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, Proceedings

Albarqouni, Shadi (EDT); Bakas, Spyridon (EDT); Kamnitsas, Konstantinos (EDT); Cardoso, M. Jorge (EDT); Landman, Bennett (EDT)

ISBN 10: 3030605477 / ISBN 13: 9783030605476

Published by Springer, 2020

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Seller Inventory # 41944401-n

Bibliographic Details

• Title: 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, Proceedings
• Publisher: Springer
• Publication Date: 2020
• Language: English
• ISBN 10: 3030605477
• ISBN 13: 9783030605476
• Binding: Soft cover
• Condition: New

About this title

Synopsis

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.