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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: R1,604
Discovery Miles 16 040
Interpretable and Annotation-Efficient Learning for Medical Image Computing - Third International Workshop, iMIMIC 2020, Second...

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)

Jaime Cardoso, Hien Van Nguyen, Nicholas Heller, Pedro Henriques Abreu, Ivana Isgum, Wilson Silva, Ricardo Cruz, Jose Pereira Amorim, Vishal Patel, Badri Roysam

Series: Image Processing, Computer Vision, Pattern Recognition, and Graphics, 12446

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Loot Price R1,604 Discovery Miles 16 040 | Repayment Terms: R150 pm x 12*

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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.

General

Imprint: Springer Nature Switzerland AG
Country of origin: Switzerland
Series: Image Processing, Computer Vision, Pattern Recognition, and Graphics, 12446
Release date: November 2020
First published: 2020
Editors: Jaime Cardoso • Hien Van Nguyen • Nicholas Heller • Pedro Henriques Abreu • Ivana Isgum • Wilson Silva • Ricardo Cruz • Jose Pereira Amorim • Vishal Patel • Badri Roysam
Dimensions: 235 x 155mm (L x W)
Format: Paperback
Pages: 292
Edition: 1st ed. 2020
ISBN-13: 978-3-03-061165-1
Categories: Books > Social sciences > General
Books > Computing & IT > Applications of computing > Pattern recognition
Books > Science & Mathematics > Biology, life sciences > Life sciences: general issues > General
Books > Computing & IT > Applications of computing > Artificial intelligence > General
Books > Computing & IT > Applications of computing > Image processing > General
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LSN: 3-03-061165-5
Barcode: 9783030611651

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