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Books > Computing & IT > Applications of computing > Image processing
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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 (Paperback, 1st ed. 2020)
Loot Price: R1,564
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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 (Paperback, 1st ed. 2020)
Series: Image Processing, Computer Vision, Pattern Recognition, and Graphics, 12446
Expected to ship within 10 - 15 working days
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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.
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