Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning: Second Miccai Workshop, Dart 2020, and First Miccai Worksho (Paperback, 2020)

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 Specific Information 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.

M. Jorge Cardoso (엮은이)    정보 더보기

Spyridon Bakas (엮은이)    정보 더보기

Bennett Landman (엮은이)    정보 더보기

Shadi Albarqouni (엮은이)    정보 더보기

Konstantinos Kamnitsas (엮은이)    정보 더보기

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