Título :	24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part II
Tipo de documento:	documento electrónico
Autores:	de Bruijne, Marleen, ; Cattin, Philippe C., ; Cotin, Stéphane, ; Padoy, Nicolas, ; Speidel, Stefanie, ; Zheng, Yefeng, ; Essert, Caroline,
Mención de edición:	1 ed.
Editorial:	[s.l.] : Springer
Fecha de publicación:	2021
Número de páginas:	XXXVII, 662 p. 181 ilustraciones, 175 ilustraciones en color.
ISBN/ISSN/DL:	978-3-030-87196-3
Nota general:	Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Palabras clave:	Visión por computador  Inteligencia artificial  Sistemas de reconocimiento de patrones  Bioinformática  Informática Médica  Reconocimiento de patrones automatizado  Biología Computacional y de Sistemas  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:	Machine Learning - Self-Supervised Learning -- SSLP: Spatial Guided Self-supervised Learning on Pathological Images -- Segmentation of Left Atrial MR Images via Self-supervised Semi-supervised Meta-learning -- Deformed2Self: Self-Supervised Denoising for Dynamic Medical Imaging -- Imbalance-Aware Self-Supervised Learning for 3D Radiomic Representations -- Self-supervised visual representation learning for histopathological images -- Contrastive Learning with Continuous Proxy Meta-Data For 3D MRI Classification -- Sli2Vol: Annotate a 3D Volume from a Single Slice with Self-Supervised Learning -- Self-Supervised Longitudinal Neighbourhood Embedding -- Self-Supervised Multi-Modal Alignment For Whole Body Medical Imaging -- SimTriplet: Simple Triplet Representation Learning with a Single GPU -- Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images -- SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation -- Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation -- SpineGEM: A Hybrid-Supervised Model Generation Strategy Enabling Accurate Spine Disease Classification with a Small Training Dataset -- Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Images -- Topological Learning and Its Application to Multimodal Brain Network Integration -- One-Shot Medical Landmark Detection -- Implicit field learning for unsupervised anomaly detection in medical images -- Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images -- Contrastive Pre-training and Representation Distillation for Medical Visual Question Answering Based on Radiology Images -- Positional Contrastive Learning for Volumetric Medical Image Segmentation -- Longitudinal self-supervision to disentangle inter-patient variability from disease progression -- Self-Supervised Vessel Enhancement Using Flow-Based Consistencies -- Unsupervised Contrastive Learning of Radiomics and Deep Features for Label-Efficient Tumor Classification -- Learning 4D Infant Cortical Surface Atlas with Unsupervised Spherical Networks -- Multimodal Representation Learning via Maximization of Local Mutual Information -- Inter-Regional High-level Relation Learning from Functional Connectivity via Self-Supervision -- Machine Learning - Semi-Supervised Learning -- Semi-supervised Left Atrium Segmentation with Mutual Consistency Training -- Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation -- Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency -- Few-Shot Domain Adaptation with Polymorphic Transformers -- Lesion Segmentation and RECIST Diameter Prediction via Click-driven Attention and Dual-path Connection -- Reciprocal Learning for Semi-supervised Segmentation -- Disentangled Sequential Graph Autoencoder for Preclinical Alzheimer's Disease Characterizations from ADNI Study -- POPCORN: Progressive Pseudo-labeling with Consistency Regularization and Neighboring -- 3D Semantic Mapping from Arthroscopy using Out-of-distribution Pose and Depth and In-distribution Segmentation Training -- Semi-Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation -- Implicit Neural Distance Representation for Unsupervised and Supervised Classification of Complex Anatomies -- 3D Graph-S2Net: Shape-Aware Self-Ensembling Network for Semi-Supervised Segmentation with Bilateral Graph Convolution -- Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation -- Neighbor Matching for Semi-supervised Learning -- Tripled-uncertainty Guided Mean Teacher model for Semi-supervised Medical Image Segmentation -- Learning with Noise: Mask-guided Attention Model for Weakly Supervised Nuclei Segmentation -- Order-Guided Disentangled Representation Learning for Ulcerative Colitis Classification with Limited Labels -- Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation -- Functional Magnetic Resonance Imaging data augmentation through conditional ICA -- Scalable joint detection and segmentation of surgical instruments with weak supervision -- Machine Learning - Weakly Supervised Learning -- Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss -- Bounding Box Tightness Prior for Weakly Supervised Image Segmentation -- OXnet: Deep Omni-supervised Thoracic Disease Detection from Chest X-rays -- Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation -- Quality-Aware Memory Network for Interactive Volumetric Image Segmentation -- Improving Pneumonia Localization via Cross-Attention on Medical Images and Reports -- Combining Attention-based Multiple Instance Learning and Gaussian Processes for CT Hemorrhage Detection -- CPNet: Cycle Prototype Network for Weakly-supervised 3D Renal Chamber Segmentation -- Observational Supervision for Medical Image Classification using Gaze Data -- Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation -- Efficient and Generic Interactive Segmentation Framework to Correct Mispredictions during Clinical Evaluation of Medical Images -- Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs -- Labels-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation.
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 II [documento electrónico] / de Bruijne, Marleen, ; Cattin, Philippe C., ; Cotin, Stéphane, ; Padoy, Nicolas, ; Speidel, Stefanie, ; Zheng, Yefeng, ; Essert, Caroline,  . -  1 ed.  . - [s.l.] : Springer, 2021 . - XXXVII, 662 p. 181 ilustraciones, 175 ilustraciones en color.
ISBN : 978-3-030-87196-3
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.

Palabras clave:	Visión por computador  Inteligencia artificial  Sistemas de reconocimiento de patrones  Bioinformática  Informática Médica  Reconocimiento de patrones automatizado  Biología Computacional y de Sistemas  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:	Machine Learning - Self-Supervised Learning -- SSLP: Spatial Guided Self-supervised Learning on Pathological Images -- Segmentation of Left Atrial MR Images via Self-supervised Semi-supervised Meta-learning -- Deformed2Self: Self-Supervised Denoising for Dynamic Medical Imaging -- Imbalance-Aware Self-Supervised Learning for 3D Radiomic Representations -- Self-supervised visual representation learning for histopathological images -- Contrastive Learning with Continuous Proxy Meta-Data For 3D MRI Classification -- Sli2Vol: Annotate a 3D Volume from a Single Slice with Self-Supervised Learning -- Self-Supervised Longitudinal Neighbourhood Embedding -- Self-Supervised Multi-Modal Alignment For Whole Body Medical Imaging -- SimTriplet: Simple Triplet Representation Learning with a Single GPU -- Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images -- SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation -- Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation -- SpineGEM: A Hybrid-Supervised Model Generation Strategy Enabling Accurate Spine Disease Classification with a Small Training Dataset -- Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Images -- Topological Learning and Its Application to Multimodal Brain Network Integration -- One-Shot Medical Landmark Detection -- Implicit field learning for unsupervised anomaly detection in medical images -- Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images -- Contrastive Pre-training and Representation Distillation for Medical Visual Question Answering Based on Radiology Images -- Positional Contrastive Learning for Volumetric Medical Image Segmentation -- Longitudinal self-supervision to disentangle inter-patient variability from disease progression -- Self-Supervised Vessel Enhancement Using Flow-Based Consistencies -- Unsupervised Contrastive Learning of Radiomics and Deep Features for Label-Efficient Tumor Classification -- Learning 4D Infant Cortical Surface Atlas with Unsupervised Spherical Networks -- Multimodal Representation Learning via Maximization of Local Mutual Information -- Inter-Regional High-level Relation Learning from Functional Connectivity via Self-Supervision -- Machine Learning - Semi-Supervised Learning -- Semi-supervised Left Atrium Segmentation with Mutual Consistency Training -- Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation -- Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency -- Few-Shot Domain Adaptation with Polymorphic Transformers -- Lesion Segmentation and RECIST Diameter Prediction via Click-driven Attention and Dual-path Connection -- Reciprocal Learning for Semi-supervised Segmentation -- Disentangled Sequential Graph Autoencoder for Preclinical Alzheimer's Disease Characterizations from ADNI Study -- POPCORN: Progressive Pseudo-labeling with Consistency Regularization and Neighboring -- 3D Semantic Mapping from Arthroscopy using Out-of-distribution Pose and Depth and In-distribution Segmentation Training -- Semi-Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation -- Implicit Neural Distance Representation for Unsupervised and Supervised Classification of Complex Anatomies -- 3D Graph-S2Net: Shape-Aware Self-Ensembling Network for Semi-Supervised Segmentation with Bilateral Graph Convolution -- Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation -- Neighbor Matching for Semi-supervised Learning -- Tripled-uncertainty Guided Mean Teacher model for Semi-supervised Medical Image Segmentation -- Learning with Noise: Mask-guided Attention Model for Weakly Supervised Nuclei Segmentation -- Order-Guided Disentangled Representation Learning for Ulcerative Colitis Classification with Limited Labels -- Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation -- Functional Magnetic Resonance Imaging data augmentation through conditional ICA -- Scalable joint detection and segmentation of surgical instruments with weak supervision -- Machine Learning - Weakly Supervised Learning -- Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss -- Bounding Box Tightness Prior for Weakly Supervised Image Segmentation -- OXnet: Deep Omni-supervised Thoracic Disease Detection from Chest X-rays -- Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation -- Quality-Aware Memory Network for Interactive Volumetric Image Segmentation -- Improving Pneumonia Localization via Cross-Attention on Medical Images and Reports -- Combining Attention-based Multiple Instance Learning and Gaussian Processes for CT Hemorrhage Detection -- CPNet: Cycle Prototype Network for Weakly-supervised 3D Renal Chamber Segmentation -- Observational Supervision for Medical Image Classification using Gaze Data -- Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation -- Efficient and Generic Interactive Segmentation Framework to Correct Mispredictions during Clinical Evaluation of Medical Images -- Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs -- Labels-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation.
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