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

Shadi Albarqouni, M. Jorge Cardoso, Qi Dou, Konstantinos Kamnitsas, Nicola Rieke, Sotirios Tsaftaris, Daguang Xu, Ziyue Xu

Research output: Chapter in Book/Report/Conference proceedingForeword/postscript

Abstract / Description of output

Computer vision and medical imaging have been revolutionized by the introduction of advanced machine learning and deep learning methodologies. Recent approaches have shown unprecedented performance gains in tasks such as segmentation, classification, detection, and registration. Although these results (obtained mainly on public datasets) represent important milestones for the MICCAI community, most methods lack generalization capabilities when presented with previously unseen situations (corner cases) or different input data domains. This limits clinical applicability of these innovative approaches and therefore diminishes their impact. Transfer learning, representation learning, and domain adaptation techniques have been used to tackle problems such as model training using small datasets while obtaining generalizable representations; performing domain adaptation via few-shot learning; obtaining interpretable representations that are understood by humans; and leveraging knowledge learned from a particular domain to solve problems in another.
The third MICCAI workshop on Domain Adaptation and Representation Transfer (DART 2021) aimed 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 settings by making them robust and consistent across different domains.
Original languageEnglish
Title of host publicationDomain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health
Subtitle of host publicationThird 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
PublisherSpringer
Pagesv-vi
Volume12968 LNCS
ISBN (Electronic)978-3-030-87722-4
ISBN (Print)978-3-030-87721-7
Publication statusPublished - 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
ISSN (Print)0302-9743

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