detection in Domain Shift Problem Using Pseudo-Cell-Position Heatmap -- 2D Histology Meets 3D Topology: Cytoarchitectonic Brain Mapping with Graph Neural Networks -- Annotation-efficient Cell Counting -- A Deep Learning Bidirectional Temporal Tracking Algorithm for Automated Blood Cell Counting from Non-invasive Capillaroscopy Videos -- Cell Detection from Imperfect Annotation by Pseudo Label Selection Using P-classification -- Learning Neuron Stitching for Connectomics -- CA^{2.5}-Net Nuclei Segmentation Framework with a Microscopy Cell Benchmark Collection -- Automated Malaria Cells Detection from Blood Smears under Severe Class Imbalance via Importance-aware Balanced Group Softmax -- Non-parametric vignetting correction for sparse spatial transcriptomics images -- Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy -- Deep Reinforcement Exemplar Learning for Annotation Refinement -- Modalities - Histopathology -- Instance-aware Feature Alignment for Cross-domain Cell Nuclei Detection in Histopathology Images -- Positive-unlabeled Learning for Cell Detection in Histopathology Images with Incomplete Annotations -- GloFlow: Whole Slide Image Stitching from Video using Optical Flow and Global Image Alignment -- Multi-modal Multi-instance Learning using Weakly Correlated Histopathological Images and Tabular Clinical Information -- Ranking loss: A ranking-based deep neural network for colorectal cancer grading in pathology images -- Spatial Attention-based Deep Learning System for Breast Cancer Pathological Complete Response Prediction with Serial Histopathology Images in Multiple Stains -- Integration of Patch Features through Self-Supervised Learning and Transformer for Survival Analysis on Whole Slide Images -- Contrastive Learning Based Stain Normalization Across Multiple Tumor Histopathology -- Semi-supervised Adversarial Learning for Stain Normalisation in Histopathology Images -- Learning Visual Features by Colorization for Slide-Consistent Survival Prediction from Whole Slide Images -- Adversarial learning of cancer tissue representations -- A Multi-attribute Controllable Generative Model for Histopathology Image Synthesis -- Modalities - Ultrasound -- USCL: Pretraining Deep Ultrasound Image Diagnosis Model through Video Contrastive Representation Learning -- Identifying Quantitative and Explanatory Tumor Indexes from Dynamic Contrast Enhanced Ultrasound -- Weakly-Supervised Ultrasound Video Segmentation with Minimal Annotations -- Content-Preserving Unpaired Translation from Simulated to Realistic Ultrasound Images -- Visual-Assisted Probe Movement Guidance for Obstetric Ultrasound Scanning using Landmark Retrieval -- Training Deep Networks for Prostate Cancer Diagnosis Using Coarse Histopathological Labels -- Rethinking Ultrasound Augmentation: A Physics-Inspired Approach.
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 VIII [documento electrónico] / de Bruijne, Marleen, ; Cattin, Philippe C., ; Cotin, Stéphane, ; Padoy, Nicolas, ; Speidel, Stefanie, ; Zheng, Yefeng, ; Essert, Caroline,  . -  1 ed.  . - [s.l.] : Springer, 2021 . - XXXVIII, 704 p. 227 ilustraciones, 213 ilustraciones en color.
ISBN : 978-3-030-87237-3
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  Informática Médica  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 - Ophthalmology -- Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition -- Cross-domain Depth Estimation Network for 3D Vessel Reconstruction in OCT Angiography -- Distinguishing Differences Matters: Focal Contrastive Network for Peripheral Anterior Synechiae Recognition -- RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network -- MIL-VT: Multiple Instance Learning Enhanced Vision Transformer for Fundus Image Classification -- Local-global Dual Perception based Deep Multiple Instance Learning for Retinal Disease Classification -- BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images -- LensID: A CNN-RNN-Based Framework Towards Lens Irregularity Detection in Cataract Surgery Videos -- I-SECRET: Importance-guided fundus image enhancement via semi-supervised contrastive constraining -- Few-shot Transfer Learning for Hereditary Retinal Diseases Recognition -- Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retina OCT Images -- Computational (Integrative) Pathology -- GQ-GCN: Group Quadratic Graph Convolutional Network for Classification of Histopathological Images -- Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network -- Prototypical models for classifying high-risk atypical breast lesions -- Hierarchical Attention Guided Framework for Multi-resolution Collaborative Whole Slide Image Segmentation -- Hierarchical Phenotyping and Graph Modeling of Spatial Architecture in Lymphoid Neoplasms -- A computational geometry approach for modeling neuronal fiber pathways -- TransPath: Transformer-based Self-supervised Learning for Histopathological Image Classification -- From Pixel to Whole Slide: Automatic Detection of Microvascular Invasion in Hepatocellular Carcinoma on Histopathological Image via Cascaded Networks -- DT-MIL: Deformable Transformer for Multi-instance Learning on Histopathological Image -- Early Detection of Liver Fibrosis Using Graph Convolutional Networks -- Hierarchical graph pathomic network for progression free survival prediction -- Increasing Consistency of Evoked Response in Thalamic Nuclei During Repetitive Burst Stimulation of Peripheral Nerve in Humans -- Weakly supervised pan-cancer segmentation tool -- Structure-Preserving Multi-Domain Stain Color Augmentation using Style-Transfer with Disentangled Representations -- MetaCon: Meta Contrastive Learning for Microsatellite Instability Detection -- Generalizing Nucleus Recognition Model in Multi-source Ki67 Immunohistochemistry Stained Images via Domain-specific Pruning -- Cells are Actors: Social Network Analysis with Classical ML for SOTA Histology Image Classification -- Instance-based Vision Transformer for Subtyping of Papillary Renal Cell Carcinoma in Histopathological Image -- Hybrid Supervision Learning for Whole Slide Image Classification -- MorphSet: Improving Renal Histopathology Case Assessment Through Learned Prognostic Vectors -- Accounting for Dependencies in Deep Learning based Multiple Instance Learning for Whole Slide Imaging -- Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks -- Pay Attention with Focus: A Novel Learning Scheme for Classification of Whole Slide Images -- Modalities - Microscopy -- Developmental Stage Classification of Embryos Using Two-Stream Neural Network with Linear-Chain Conditional Random Field -- Semi-supervised Cell Detection in Time-lapse Images Using Temporal Consistency -- Cell Detection in Domain Shift Problem Using Pseudo-Cell-Position Heatmap -- 2D Histology Meets 3D Topology: Cytoarchitectonic Brain Mapping with Graph Neural Networks -- Annotation-efficient Cell Counting -- A Deep Learning Bidirectional Temporal Tracking Algorithm for Automated Blood Cell Counting from Non-invasive Capillaroscopy Videos -- Cell Detection from Imperfect Annotation by Pseudo Label Selection Using P-classification -- Learning Neuron Stitching for Connectomics -- CA^{2.5}-Net Nuclei Segmentation Framework with a Microscopy Cell Benchmark Collection -- Automated Malaria Cells Detection from Blood Smears under Severe Class Imbalance via Importance-aware Balanced Group Softmax -- Non-parametric vignetting correction for sparse spatial transcriptomics images -- Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy -- Deep Reinforcement Exemplar Learning for Annotation Refinement -- Modalities - Histopathology -- Instance-aware Feature Alignment for Cross-domain Cell Nuclei Detection in Histopathology Images -- Positive-unlabeled Learning for Cell Detection in Histopathology Images with Incomplete Annotations -- GloFlow: Whole Slide Image Stitching from Video using Optical Flow and Global Image Alignment -- Multi-modal Multi-instance Learning using Weakly Correlated Histopathological Images and Tabular Clinical Information -- Ranking loss: A ranking-based deep neural network for colorectal cancer grading in pathology images -- Spatial Attention-based Deep Learning System for Breast Cancer Pathological Complete Response Prediction with Serial Histopathology Images in Multiple Stains -- Integration of Patch Features through Self-Supervised Learning and Transformer for Survival Analysis on Whole Slide Images -- Contrastive Learning Based Stain Normalization Across Multiple Tumor Histopathology -- Semi-supervised Adversarial Learning for Stain Normalisation in Histopathology Images -- Learning Visual Features by Colorization for Slide-Consistent Survival Prediction from Whole Slide Images -- Adversarial learning of cancer tissue representations -- A Multi-attribute Controllable Generative Model for Histopathology Image Synthesis -- Modalities - Ultrasound -- USCL: Pretraining Deep Ultrasound Image Diagnosis Model through Video Contrastive Representation Learning -- Identifying Quantitative and Explanatory Tumor Indexes from Dynamic Contrast Enhanced Ultrasound -- Weakly-Supervised Ultrasound Video Segmentation with Minimal Annotations -- Content-Preserving Unpaired Translation from Simulated to Realistic Ultrasound Images -- Visual-Assisted Probe Movement Guidance for Obstetric Ultrasound Scanning using Landmark Retrieval -- Training Deep Networks for Prostate Cancer Diagnosis Using Coarse Histopathological Labels -- Rethinking Ultrasound Augmentation: A Physics-Inspired Approach.
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