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Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health

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Produktnummer: 180dd0e81fa7f6488996e7f4e6248789d0
Themengebiete: artificial intelligence bioinformatics color image processing computer vision deep learning image analysis image processing image reconstruction image segmentation imaging systems
Veröffentlichungsdatum: 24.09.2021
EAN: 9783030877217
Sprache: Englisch
Seitenzahl: 264
Produktart: Kartoniert / Broschiert
Herausgeber: Albarqouni, Shadi Cardoso, M. Jorge Dou, Qi Kamnitsas, Konstantinos Khanal, Bishesh Rekik, Islem Rieke, Nicola Sheet, Debdoot Tsaftaris, Sotirios Xu, Daguang Xu, Ziyue
Verlag: Springer International Publishing
Untertitel: Third MICCAI Workshop, DART 2021, and First MICCAI Workshop, FAIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021, Proceedings
Produktinformationen "Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health"
This book constitutes the refereed proceedings of the Third MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2021, and the First MICCAI Workshop on Affordable Healthcare and AI for Resource Diverse Global Health, FAIR 2021, held in conjunction with MICCAI 2021, in September/October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic.DART 2021 accepted 13 papers from the 21 submissions received. The workshop aims at creating a discussion forum to compare, evaluate, and discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical setting by making them robust and consistent across different domains. For FAIR 2021, 10 papers from 17 submissions were accepted for publication. They focus on Image-to-Image Translation particularly for low-dose or low-resolution settings; Model Compactness and Compression; Domain Adaptation and Transfer Learning; Active, Continual and Meta-Learning.

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