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Image Processing, Computer Vision, Pattern Recognition, and Graphics

Interpretable and Annotation-Efficient 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

Editors: Cardoso, J., Van Nguyen, H., Heller, N., Henriques Abreu, P., Isgum, I., Silva, W., Cruz, R., Pereira Amorim, J., Patel, V., Roysam, B., Zhou, K., Jiang, S., Le, N., Luu, K., Sznitman, R., Cheplygina, V., Mateus, D., Trucco, E., Abbasi, S. (Eds.)

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  • ISBN 978-3-030-61166-8
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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.

Table of contents (30 chapters)

Table of contents (30 chapters)
  • Assessing Attribution Maps for Explaining CNN-Based Vertebral Fracture Classifiers

    Pages 3-12

    Yilmaz, Eren Bora (et al.)

  • Projective Latent Interventions for Understanding and Fine-Tuning Classifiers

    Pages 13-22

    Hinterreiter, Andreas (et al.)

  • Interpretable CNN Pruning for Preserving Scale-Covariant Features in Medical Imaging

    Pages 23-32

    Graziani, Mara (et al.)

  • Improving the Performance and Explainability of Mammogram Classifiers with Local Annotations

    Pages 33-42

    Ness, Lior (et al.)

  • Improving Interpretability for Computer-Aided Diagnosis Tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-Based Explanations

    Pages 43-53

    Pirovano, Antoine (et al.)

Buy this book

eBook $54.99
price for Brazil
  • ISBN 978-3-030-61166-8
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $69.99
price for Brazil
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Bibliographic Information

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
Image Processing, Computer Vision, Pattern Recognition, and Graphics
Series Volume
12446
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-61166-8
DOI
10.1007/978-3-030-61166-8
Softcover ISBN
978-3-030-61165-1
Edition Number
1
Number of Pages
XVII, 292
Number of Illustrations
109 b/w illustrations
Topics