Preface

M. Jorge Cardoso, Qi Dou, Mobarakol Islam, Konstantinos Kamnitsas, Lisa Koch, Nicola Rieke, Sotirios Tsaftaris, 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 Medical Image Computing and Computer Assisted Intervention (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 fourth MICCAI workshop on Domain Adaptation and Representation Transfer (DART 2022) 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
Subtitle of host publication4th MICCAI Workshop, DART 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings
PublisherSpringer
Pagesv-vi
Volume13542 LNCS
ISBN (Electronic)978-3-031-16852-9
ISBN (Print)978-3-031-16851-2
DOIs
Publication statusPublished - 20 Sept 2022
Event4th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Duration: 22 Sept 202222 Sept 2022

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

Conference

Conference4th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Country/TerritorySingapore
CitySingapore
Period22/09/2222/09/22

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