ENGLISH

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

By Raphael Sznitman , Emanuele Trucco , Diana Mateus , Veronika Cheplygina , Steve Jiang , Vishal Patel , Badri Roysam , Kevin Zhou , Khoa Luu , Ngan Le , Nicholas Heller , Jaime Cardoso , Hien Van Nguyen , Pedro Henriques Abreu , Ivana Isgum , Wilson Silva , Ricardo Cruz , Jose Pereira Amorim , Samaneh Abbasi

Book information

Publisher
Springer International Publishing;Springer
Year
2020
ISBN
9783030611651, 9783030611668
DOI
10.1007/978-3-030-61166-8
Language
english
Format
PDF
Filesize
54 MB (56916964 bytes)
Series
Lecture Notes in Computer Science 12446
Edition
1st ed.
Pages
XVII, 292\305
Time added
2021-01-06 05:41:07
Computer Science; Image Processing and Computer Vi

Description

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.

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