Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data / Lecture Notes in Computer Science Bd.11795 (PDF)
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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.
- 2019, 1st ed. 2019, 254 Seiten, Englisch
- Herausgegeben: Qian Wang, Badri Roysam, Steve Jiang, Kevin Zhou, Khoa Luu, Ngan Le, Fausto Milletari, Hien V. Nguyen, Shadi Albarqouni, M. Jorge Cardoso, Nicola Rieke, Ziyue Xu, Konstantinos Kamnitsas, Vishal Patel
- Verlag: Springer-Verlag GmbH
- ISBN-10: 3030333914
- ISBN-13: 9783030333911
- Erscheinungsdatum: 13.10.2019
Abhängig von Bildschirmgrösse und eingestellter Schriftgrösse kann die Seitenzahl auf Ihrem Lesegerät variieren.
- Dateiformat: PDF
- Grösse: 41 MB
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