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Autor Kamnitsas, Konstantinos |
Documentos disponibles escritos por este autor (3)
Crear una solicitud de compra Refinar búsquedaDomain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health / Albarqouni, Shadi ; Cardoso, M. Jorge ; Dou, Qi ; Kamnitsas, Konstantinos ; Khanal, Bishesh ; Rekik, Islem ; Rieke, Nicola ; Sheet, Debdoot ; Tsaftaris, Sotirios ; Xu, Daguang ; Xu, Ziyue
TÃtulo : Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health : Third MICCAI Workshop, DART 2021, and First MICCAI Workshop, FAIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021, Proceedings Tipo de documento: documento electrónico Autores: Albarqouni, Shadi, ; Cardoso, M. Jorge, ; Dou, Qi, ; Kamnitsas, Konstantinos, ; Khanal, Bishesh, ; Rekik, Islem, ; Rieke, Nicola, ; Sheet, Debdoot, ; Tsaftaris, Sotirios, ; Xu, Daguang, ; Xu, Ziyue, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2021 Número de páginas: XV, 264 p. 95 ilustraciones, 90 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-87722-4 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 Bioinformática Informática Médica BiologÃa Computacional y de Sistemas Informática de la Salud Clasificación: 006.37 Resumen: Este libro constituye las actas arbitradas del Tercer Taller MICCAI sobre Adaptación de Dominios y Transferencia de Representación, DART 2021, y el Primer Taller MICCAI sobre Atención Médica Asequible e IA para la Salud Global con Diversidad de Recursos, FAIR 2021, celebrado junto con MICCAI 2021, en septiembre/octubre de 2021. Los talleres estaban previstos para realizarse en Estrasburgo, Francia, pero se realizaron de forma virtual debido a la pandemia de COVID-19. DART 2021 aceptó 13 artÃculos de las 21 presentaciones recibidas. El taller tiene como objetivo crear un foro de debate para comparar, evaluar y discutir los avances metodológicos y las ideas que pueden mejorar la aplicabilidad de los enfoques de aprendizaje automático (ML)/aprendizaje profundo (DL) en el entorno clÃnico al hacerlos robustos y consistentes en diferentes dominios. Para FAIR 2021, se aceptaron para su publicación 10 artÃculos de 17 presentaciones. Se centran en la traducción de imagen a imagen, en particular para entornos de baja dosis o baja resolución; la compacidad y compresión del modelo; Adaptación de dominio y aprendizaje por transferencia; aprendizaje activo, continuo y metaaprendizaje. Nota de contenido: Domain Adaptation and Representation Transfer -- A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis -- Self-supervised Multi-scale Consistency for Weakly Supervised Segmentation Learning -- FDA: Feature Decomposition and Aggregation for Robust Airway Segmentation -- Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation -- Self-Supervised Multimodal Generalized Zero Shot Learning For Gleason Grading -- Self-Supervised Learning of Inter-Label Geometric Relationships For Gleason Grade Segmentation -- Stop Throwing Away Discriminators! Re-using Adversaries for Test-Time Training -- Transductive image segmentation: Self-training and effect of uncertainty estimation -- Unsupervised Domain Adaptation with Semantic Consistency across Heterogeneous Modalities for MRI Prostate Lesion Segmentation -- Cohort Bias Adaptation in Federated Datasets for Lesion Segmentation -- Exploring Deep Registration Latent Spaces -- Learning from Partially Overlapping Labels: Image Segmentation under Annotation Shift -- Unsupervised Domain Adaption via Similarity-based Prototypes for Cross-Modality Segmentation -- A ordable AI and Healthcare -- Classification and Generation of Microscopy Images with Plasmodium Falciparum via Arti cial Neural Networks using Low Cost Settings -- Contrast and Resolution Improvement of POCUS Using Self-Consistent CycleGAN -- Low-Dose Dynamic CT Perfusion Denoising without Training Data -- Recurrent Brain Graph Mapper for Predicting Time-Dependent Brain Graph Evaluation Trajectory -- COVID-Net US: A Tailored, Highly Efficient, Self-Attention Deep Convolutional Neural Network Design for Detection of COVID-19Patient Cases from Point-of-care Ultrasound Imaging -- Inter-Domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student Learning -- Sickle Cell Disease Severity Prediction from Percoll Gradient Images using Graph Convolutional Networks -- Continual Domain Incremental Learning for Chest X-ray Classificationin Low-Resource Clinical Settings -- Deep learning based Automatic detection of adequately positioned mammograms -- Can non-specialists provide high quality Gold standard labels in challenging modalities. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the Third MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2021, and the First MICCAI Workshop on Affordable Healthcare and AI for Resource Diverse Global Health, FAIR 2021, held in conjunction with MICCAI 2021, in September/October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic. DART 2021 accepted 13 papers from the 21 submissions received. The workshop aims at creating a discussion forum to compare, evaluate, and discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical setting by making them robust and consistent across different domains. For FAIR 2021, 10 papers from 17 submissions were accepted for publication. They focus on Image-to-Image Translation particularly for low-dose or low-resolution settings; Model Compactness and Compression; Domain Adaptation and Transfer Learning; Active, Continual and Meta-Learning. . Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health : Third MICCAI Workshop, DART 2021, and First MICCAI Workshop, FAIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021, Proceedings [documento electrónico] / Albarqouni, Shadi, ; Cardoso, M. Jorge, ; Dou, Qi, ; Kamnitsas, Konstantinos, ; Khanal, Bishesh, ; Rekik, Islem, ; Rieke, Nicola, ; Sheet, Debdoot, ; Tsaftaris, Sotirios, ; Xu, Daguang, ; Xu, Ziyue, . - 1 ed. . - [s.l.] : Springer, 2021 . - XV, 264 p. 95 ilustraciones, 90 ilustraciones en color.
ISBN : 978-3-030-87722-4
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 Bioinformática Informática Médica BiologÃa Computacional y de Sistemas Informática de la Salud Clasificación: 006.37 Resumen: Este libro constituye las actas arbitradas del Tercer Taller MICCAI sobre Adaptación de Dominios y Transferencia de Representación, DART 2021, y el Primer Taller MICCAI sobre Atención Médica Asequible e IA para la Salud Global con Diversidad de Recursos, FAIR 2021, celebrado junto con MICCAI 2021, en septiembre/octubre de 2021. Los talleres estaban previstos para realizarse en Estrasburgo, Francia, pero se realizaron de forma virtual debido a la pandemia de COVID-19. DART 2021 aceptó 13 artÃculos de las 21 presentaciones recibidas. El taller tiene como objetivo crear un foro de debate para comparar, evaluar y discutir los avances metodológicos y las ideas que pueden mejorar la aplicabilidad de los enfoques de aprendizaje automático (ML)/aprendizaje profundo (DL) en el entorno clÃnico al hacerlos robustos y consistentes en diferentes dominios. Para FAIR 2021, se aceptaron para su publicación 10 artÃculos de 17 presentaciones. Se centran en la traducción de imagen a imagen, en particular para entornos de baja dosis o baja resolución; la compacidad y compresión del modelo; Adaptación de dominio y aprendizaje por transferencia; aprendizaje activo, continuo y metaaprendizaje. Nota de contenido: Domain Adaptation and Representation Transfer -- A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis -- Self-supervised Multi-scale Consistency for Weakly Supervised Segmentation Learning -- FDA: Feature Decomposition and Aggregation for Robust Airway Segmentation -- Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation -- Self-Supervised Multimodal Generalized Zero Shot Learning For Gleason Grading -- Self-Supervised Learning of Inter-Label Geometric Relationships For Gleason Grade Segmentation -- Stop Throwing Away Discriminators! Re-using Adversaries for Test-Time Training -- Transductive image segmentation: Self-training and effect of uncertainty estimation -- Unsupervised Domain Adaptation with Semantic Consistency across Heterogeneous Modalities for MRI Prostate Lesion Segmentation -- Cohort Bias Adaptation in Federated Datasets for Lesion Segmentation -- Exploring Deep Registration Latent Spaces -- Learning from Partially Overlapping Labels: Image Segmentation under Annotation Shift -- Unsupervised Domain Adaption via Similarity-based Prototypes for Cross-Modality Segmentation -- A ordable AI and Healthcare -- Classification and Generation of Microscopy Images with Plasmodium Falciparum via Arti cial Neural Networks using Low Cost Settings -- Contrast and Resolution Improvement of POCUS Using Self-Consistent CycleGAN -- Low-Dose Dynamic CT Perfusion Denoising without Training Data -- Recurrent Brain Graph Mapper for Predicting Time-Dependent Brain Graph Evaluation Trajectory -- COVID-Net US: A Tailored, Highly Efficient, Self-Attention Deep Convolutional Neural Network Design for Detection of COVID-19Patient Cases from Point-of-care Ultrasound Imaging -- Inter-Domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student Learning -- Sickle Cell Disease Severity Prediction from Percoll Gradient Images using Graph Convolutional Networks -- Continual Domain Incremental Learning for Chest X-ray Classificationin Low-Resource Clinical Settings -- Deep learning based Automatic detection of adequately positioned mammograms -- Can non-specialists provide high quality Gold standard labels in challenging modalities. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the Third MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2021, and the First MICCAI Workshop on Affordable Healthcare and AI for Resource Diverse Global Health, FAIR 2021, held in conjunction with MICCAI 2021, in September/October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic. DART 2021 accepted 13 papers from the 21 submissions received. The workshop aims at creating a discussion forum to compare, evaluate, and discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical setting by making them robust and consistent across different domains. For FAIR 2021, 10 papers from 17 submissions were accepted for publication. They focus on Image-to-Image Translation particularly for low-dose or low-resolution settings; Model Compactness and Compression; Domain Adaptation and Transfer Learning; Active, Continual and Meta-Learning. . Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning : Second MICCAI Workshop, DART 2020, and First MICCAI Workshop, DCL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings / Tipo de documento: documento electrónico Autores: Albarqouni, Shadi, ; Bakas, Spyridon, ; Kamnitsas, Konstantinos, ; Cardoso, M. Jorge, ; Landman, Bennett, ; Li, Wenqi, ; Milletari, Fausto, ; Rieke, Nicola, ; Roth, Holger, ; Xu, Daguang, ; Xu, Ziyue, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2020 Número de páginas: XIII, 212 p. 86 ilustraciones, 67 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-60548-3 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 Ciencias sociales Aprendizaje automático Software de la aplicacion Aplicación informática en ciencias sociales y del comportamiento. Computadoras y Educación Aplicaciones informáticas y de sistemas de información Clasificación: 006.37 Resumen: Este libro constituye las actas arbitradas del Segundo Taller MICCAI sobre Adaptación de Dominio y Transferencia de Representación, DART 2020, y el Primer Taller MICCAI sobre Aprendizaje Distribuido y Colaborativo, DCL 2020, celebrado junto con MICCAI 2020 en octubre de 2020. Se planeó que la conferencia se llevará a cabo en Lima, Perú, pero cambió a un formato en lÃnea debido a la pandemia de Coronavirus. Para DART 2020, se aceptaron 12 artÃculos completos de 18 presentaciones. Se ocupan de avances metodológicos e ideas que pueden mejorar la aplicabilidad de los enfoques de aprendizaje automático (ML)/aprendizaje profundo (DL) en entornos clÃnicos haciéndolos sólidos y consistentes en diferentes dominios. Para DCL 2020, se aceptaron los 8 artÃculos incluidos en este libro de un total de 12 presentaciones. Se centran en la comparación, evaluación y discusión de avances metodológicos e ideas prácticas sobre el aprendizaje automático aplicado a problemas donde los datos no se pueden almacenar en bases de datos centralizadas; donde la privacidad de la información es una prioridad; cuando sea necesario ofrecer garantÃas sólidas sobre la cantidad y la naturaleza de la información privada que el modelo puede revelar como resultado de la capacitación; y donde es necesario orquestar, gestionar y dirigir grupos de nodos que participan en la misma tarea de aprendizaje. Nota de contenido: a-Unet++:A Data-driven Neural Network Architecture for Medical Image Segmentation -- DAPR-Net: Domain Adaptive Predicting-refinement Network for Retinal Vessel Segmentation -- Augmented Radiology: Patient-wise Feature Transfer Model for Glioma Grading -- Attention-Guided Deep Domain Adaptation for Brain Dementia Identication with Multi-Site Neuroimaging Data -- Registration of Histopathology Images Using Self Supervised Fine Grained Feature Maps -- Cross-Modality Segmentation by Self-Supervised Semantic Alignment in Disentangled Content Space -- Semi-supervised Pathology Segmentation with Disentangled Representations -- Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical Imaging -- Parts2Whole: Self-supervised Contrastive Learning via Reconstruction -- Cross-View Label Transfer in Knee MR Segmentation Using Iterative Context Learning -- Continual Class Incremental Learning for CT Thoracic Segmentation -- First U-Net Layers Contain More Domain SpecificInformation Than The Last Ones -- Siloed Federated Learning for Multi-Centric Histopathology Datasets -- On the Fairness of Privacy-Preserving Representations in Medical Applications -- Inverse Distance Aggregation for Federated Learning with Non-IID Data -- Weight Erosion: an Update Aggregation Scheme for Personalized Collaborative Machine Learning -- Federated Gradient Averaging for Multi-Site Training with Momentum-Based Optimizers -- Federated Learning for Breast Density Classification: A Real-World Implementation -- Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning -- Fed-BioMed: A general open-source frontend framework for federated learning in healthcare. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the Second MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2020, and the First MICCAI Workshop on Distributed and Collaborative Learning, DCL 2020, held in conjunction with MICCAI 2020 in October 2020. The conference was planned to take place in Lima, Peru, but changed to an online format due to the Coronavirus pandemic. For DART 2020, 12 full papers were accepted from 18 submissions. They deal with methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical settings by making them robust and consistent across different domains. For DCL 2020, the 8 papers included in this book were accepted from a total of 12 submissions. They focus on the comparison, evaluation and discussion of methodological advancement and practical ideas about machine learning applied to problems where data cannot be stored in centralized databases; where information privacy is a priority; where it is necessary to deliver strong guarantees on the amount and nature of private information that may be revealed by the model as a result of training; and where it's necessary to orchestrate, manage and direct clusters of nodes participating in the same learning task. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning : Second MICCAI Workshop, DART 2020, and First MICCAI Workshop, DCL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings / [documento electrónico] / Albarqouni, Shadi, ; Bakas, Spyridon, ; Kamnitsas, Konstantinos, ; Cardoso, M. Jorge, ; Landman, Bennett, ; Li, Wenqi, ; Milletari, Fausto, ; Rieke, Nicola, ; Roth, Holger, ; Xu, Daguang, ; Xu, Ziyue, . - 1 ed. . - [s.l.] : Springer, 2020 . - XIII, 212 p. 86 ilustraciones, 67 ilustraciones en color.
ISBN : 978-3-030-60548-3
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 Ciencias sociales Aprendizaje automático Software de la aplicacion Aplicación informática en ciencias sociales y del comportamiento. Computadoras y Educación Aplicaciones informáticas y de sistemas de información Clasificación: 006.37 Resumen: Este libro constituye las actas arbitradas del Segundo Taller MICCAI sobre Adaptación de Dominio y Transferencia de Representación, DART 2020, y el Primer Taller MICCAI sobre Aprendizaje Distribuido y Colaborativo, DCL 2020, celebrado junto con MICCAI 2020 en octubre de 2020. Se planeó que la conferencia se llevará a cabo en Lima, Perú, pero cambió a un formato en lÃnea debido a la pandemia de Coronavirus. Para DART 2020, se aceptaron 12 artÃculos completos de 18 presentaciones. Se ocupan de avances metodológicos e ideas que pueden mejorar la aplicabilidad de los enfoques de aprendizaje automático (ML)/aprendizaje profundo (DL) en entornos clÃnicos haciéndolos sólidos y consistentes en diferentes dominios. Para DCL 2020, se aceptaron los 8 artÃculos incluidos en este libro de un total de 12 presentaciones. Se centran en la comparación, evaluación y discusión de avances metodológicos e ideas prácticas sobre el aprendizaje automático aplicado a problemas donde los datos no se pueden almacenar en bases de datos centralizadas; donde la privacidad de la información es una prioridad; cuando sea necesario ofrecer garantÃas sólidas sobre la cantidad y la naturaleza de la información privada que el modelo puede revelar como resultado de la capacitación; y donde es necesario orquestar, gestionar y dirigir grupos de nodos que participan en la misma tarea de aprendizaje. Nota de contenido: a-Unet++:A Data-driven Neural Network Architecture for Medical Image Segmentation -- DAPR-Net: Domain Adaptive Predicting-refinement Network for Retinal Vessel Segmentation -- Augmented Radiology: Patient-wise Feature Transfer Model for Glioma Grading -- Attention-Guided Deep Domain Adaptation for Brain Dementia Identication with Multi-Site Neuroimaging Data -- Registration of Histopathology Images Using Self Supervised Fine Grained Feature Maps -- Cross-Modality Segmentation by Self-Supervised Semantic Alignment in Disentangled Content Space -- Semi-supervised Pathology Segmentation with Disentangled Representations -- Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical Imaging -- Parts2Whole: Self-supervised Contrastive Learning via Reconstruction -- Cross-View Label Transfer in Knee MR Segmentation Using Iterative Context Learning -- Continual Class Incremental Learning for CT Thoracic Segmentation -- First U-Net Layers Contain More Domain SpecificInformation Than The Last Ones -- Siloed Federated Learning for Multi-Centric Histopathology Datasets -- On the Fairness of Privacy-Preserving Representations in Medical Applications -- Inverse Distance Aggregation for Federated Learning with Non-IID Data -- Weight Erosion: an Update Aggregation Scheme for Personalized Collaborative Machine Learning -- Federated Gradient Averaging for Multi-Site Training with Momentum-Based Optimizers -- Federated Learning for Breast Density Classification: A Real-World Implementation -- Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning -- Fed-BioMed: A general open-source frontend framework for federated learning in healthcare. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the Second MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2020, and the First MICCAI Workshop on Distributed and Collaborative Learning, DCL 2020, held in conjunction with MICCAI 2020 in October 2020. The conference was planned to take place in Lima, Peru, but changed to an online format due to the Coronavirus pandemic. For DART 2020, 12 full papers were accepted from 18 submissions. They deal with methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical settings by making them robust and consistent across different domains. For DCL 2020, the 8 papers included in this book were accepted from a total of 12 submissions. They focus on the comparison, evaluation and discussion of methodological advancement and practical ideas about machine learning applied to problems where data cannot be stored in centralized databases; where information privacy is a priority; where it is necessary to deliver strong guarantees on the amount and nature of private information that may be revealed by the model as a result of training; and where it's necessary to orchestrate, manage and direct clusters of nodes participating in the same learning task. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : 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 artÃculos para publicación de 18 presentaciones. Los artÃculos abordan avances metodológicos e ideas que pueden mejorar la aplicabilidad del aprendizaje automático y los enfoques de aprendizaje profundo en entornos clÃnicos haciéndolos sólidos y consistentes en diferentes dominios. MIL3ID aceptó 16 artÃculos 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 artÃculos para publicación de 18 presentaciones. Los artÃculos abordan avances metodológicos e ideas que pueden mejorar la aplicabilidad del aprendizaje automático y los enfoques de aprendizaje profundo en entornos clÃnicos haciéndolos sólidos y consistentes en diferentes dominios. MIL3ID aceptó 16 artÃculos 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 [...]
