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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- About this book
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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)
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Assessing Attribution Maps for Explaining CNN-Based Vertebral Fracture Classifiers
Pages 3-12
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Projective Latent Interventions for Understanding and Fine-Tuning Classifiers
Pages 13-22
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Interpretable CNN Pruning for Preserving Scale-Covariant Features in Medical Imaging
Pages 23-32
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Improving the Performance and Explainability of Mammogram Classifiers with Local Annotations
Pages 33-42
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Improving Interpretability for Computer-Aided Diagnosis Tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-Based Explanations
Pages 43-53
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Table of contents (30 chapters)
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Bibliographic Information
- Bibliographic Information
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- 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
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- 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
