Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health

Distributed, Collaborative, and Federated Learning.- Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation .- FedAP: Adaptive Personalization in Federated Learning for Non-IID Data Data Stealing Attack on Medical Images: Is it Safe to Export Networks from Data Lakes? .- Data Stealing Attack on Medical Images: Is it Safe to Export Networks from Data Lakes?.- Can collaborative learning be private, robust and scalable?.- Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation.- Joint Multi Organ and Tumor Segmentation from Partial Labels using Federated Learning.- Fuh, Kensaku Mori, Weichung Wang, Holger R Roth GAN Latent Space Manipulation and Aggregation for Federated Learning in Medical Imaging.- A Specificity-Preserving Generative Model for Federated MRI Translation.- Content-Aware Differential Privacy with Conditional Invertible Neural Networks.- DeMed: A Novel and Efficient Decentralized Learning Framework for Medical Images Classification on Blockchain.- Cluster Based Secure Multi-Party Computation in Federated Learning for Histopathology Images.- Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling.- Towards Real-World Federated Learning in Medical Image Analysis Using Kaapana.- Towards Sparsified Federated Neuroimaging Models via Weight Pruning.- Affordable AI and Healthcare.- Enhancing Portable OCT Image Quality via GANs for AI-Based Eye Disease Detection.- Deep Learning-based Segmentation of Pleural Effusion From Ultrasound Using Coordinate Convolutions.- Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Networks.- LRH-Net: A Multi-Level Knowledge Distillation Approach for Low-Resource Heart Network.