Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning

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.