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Data Augmentation, Labelling, and Imperfections - Second MICCAI Workshop, DALI 2022, Held in Conjunction with MICCAI 2022,... Data Augmentation, Labelling, and Imperfections - Second MICCAI Workshop, DALI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings (Paperback, 1st ed. 2022)
Hien V. Nguyen, Sharon X. Huang, Yuan Xue
R1,479 Discovery Miles 14 790 Ships in 18 - 22 working days

This book constitutes the refereed proceedings of the Second MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2022, held in conjunction with MICCAI 2022, in Singapore in September 2022.DALI 2022 accepted 12 papers from the 22 submissions that were reviewed. The papers focus on rigorous study of medical data related to machine learning systems.

Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data - First MICCAI... Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data - First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019, Shenzhen, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings (Paperback, 1st ed. 2019)
Qian Wang, Fausto Milletari, Hien V. Nguyen, Shadi Albarqouni, M. Jorge Cardoso, …
R1,408 Discovery Miles 14 080 Ships in 18 - 22 working days

This book constitutes the refereed proceedings of the First MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the First International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. DART 2019 accepted 12 papers for publication out of 18 submissions. The papers deal with methodological advancements and ideas that can improve the applicability of machine learning and deep learning approaches to clinical settings by making them robust and consistent across different domains. MIL3ID accepted 16 papers out of 43 submissions for publication, dealing with best practices in medical image learning with label scarcity and data imperfection.

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