Risk of Training Diagnostic Algorithms on Data with Demographic Bias
One of the critical challenges in machine learning applications is to have fair predictions. There are numerous recent examples in various domains that convincingly show that algorithms trained with biased datasets can easily lead to erroneous or discrimi
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Jaime Cardoso · Hien Van Nguyen · Nicholas Heller et al. (Eds.)
Interpretable and AnnotationEfficient Learning for Medical Image Computing Third International Workshop, iMIMIC 2020 Second International Workshop, MIL3iD 2020 and 5th International Workshop, LABELS 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
Jaime Cardoso Hien Van Nguyen Nicholas Heller et al. (Eds.) •
•
Interpretable and AnnotationEfficient Learning for Medical Image Computing Third International Workshop, iMIMIC 2020 Second International Workshop, MIL3iD 2020 and 5th International Workshop, LABELS 2020 Held in Conjunction with MICCAI 2020 Lima, Peru, October 4–8, 2020 Proceedings
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Editors Jaime Cardoso University of Porto Porto, Portugal
Hien Van Nguyen University of Houston Houston, TX, USA
Nicholas Heller University of Minnesota Minneapolis, MN, USA Additional Workshop Editors see next page
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-030-61165-1 ISBN 978-3-030-61166-8 (eBook) https://doi.org/10.1007/978-3-030-61166-8 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 accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzer
