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Book synopsis

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.

Book details

Book category Books in German Naturwissenschaften, Medizin, Informatik, Technik Informatik, EDV Informatik

• Full title: Interpretable and Annotation-Efficient Learning for Medical Image Computing
• Author: Badri Roysam, Kevin Zhou, Steve Jiang, Ngan Le, Khoa Luu, Raphael Sznitman, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, Hien van Nguyen, Samaneh Abbasi, Nicholas Heller, Pedro Henriques Abreu
• Language: English
• Binding: Paperback
• Number of pages: 292
• EAN: 9783030611651
• ISBN: 3030611655
• ID: 33266137
• Publisher: Springer Nature Switzerland AG
• Weight: 486 g
• Dimensions: 235 × 155 × 18 mm
• Date of publishing: 04. October 2020

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