Información del autor
Autor Xu, Daguang |
Documentos disponibles escritos por este autor (3)
Crear una solicitud de compra Refinar búsquedaClinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning / Oyarzun Laura, Cristina ; Cardoso, M. Jorge ; Rosen-Zvi, Michal ; Kaissis, Georgios ; Linguraru, Marius George ; Shekhar, Raj ; Wesarg, Stefan ; Erdt, Marius ; Drechsler, Klaus ; Chen, Yufei ; Albarqouni, Shadi ; Bakas, Spyridon ; Landman, Bennett ; Rieke, Nicola ; Roth, Holger ; Li, Xiaoxiao ; Xu, Daguang ; Gabrani, Maria ; Konukoglu, Ender ; Guindy, Michal ; Rueckert, Daniel ; Ziller, Alexander ; Usynin, Dmitrii ; Passerat-Palmbach, Jonathan
TÃtulo : Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning : 10th Workshop, CLIP 2021, Second Workshop, DCL 2021, First Workshop, LL-COVID19 2021, and First Workshop and Tutorial, PPML 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021, Proceedings Tipo de documento: documento electrónico Autores: Oyarzun Laura, Cristina, ; Cardoso, M. Jorge, ; Rosen-Zvi, Michal, ; Kaissis, Georgios, ; Linguraru, Marius George, ; Shekhar, Raj, ; Wesarg, Stefan, ; Erdt, Marius, ; Drechsler, Klaus, ; Chen, Yufei, ; Albarqouni, Shadi, ; Bakas, Spyridon, ; Landman, Bennett, ; Rieke, Nicola, ; Roth, Holger, ; Li, Xiaoxiao, ; Xu, Daguang, ; Gabrani, Maria, ; Konukoglu, Ender, ; Guindy, Michal, ; Rueckert, Daniel, ; Ziller, Alexander, ; Usynin, Dmitrii, ; Passerat-Palmbach, Jonathan, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2021 Número de páginas: XXV, 190 p. 78 ilustraciones, 67 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-90874-4 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 Aprendizaje automático Red de computadoras Ciencias sociales Redes de comunicación informática Aplicación informática en ciencias sociales y del comportamiento. Clasificación: 006.37 Resumen: Este libro constituye las actas arbitradas del décimo taller internacional sobre procedimientos clÃnicos basados ​​en imágenes, CLIP 2021, segundo taller MICCAI sobre aprendizaje distribuido y colaborativo, DCL 2021, primer taller MICCAI, LL-COVID19, primer aprendizaje automático seguro y que preserva la privacidad para Taller y tutorial sobre imágenes médicas, PPML 2021, celebrado junto con MICCAI 2021, en octubre de 2021. Estaba previsto que los talleres se llevaran a cabo en Estrasburgo, Francia, pero se llevaron a cabo virtualmente debido a la pandemia de COVID-19. CLIP 2021 aceptó 9 artÃculos de las 13 presentaciones recibidas. Se centra en modelos holÃsticos de pacientes para una atención sanitaria personalizada con el objetivo de acercar los métodos de investigación básica a la práctica clÃnica. Para DCL 2021, se aceptó para publicación 4 artÃculos de 7 presentaciones. Se ocupan del aprendizaje automático aplicado a problemas en los que los datos no se pueden almacenar en bases de datos centralizadas y la privacidad de la información es una prioridad. LL-COVID19 2021 aceptó 2 artÃculos de 3 presentados que trataban sobre el uso de modelos de IA en la práctica clÃnica. Y para PPML 2021, se aceptaron 2 artÃculos de un total de 6 presentaciones, que exploraban el uso de técnicas de privacidad en la comunidad de imágenes médicas. Nota de contenido: Intestine segmentation with small computational cost for diagnosis assistance of ileus and intestinal obstruction -- Generation of Patient-Specific, Ligamentoskeletal, Finite Element Meshes for Scoliosis Correction Planning -- Bayesian Graph Neural Networks For EEG-based Emotion Recognition -- ViTBIS: Vision Transformer for Biomedical Image Segmentation -- Attention-guided pancreatic duct segmentation from abdominal CT volumes -- Development of the Next Generation Hand-Held Doppler With Waveform Phasicity Predictive Capabilities Using Deep Learning -- Learning from mistakes: an error-driven mechanism to improve segmentation performance based on expert feedback -- TMJOAI: an artificial web-based intelligence tool for early diagnosis of the Temporomandibular Joint Osteoarthritis -- COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty -- Multi-task Federated Learning for Heterogeneous Pancreas Segmentation -- Federated Learning in the Cloud for Analysis of Medical Images- Experience with Open Source Frameworks -- On the Fairness of Swarm Learning in Skin Lesion Classification -- Lessons learned from the development and application of medical imaging-based AI technologies for combating COVID-19: why discuss, what next -- The Role of Pleura and Adipose in Lung Ultrasound AI -- DuCN: Dual-children Network for Medical Diagnosis and Similar Case Recommendation towards COVID-19 -- Data imputation and reconstruction of distributed Parkinson's disease clinical assessments: A comparative evaluation of two aggregation algorithms -- Defending Medical Image Diagnostics against Privacy Attacks using Generative Methods: Application to Retinal Diagnostics. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the 10th International Workshop on Clinical Image-Based Procedures, CLIP 2021, Second MICCAI Workshop on Distributed and Collaborative Learning, DCL 2021, First MICCAI Workshop, LL-COVID19, First Secure and Privacy-Preserving Machine Learning for Medical Imaging Workshop and Tutorial, PPML 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic. CLIP 2021 accepted 9 papers from the 13 submissions received. It focuses on holistic patient models for personalized healthcare with the goal to bring basic research methods closer to the clinical practice. For DCL 2021, 4 papers from 7 submissions were accepted for publication. They deal with machine learning applied to problems where data cannot be stored in centralized databases and information privacy is a priority. LL-COVID19 2021 accepted 2 papers out of 3 submissions dealing with the use of AI models in clinical practice. And for PPML 2021, 2 papers were accepted from a total of 6 submissions, exploring the use of privacy techniques in the medical imaging community. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning : 10th Workshop, CLIP 2021, Second Workshop, DCL 2021, First Workshop, LL-COVID19 2021, and First Workshop and Tutorial, PPML 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021, Proceedings [documento electrónico] / Oyarzun Laura, Cristina, ; Cardoso, M. Jorge, ; Rosen-Zvi, Michal, ; Kaissis, Georgios, ; Linguraru, Marius George, ; Shekhar, Raj, ; Wesarg, Stefan, ; Erdt, Marius, ; Drechsler, Klaus, ; Chen, Yufei, ; Albarqouni, Shadi, ; Bakas, Spyridon, ; Landman, Bennett, ; Rieke, Nicola, ; Roth, Holger, ; Li, Xiaoxiao, ; Xu, Daguang, ; Gabrani, Maria, ; Konukoglu, Ender, ; Guindy, Michal, ; Rueckert, Daniel, ; Ziller, Alexander, ; Usynin, Dmitrii, ; Passerat-Palmbach, Jonathan, . - 1 ed. . - [s.l.] : Springer, 2021 . - XXV, 190 p. 78 ilustraciones, 67 ilustraciones en color.
ISBN : 978-3-030-90874-4
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 Aprendizaje automático Red de computadoras Ciencias sociales Redes de comunicación informática Aplicación informática en ciencias sociales y del comportamiento. Clasificación: 006.37 Resumen: Este libro constituye las actas arbitradas del décimo taller internacional sobre procedimientos clÃnicos basados ​​en imágenes, CLIP 2021, segundo taller MICCAI sobre aprendizaje distribuido y colaborativo, DCL 2021, primer taller MICCAI, LL-COVID19, primer aprendizaje automático seguro y que preserva la privacidad para Taller y tutorial sobre imágenes médicas, PPML 2021, celebrado junto con MICCAI 2021, en octubre de 2021. Estaba previsto que los talleres se llevaran a cabo en Estrasburgo, Francia, pero se llevaron a cabo virtualmente debido a la pandemia de COVID-19. CLIP 2021 aceptó 9 artÃculos de las 13 presentaciones recibidas. Se centra en modelos holÃsticos de pacientes para una atención sanitaria personalizada con el objetivo de acercar los métodos de investigación básica a la práctica clÃnica. Para DCL 2021, se aceptó para publicación 4 artÃculos de 7 presentaciones. Se ocupan del aprendizaje automático aplicado a problemas en los que los datos no se pueden almacenar en bases de datos centralizadas y la privacidad de la información es una prioridad. LL-COVID19 2021 aceptó 2 artÃculos de 3 presentados que trataban sobre el uso de modelos de IA en la práctica clÃnica. Y para PPML 2021, se aceptaron 2 artÃculos de un total de 6 presentaciones, que exploraban el uso de técnicas de privacidad en la comunidad de imágenes médicas. Nota de contenido: Intestine segmentation with small computational cost for diagnosis assistance of ileus and intestinal obstruction -- Generation of Patient-Specific, Ligamentoskeletal, Finite Element Meshes for Scoliosis Correction Planning -- Bayesian Graph Neural Networks For EEG-based Emotion Recognition -- ViTBIS: Vision Transformer for Biomedical Image Segmentation -- Attention-guided pancreatic duct segmentation from abdominal CT volumes -- Development of the Next Generation Hand-Held Doppler With Waveform Phasicity Predictive Capabilities Using Deep Learning -- Learning from mistakes: an error-driven mechanism to improve segmentation performance based on expert feedback -- TMJOAI: an artificial web-based intelligence tool for early diagnosis of the Temporomandibular Joint Osteoarthritis -- COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty -- Multi-task Federated Learning for Heterogeneous Pancreas Segmentation -- Federated Learning in the Cloud for Analysis of Medical Images- Experience with Open Source Frameworks -- On the Fairness of Swarm Learning in Skin Lesion Classification -- Lessons learned from the development and application of medical imaging-based AI technologies for combating COVID-19: why discuss, what next -- The Role of Pleura and Adipose in Lung Ultrasound AI -- DuCN: Dual-children Network for Medical Diagnosis and Similar Case Recommendation towards COVID-19 -- Data imputation and reconstruction of distributed Parkinson's disease clinical assessments: A comparative evaluation of two aggregation algorithms -- Defending Medical Image Diagnostics against Privacy Attacks using Generative Methods: Application to Retinal Diagnostics. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the 10th International Workshop on Clinical Image-Based Procedures, CLIP 2021, Second MICCAI Workshop on Distributed and Collaborative Learning, DCL 2021, First MICCAI Workshop, LL-COVID19, First Secure and Privacy-Preserving Machine Learning for Medical Imaging Workshop and Tutorial, PPML 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic. CLIP 2021 accepted 9 papers from the 13 submissions received. It focuses on holistic patient models for personalized healthcare with the goal to bring basic research methods closer to the clinical practice. For DCL 2021, 4 papers from 7 submissions were accepted for publication. They deal with machine learning applied to problems where data cannot be stored in centralized databases and information privacy is a priority. LL-COVID19 2021 accepted 2 papers out of 3 submissions dealing with the use of AI models in clinical practice. And for PPML 2021, 2 papers were accepted from a total of 6 submissions, exploring the use of privacy techniques in the medical imaging community. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
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 [...]
