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 [...]

---

1 (1 - 1 / 1) Ver por página : 25 50 100 200