Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning

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 withmethodological advancements and ideas thatcan improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical settings bymaking 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 andpractical ideas about machine learning applied to problems where data cannot be stored in centralizeddatabases; where information privacy is a priority; where it is necessary to deliver strong guarantees on theamount and nature of private information that may be revealed by the model as a result of training; and whereit's necessary to orchestrate, manage and direct clusters of nodes participating in the same learning task.