Interpretable Deep Neural Network to Predict Estrogen Receptor Status from Haematoxylin-Eosin Images
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State-of-the-Art Survey
Andreas Holzinger Randy Goebel Michael Mengel Heimo Müller (Eds.)
Artificial Intelligence and Machine Learning for Digital Pathology State-of-the-Art and Future Challenges
Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science
Series Editors Randy Goebel University of Alberta, Edmonton, Canada Yuzuru Tanaka Hokkaido University, Sapporo, Japan Wolfgang Wahlster DFKI and Saarland University, Saarbrücken, Germany
Founding Editor Jörg Siekmann DFKI and Saarland University, Saarbrücken, Germany
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More information about this series at http://www.springer.com/series/1244
Andreas Holzinger Randy Goebel Michael Mengel Heimo Müller (Eds.) •
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Artificial Intelligence and Machine Learning for Digital Pathology State-of-the-Art and Future Challenges
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Editors Andreas Holzinger Medical University of Graz Graz, Austria University of Alberta Edmonton, AB, Canada Michael Mengel University of Alberta Edmonton, AB, Canada
Randy Goebel University of Alberta Edmonton, AB, Canada Heimo Müller Medical University of Graz Graz, Austria
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Artificial Intelligence ISBN 978-3-030-50401-4 ISBN 978-3-030-50402-1 (eBook) https://doi.org/10.1007/978-3-030-50402-1 LNCS Sublibrary: SL7 – Artificial Intelligence © 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 Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
The work of pathologists is interesting for fundamental research in Artificial Intelligence (AI) and Machine Learning (ML) for several reasons: 1) digital pathology is not just the transformation of the classical microscopic analysis to a digital visualization, it is a disruptive innovation tha
