Editors:
Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12446)
Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)
Conference series link(s): iMIMIC: International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, LABELS: International Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis, MIL3ID: International Workshop on Medical Image Learning with Less Labels and Imperfect Data
Conference proceedings info: IMIMIC 2020, LABELS 2020, MIL3ID 2020.
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Table of contents (30 papers)
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Front Matter
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iMIMIC 2020
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Front Matter
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MIL3ID 2020
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Front Matter
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Other Volumes
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Interpretable and Annotation-Efficient Learning for Medical Image Computing
About this book
This book constitutes the refereed joint proceedings of the Third International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2020, the Second International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2020, and the 5th International Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis, LABELS 2020, held in conjunction with the 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020.
The 8 full papers presented at iMIMIC 2020, 11 full papers to MIL3ID 2020, and the 10 full papers presented at LABELS 2020 were carefully reviewed and selected from 16 submissions to iMIMIC, 28 to MIL3ID, and 12 submissions to LABELS. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. MIL3ID deals with best practices in medical image learning with label scarcity and data imperfection. The LABELS papers present a variety of approaches for dealing with a limited number of labels, from semi-supervised learning to crowdsourcing.
Editors and Affiliations
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University of Porto, Porto, Portugal
Jaime Cardoso, Wilson Silva, Ricardo Cruz
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University of Houston, Houston, USA
Hien Van Nguyen, Badri Roysam
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University of Minnesota, Minneapolis, USA
Nicholas Heller
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University of Coimbra, Coimbra, Portugal
Pedro Henriques Abreu, Jose Pereira Amorim
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Amsterdam University Medical Center, Amsterdam, The Netherlands
Ivana Isgum
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Johns Hopkins University, Baltimore, USA
Vishal Patel
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Chinese Academy of Sciences, Beijing, China
Kevin Zhou
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UT Southwestern Medical Center, Dallas, USA
Steve Jiang
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University of Arkansas, Fayetteville, USA
Ngan Le, Khoa Luu
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University of Bern, Bern, Switzerland
Raphael Sznitman
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Eindhoven University of Technology, Eindhoven, The Netherlands
Veronika Cheplygina, Samaneh Abbasi
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Technical University of Munich, Nantes, Germany
Diana Mateus
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University of Dundee, Dundee, UK
Emanuele Trucco
Bibliographic Information
Book Title: Interpretable and Annotation-Efficient Learning for Medical Image Computing
Book Subtitle: 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
Editors: Jaime Cardoso, Hien Van Nguyen, Nicholas Heller, Pedro Henriques Abreu, Ivana Isgum, Wilson Silva, Ricardo Cruz, Jose Pereira Amorim, Vishal Patel, Badri Roysam, Kevin Zhou, Steve Jiang, Ngan Le, Khoa Luu, Raphael Sznitman, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, … Samaneh Abbasi
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/978-3-030-61166-8
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Softcover ISBN: 978-3-030-61165-1Published: 04 October 2020
eBook ISBN: 978-3-030-61166-8Published: 03 October 2020
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: XVII, 292
Number of Illustrations: 109 b/w illustrations
Topics: Artificial Intelligence, Image Processing and Computer Vision, Computer Appl. in Social and Behavioral Sciences, Computational Biology/Bioinformatics, Pattern Recognition