Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data / Wang, Qian ; Milletari, Fausto ; Nguyen, Hien V. ; Albarqouni, Shadi ; Cardoso, M. Jorge ; Rieke, Nicola ; Xu, Ziyue ; Kamnitsas, Konstantinos ; Patel, Vishal ; Roysam, Badri ; Jiang, Steve ; Zhou, Kevin ; Luu, Khoa ; Le, Ngan  

Público ISBD Ningún comentario para este registro. Título : Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data : First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019, Shenzhen, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings Tipo de documento: documento electrónico Autores: Wang, Qian, ; Milletari, Fausto, ; Nguyen, Hien V., ; Albarqouni, Shadi, ; Cardoso, M. Jorge, ; Rieke, Nicola, ; Xu, Ziyue, ; Kamnitsas, Konstantinos, ; Patel, Vishal, ; Roysam, Badri, ; Jiang, Steve, ; Zhou, Kevin, ; Luu, Khoa, ; Le, Ngan, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2019 Número de páginas: XVII, 254 p. 113 ilustraciones, 79 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-33391-1 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Visión por computador  Inteligencia artificial  Informática Médica  Informática de la Salud Clasificación: 006.37 Resumen: Este libro constituye las actas arbitradas del Primer Taller MICCAI sobre Adaptación de Dominio y Transferencia de Representación, DART 2019, y el Primer Taller Internacional sobre Aprendizaje de Imágenes Médicas con Menos Etiquetas y Datos Imperfectos, MIL3ID 2019, celebrado junto con MICCAI 2019, en Shenzhen, China, en octubre de 2019. DART 2019 aceptó 12 artículos para publicación de 18 presentaciones. Los artículos abordan avances metodológicos e ideas que pueden mejorar la aplicabilidad del aprendizaje automático y los enfoques de aprendizaje profundo en entornos clínicos haciéndolos sólidos y consistentes en diferentes dominios. MIL3ID aceptó 16 artículos de 43 enviados para publicación, que abordan las mejores prácticas en el aprendizaje de imágenes médicas con escasez de etiquetas e imperfección de datos. . Nota de contenido: DART 2019 -- Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation -- Temporal Consistency Objectives Regularize the Learning of Disentangled Representations -- Multi-layer Domain Adaptation for Deep Convolutional Networks -- Intramodality Domain Adaptation using Self Ensembling and Adversarial Training -- Learning Interpretable Disentangled Representations using Adversarial VAEs -- Synthesising Images and Labels Between MR Sequence Types With CycleGAN -- Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning -- Cross-modality Knowledge Transfer for Prostate Segmentation from CT Scans -- A Pulmonary Nodule Detection Method Based on Residual Learning and Dense Connection -- Harmonization and Targeted Feature Dropout for Generalized Segmentation: Application to Multi-site Traumatic Brain Injury Images -- Improving Pathological Structure Segmentation Via Transfer Learning Across Diseases -- Generating Virtual Chromoendoscopic Imagesand Improving Detectability and Classification Performance of Endoscopic Lesions -- MIL3ID 2019 -- Self-supervised learning of inverse problem solvers in medical imaging -- Weakly Supervised Segmentation of Vertebral Bodies with Iterative Slice-propagation -- A Cascade Attention Network for Liver Lesion Classification in Weakly-labeled Multi-phase CT Images -- CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT -- Active Learning Technique for Multimodal Brain Tumor Segmentation using Limited Labeled Images -- Semi-supervised Learning of Fetal Anatomy from Ultrasound -- Multi-modal segmentation with missing MR sequences using pre-trained fusion networks -- More unlabelled data or label more data? A study on semi-supervised laparoscopic image segmentation -- Few-shot Learning with Deep Triplet Networks for Brain Imaging Modality Recognition -- A Convolutional Neural Network Method for Boundary Optimization Enables Few-Shot Learning for Biomedical Image Segmentation -- Transfer Learning from Partial Annotations for Whole Brain Segmentation -- Learning to Segment Skin Lesions from Noisy Annotations -- A Weakly Supervised Method for Instance Segmentation of Biological Cells -- Towards Practical Unsupervised Anomaly Detection on Retinal Images -- Fine tuning U-Net for ultrasound image segmentation: which layers -- Multi-task Learning for Neonatal Brain Segmentation Using 3D Dense-Unet with Dense Attention Guided by Geodesic Distance. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the First MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the First International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. DART 2019 accepted 12 papers for publication out of 18 submissions. The papers deal with methodological advancements and ideas that can improve the applicability of machine learning and deep learning approaches to clinical settings by making them robust and consistent across different domains. MIL3ID accepted 16 papers out of 43 submissions for publication, dealing with best practices in medical image learning with label scarcity and data imperfection. . Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data : First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019, Shenzhen, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings [documento electrónico] / Wang, Qian, ; Milletari, Fausto, ; Nguyen, Hien V., ; Albarqouni, Shadi, ; Cardoso, M. Jorge, ; Rieke, Nicola, ; Xu, Ziyue, ; Kamnitsas, Konstantinos, ; Patel, Vishal, ; Roysam, Badri, ; Jiang, Steve, ; Zhou, Kevin, ; Luu, Khoa, ; Le, Ngan,  . -  1 ed.  . - [s.l.] : Springer, 2019 . - XVII, 254 p. 113 ilustraciones, 79 ilustraciones en color. ISBN : 978-3-030-33391-1 Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Visión por computador  Inteligencia artificial  Informática Médica  Informática de la Salud Clasificación: 006.37 Resumen: Este libro constituye las actas arbitradas del Primer Taller MICCAI sobre Adaptación de Dominio y Transferencia de Representación, DART 2019, y el Primer Taller Internacional sobre Aprendizaje de Imágenes Médicas con Menos Etiquetas y Datos Imperfectos, MIL3ID 2019, celebrado junto con MICCAI 2019, en Shenzhen, China, en octubre de 2019. DART 2019 aceptó 12 artículos para publicación de 18 presentaciones. Los artículos abordan avances metodológicos e ideas que pueden mejorar la aplicabilidad del aprendizaje automático y los enfoques de aprendizaje profundo en entornos clínicos haciéndolos sólidos y consistentes en diferentes dominios. MIL3ID aceptó 16 artículos de 43 enviados para publicación, que abordan las mejores prácticas en el aprendizaje de imágenes médicas con escasez de etiquetas e imperfección de datos. . Nota de contenido: DART 2019 -- Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation -- Temporal Consistency Objectives Regularize the Learning of Disentangled Representations -- Multi-layer Domain Adaptation for Deep Convolutional Networks -- Intramodality Domain Adaptation using Self Ensembling and Adversarial Training -- Learning Interpretable Disentangled Representations using Adversarial VAEs -- Synthesising Images and Labels Between MR Sequence Types With CycleGAN -- Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning -- Cross-modality Knowledge Transfer for Prostate Segmentation from CT Scans -- A Pulmonary Nodule Detection Method Based on Residual Learning and Dense Connection -- Harmonization and Targeted Feature Dropout for Generalized Segmentation: Application to Multi-site Traumatic Brain Injury Images -- Improving Pathological Structure Segmentation Via Transfer Learning Across Diseases -- Generating Virtual Chromoendoscopic Imagesand Improving Detectability and Classification Performance of Endoscopic Lesions -- MIL3ID 2019 -- Self-supervised learning of inverse problem solvers in medical imaging -- Weakly Supervised Segmentation of Vertebral Bodies with Iterative Slice-propagation -- A Cascade Attention Network for Liver Lesion Classification in Weakly-labeled Multi-phase CT Images -- CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT -- Active Learning Technique for Multimodal Brain Tumor Segmentation using Limited Labeled Images -- Semi-supervised Learning of Fetal Anatomy from Ultrasound -- Multi-modal segmentation with missing MR sequences using pre-trained fusion networks -- More unlabelled data or label more data? A study on semi-supervised laparoscopic image segmentation -- Few-shot Learning with Deep Triplet Networks for Brain Imaging Modality Recognition -- A Convolutional Neural Network Method for Boundary Optimization Enables Few-Shot Learning for Biomedical Image Segmentation -- Transfer Learning from Partial Annotations for Whole Brain Segmentation -- Learning to Segment Skin Lesions from Noisy Annotations -- A Weakly Supervised Method for Instance Segmentation of Biological Cells -- Towards Practical Unsupervised Anomaly Detection on Retinal Images -- Fine tuning U-Net for ultrasound image segmentation: which layers -- Multi-task Learning for Neonatal Brain Segmentation Using 3D Dense-Unet with Dense Attention Guided by Geodesic Distance. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the First MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the First International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. DART 2019 accepted 12 papers for publication out of 18 submissions. The papers deal with methodological advancements and ideas that can improve the applicability of machine learning and deep learning approaches to clinical settings by making them robust and consistent across different domains. MIL3ID accepted 16 papers out of 43 submissions for publication, dealing with best practices in medical image learning with label scarcity and data imperfection. . Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]

Interpretable and Annotation-Efficient Learning for Medical Image Computing / Cardoso, Jaime ; Van Nguyen, Hien ; Heller, Nicholas ; Henriques Abreu, Pedro ; Isgum, Ivana ; Silva, Wilson ; Cruz, Ricardo ; Pereira Amorim, Jose ; Patel, Vishal ; Roysam, Badri ; Zhou, Kevin ; Jiang, Steve ; Le, Ngan ; Luu, Khoa ; Sznitman, Raphael ; Cheplygina, Veronika ; Mateus, Diana ; Trucco, Emanuele ; Abbasi, Samaneh  

Público ISBD Ningún comentario para este registro. Título : Interpretable and Annotation-Efficient Learning for Medical Image Computing : Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3ID 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings / Tipo de documento: documento electrónico Autores: Cardoso, Jaime, ; Van Nguyen, Hien, ; Heller, Nicholas, ; Henriques Abreu, Pedro, ; Isgum, Ivana, ; Silva, Wilson, ; Cruz, Ricardo, ; Pereira Amorim, Jose, ; Patel, Vishal, ; Roysam, Badri, ; Zhou, Kevin, ; Jiang, Steve, ; Le, Ngan, ; Luu, Khoa, ; Sznitman, Raphael, ; Cheplygina, Veronika, ; Mateus, Diana, ; Trucco, Emanuele, ; Abbasi, Samaneh, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2020 Número de páginas: XVII, 292 p. 109 ilustraciones ISBN/ISSN/DL: 978-3-030-61166-8 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Visión por computador  Inteligencia artificial  Biología Computacional y de Sistemas  Aplicación informática en ciencias sociales y del comportamiento.  Reconocimiento de patrones automatizado  Sistemas de reconocimiento de patrones  Bioinformática  Ciencias sociales  Procesamiento de datos Clasificación: 006.3 Resumen: Este libro constituye las actas conjuntas arbitradas del Tercer Taller Internacional sobre Interpretabilidad de la Inteligencia Artificial en Computación de Imágenes Médicas, iMIMIC 2020, el Segundo Taller Internacional sobre Aprendizaje de Imágenes Médicas con Menos Etiquetas y Datos Imperfectos, MIL3ID 2020, y el Quinto Taller Internacional sobre Aprendizaje de Imágenes Médicas con Menos Etiquetas y Datos Imperfectos, MIL3ID 2020. Anotación a escala de datos biomédicos y síntesis de etiquetas de expertos, LABELS 2020, celebrada junto con la 23.ª Conferencia Internacional sobre Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2020, en Lima, Perú, en octubre de 2020. Los 8 artículos completos presentados en iMIMIC 2020, 11 artículos completos para MIL3ID 2020 y los 10 artículos completos presentados en LABELS 2020 fueron cuidadosamente revisados ​​y seleccionados entre 16 presentaciones para iMIMIC, 28 para MIL3ID y 12 presentaciones para LABELS. Los artículos de iMIMIC se centran en presentar los desafíos y oportunidades relacionados con el tema de la interpretabilidad de los sistemas de aprendizaje automático en el contexto de las imágenes médicas y la intervención asistida por computadora. MIL3ID aborda las mejores prácticas en el aprendizaje de imágenes médicas con escasez de etiquetas e imperfección de datos. Los artículos de LABELS presentan una variedad de enfoques para abordar un número limitado de etiquetas, desde el aprendizaje semisupervisado hasta el crowdsourcing. Nota de contenido: iMIMIC 2020 -- Assessing attribution maps for explaining CNN-based vertebral fracture classifiers -- Projective Latent Interventions for Understanding and Fine-tuning Classifiers -- Interpretable CNN Pruning for Preserving Scale-Covariant Features in Medical Imaging -- Improving the Performance and Explainability of Mammogram Classifiers with Local Annotations -- Improving Interpretability for Computer-aided Diagnosis tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-based Explanations -- Explainable Disease Classification via weakly-supervised segmentation -- Reliable Saliency Maps for Weakly-Supervised Localization of Disease Patterns -- Explainability for regression CNN in fetal head circumference estimation from ultrasound images -- MIL3ID 2020 -- Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins -- Semi-supervised Instance Segmentation with a Learned Shape Prior -- COMe-SEE: Cross-Modality Semantic Embedding Ensemble for Generalized Zero-Shot Diagnosis of Chest Radiographs -- Semi-supervised Machine Learning with MixMatch and Equivalence Classes -- Non-contrast CT Liver Segmentation using CycleGAN Data Augmentation from Contrast Enhanced CT -- Uncertainty Estimation in Medical Image Localization: Towards Robust Anterior Thalamus Targeting for Deep Brain Stimulation -- A Case Study of Transfer of Lesion-Knowledge -- Transfer Learning With Joint Optimization for Label-Efficient Medical Image Anomaly Detection -- Unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-Domain Liver Segmentation -- HydraMix-Net: A Deep Multi-task Semi-supervised Learning Approach for Cell Detection and Classification -- Semi-supervised classification of chest radiographs -- LABELS 2020 -- Risk of training diagnostic algorithms on data with demographic bias -- Semi-Weakly Supervised Learning for Prostate Cancer Image Classification with Teacher-Student Deep Convolutional Networks -- Are pathologist-defined labels reproducible? Comparison of the TUPAC16 mitotic figure dataset with an alternative set of labels -- EasierPath: An Open-source Tool for Human-in-the-loop Deep Learning of Renal Pathology -- Imbalance-Effective Active Learning in Nucleus, Lymphocyte and Plasma Cell Detection -- Labeling of Multilingual Breast MRI Reports -- Predicting Scores of Medical Imaging Segmentation Methods with Meta-Learning -- Labelling imaging datasets on the basis of neuroradiology reports: a validation study -- Semi-Supervised Learning for Instrument Detection with a Class Imbalanced Dataset -- Paying Per-label Attention for Multi-label Extraction from Radiology Reports. Tipo de medio : Computadora Summary : This book constitutes the refereed joint proceedings of the Third International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2020, the Second International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2020, and the 5th International Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis, LABELS 2020, held in conjunction with the 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The 8 full papers presented at iMIMIC 2020, 11 full papers to MIL3ID 2020, and the 10 full papers presented at LABELS 2020 were carefully reviewed and selected from 16 submissions to iMIMIC, 28 to MIL3ID, and 12 submissions to LABELS. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. MIL3ID deals with best practices in medical image learning with label scarcity and data imperfection. The LABELS papers present a variety of approaches for dealing with a limited number of labels, from semi-supervised learning to crowdsourcing. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Interpretable and Annotation-Efficient Learning for Medical Image Computing : Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3ID 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings / [documento electrónico] / Cardoso, Jaime, ; Van Nguyen, Hien, ; Heller, Nicholas, ; Henriques Abreu, Pedro, ; Isgum, Ivana, ; Silva, Wilson, ; Cruz, Ricardo, ; Pereira Amorim, Jose, ; Patel, Vishal, ; Roysam, Badri, ; Zhou, Kevin, ; Jiang, Steve, ; Le, Ngan, ; Luu, Khoa, ; Sznitman, Raphael, ; Cheplygina, Veronika, ; Mateus, Diana, ; Trucco, Emanuele, ; Abbasi, Samaneh,  . -  1 ed.  . - [s.l.] : Springer, 2020 . - XVII, 292 p. 109 ilustraciones. ISBN : 978-3-030-61166-8 Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Visión por computador  Inteligencia artificial  Biología Computacional y de Sistemas  Aplicación informática en ciencias sociales y del comportamiento.  Reconocimiento de patrones automatizado  Sistemas de reconocimiento de patrones  Bioinformática  Ciencias sociales  Procesamiento de datos Clasificación: 006.3 Resumen: Este libro constituye las actas conjuntas arbitradas del Tercer Taller Internacional sobre Interpretabilidad de la Inteligencia Artificial en Computación de Imágenes Médicas, iMIMIC 2020, el Segundo Taller Internacional sobre Aprendizaje de Imágenes Médicas con Menos Etiquetas y Datos Imperfectos, MIL3ID 2020, y el Quinto Taller Internacional sobre Aprendizaje de Imágenes Médicas con Menos Etiquetas y Datos Imperfectos, MIL3ID 2020. Anotación a escala de datos biomédicos y síntesis de etiquetas de expertos, LABELS 2020, celebrada junto con la 23.ª Conferencia Internacional sobre Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2020, en Lima, Perú, en octubre de 2020. Los 8 artículos completos presentados en iMIMIC 2020, 11 artículos completos para MIL3ID 2020 y los 10 artículos completos presentados en LABELS 2020 fueron cuidadosamente revisados ​​y seleccionados entre 16 presentaciones para iMIMIC, 28 para MIL3ID y 12 presentaciones para LABELS. Los artículos de iMIMIC se centran en presentar los desafíos y oportunidades relacionados con el tema de la interpretabilidad de los sistemas de aprendizaje automático en el contexto de las imágenes médicas y la intervención asistida por computadora. MIL3ID aborda las mejores prácticas en el aprendizaje de imágenes médicas con escasez de etiquetas e imperfección de datos. Los artículos de LABELS presentan una variedad de enfoques para abordar un número limitado de etiquetas, desde el aprendizaje semisupervisado hasta el crowdsourcing. Nota de contenido: iMIMIC 2020 -- Assessing attribution maps for explaining CNN-based vertebral fracture classifiers -- Projective Latent Interventions for Understanding and Fine-tuning Classifiers -- Interpretable CNN Pruning for Preserving Scale-Covariant Features in Medical Imaging -- Improving the Performance and Explainability of Mammogram Classifiers with Local Annotations -- Improving Interpretability for Computer-aided Diagnosis tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-based Explanations -- Explainable Disease Classification via weakly-supervised segmentation -- Reliable Saliency Maps for Weakly-Supervised Localization of Disease Patterns -- Explainability for regression CNN in fetal head circumference estimation from ultrasound images -- MIL3ID 2020 -- Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins -- Semi-supervised Instance Segmentation with a Learned Shape Prior -- COMe-SEE: Cross-Modality Semantic Embedding Ensemble for Generalized Zero-Shot Diagnosis of Chest Radiographs -- Semi-supervised Machine Learning with MixMatch and Equivalence Classes -- Non-contrast CT Liver Segmentation using CycleGAN Data Augmentation from Contrast Enhanced CT -- Uncertainty Estimation in Medical Image Localization: Towards Robust Anterior Thalamus Targeting for Deep Brain Stimulation -- A Case Study of Transfer of Lesion-Knowledge -- Transfer Learning With Joint Optimization for Label-Efficient Medical Image Anomaly Detection -- Unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-Domain Liver Segmentation -- HydraMix-Net: A Deep Multi-task Semi-supervised Learning Approach for Cell Detection and Classification -- Semi-supervised classification of chest radiographs -- LABELS 2020 -- Risk of training diagnostic algorithms on data with demographic bias -- Semi-Weakly Supervised Learning for Prostate Cancer Image Classification with Teacher-Student Deep Convolutional Networks -- Are pathologist-defined labels reproducible? Comparison of the TUPAC16 mitotic figure dataset with an alternative set of labels -- EasierPath: An Open-source Tool for Human-in-the-loop Deep Learning of Renal Pathology -- Imbalance-Effective Active Learning in Nucleus, Lymphocyte and Plasma Cell Detection -- Labeling of Multilingual Breast MRI Reports -- Predicting Scores of Medical Imaging Segmentation Methods with Meta-Learning -- Labelling imaging datasets on the basis of neuroradiology reports: a validation study -- Semi-Supervised Learning for Instrument Detection with a Class Imbalanced Dataset -- Paying Per-label Attention for Multi-label Extraction from Radiology Reports. Tipo de medio : Computadora Summary : This book constitutes the refereed joint proceedings of the Third International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2020, the Second International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2020, and the 5th International Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis, LABELS 2020, held in conjunction with the 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The 8 full papers presented at iMIMIC 2020, 11 full papers to MIL3ID 2020, and the 10 full papers presented at LABELS 2020 were carefully reviewed and selected from 16 submissions to iMIMIC, 28 to MIL3ID, and 12 submissions to LABELS. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. MIL3ID deals with best practices in medical image learning with label scarcity and data imperfection. The LABELS papers present a variety of approaches for dealing with a limited number of labels, from semi-supervised learning to crowdsourcing. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]

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