Fed-BioMed: A General Open-Source Frontend Framework for Federated Learning in Healthcare
While data in healthcare is produced in quantities never imagined before, the feasibility of clinical studies is often hindered by the problem of data access and transfer, especially regarding privacy concerns. Federated learning allows privacy-preserving
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Shadi Albarqouni · Spyridon Bakas · Konstantinos Kamnitsas et al. (Eds.)
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, 2020, Proceedings
Lecture Notes in Computer Science Founding Editors Gerhard Goos Karlsruhe Institute of Technology, Karlsruhe, Germany Juris Hartmanis Cornell University, Ithaca, NY, USA
Editorial Board Members Elisa Bertino Purdue University, West Lafayette, IN, USA Wen Gao Peking University, Beijing, China Bernhard Steffen TU Dortmund University, Dortmund, Germany Gerhard Woeginger RWTH Aachen, Aachen, Germany Moti Yung Columbia University, New York, NY, USA
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More information about this series at http://www.springer.com/series/7412
Shadi Albarqouni Spyridon Bakas Konstantinos Kamnitsas M. Jorge Cardoso Bennett Landman Wenqi Li Fausto Milletari Nicola Rieke Holger Roth Daguang Xu Ziyue Xu (Eds.) •
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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, 2020 Proceedings
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Editors Shadi Albarqouni Technical University Munich Munich, Germany
Spyridon Bakas University of Pennsylvania Philadelphia, PA, USA
Konstantinos Kamnitsas Imperial College London London, UK
M. Jorge Cardoso King’s College London London, UK
Bennett Landman Vanderbilt University Nashville, TN, USA
Wenqi Li NVIDIA Ltd. Cambridge, UK
Fausto Milletari NVIDIA GmbH and Johnson & Johnson Munich, Germany
Nicola Rieke NVIDIA GmbH Munich, Germany
Holger Roth NVIDIA Corporation Bethesda, MD, USA
Daguang Xu NVIDIA Corporation Bethesda, MD, USA
Ziyue Xu NVIDIA Corporation Santa Clara, CA, USA
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-030-60547-6 ISBN 978-3-030-60548-3 (eBook) https://doi.org/10.1007/978-3-030-60548-3 LNCS Sublibrary: SL6 – Image Processing, Computer Vision, Pattern Recognition, and Graphics © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and
