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 Conjunctio

Cardoso, Jaime (Editor)/ Van Nguyen, Hien (Editor)/ Heller, Nicholas (Editor)/ Abreu, Pedro Henriques (Editor)/ Isgum, Ivana (Editor)

ISBN 10: 3030611655 / ISBN 13: 9783030611651

Published by Springer-Nature New York Inc, 2020

About this Item

316 pages. 9.25x6.10x0.72 inches. In Stock.

Seller Inventory # x-3030611655

Bibliographic Details

• Title: 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 Conjunctio
• Publisher: Springer-Nature New York Inc
• Publication Date: 2020
• Language: English
• ISBN 10: 3030611655
• ISBN 13: 9783030611651
• Binding: Paperback
• Condition: Brand New
• Weight: 0.57 kilograms

About this title

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.