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Medical Image Understanding and Analysis - 26th Annual Conference, MIUA 2022, Cambridge, UK, July 27-29, 2022, Proceedings (Paperback, 1st ed. 2022) Loot Price: R3,215
Discovery Miles 32 150
Medical Image Understanding and Analysis - 26th Annual Conference, MIUA 2022, Cambridge, UK, July 27-29, 2022, Proceedings...

Medical Image Understanding and Analysis - 26th Annual Conference, MIUA 2022, Cambridge, UK, July 27-29, 2022, Proceedings (Paperback, 1st ed. 2022)

Guang Yang, Angelica Aviles-Rivero, Michael Roberts, Carola-Bibiane Schoenlieb

Series: Lecture Notes in Computer Science, 13413

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Loot Price R3,215 Discovery Miles 32 150 | Repayment Terms: R301 pm x 12*

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This book constitutes the refereed proceedings of the 26th Conference on Medical Image Understanding and Analysis, MIUA 2022, held in Cambridge, UK, in July 2022. The 65 full papers presented were carefully reviewed and selected from 95 submissions. They were organized according to following topical sections: biomarker detection; image registration, and reconstruction; image segmentation; generative models, biomedical simulation and modelling; classification; image enhancement, quality assessment, and data privacy; radiomics, predictive models, and quantitative imaging. Chapter "FCN-Transformer Feature Fusion for Polyp Segmentation" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

General

Imprint: Springer International Publishing AG
Country of origin: Switzerland
Series: Lecture Notes in Computer Science, 13413
Release date: July 2022
First published: 2022
Editors: Guang Yang • Angelica Aviles-Rivero • Michael Roberts • Carola-Bibiane Schoenlieb
Dimensions: 235 x 155mm (L x W)
Format: Paperback
Pages: 911
Edition: 1st ed. 2022
ISBN-13: 978-3-03-112052-7
Categories: Books > Computing & IT > General theory of computing > General
Books > Computing & IT > Applications of computing > Artificial intelligence > General
Books > Computing & IT > Applications of computing > Image processing > General
LSN: 3-03-112052-3
Barcode: 9783031120527

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