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Titlebook: Domain Adaptation and Representation Transfer; 5th MICCAI Workshop, Lisa Koch,M. Jorge Cardoso,Dong Yang Conference proceedings 2024 The Ed

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书目名称Domain Adaptation and Representation Transfer
副标题5th MICCAI Workshop,
编辑Lisa Koch,M. Jorge Cardoso,Dong Yang
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Domain Adaptation and Representation Transfer; 5th MICCAI Workshop, Lisa Koch,M. Jorge Cardoso,Dong Yang Conference proceedings 2024 The Ed
描述This book constitutes the refereed proceedings of the 5th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2023, which was held in conjunction with MICCAI 2023, in October 2023. .The 16 full papers presented in this book were carefully reviewed and selected from 32 submissions. They 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.. .
出版日期Conference proceedings 2024
关键词artificial intelligence; medical imaging; transfer learning; color image processing; domain shift; domain
版次1
doihttps://doi.org/10.1007/978-3-031-45857-6
isbn_softcover978-3-031-45856-9
isbn_ebook978-3-031-45857-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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https://doi.org/10.1007/978-3-031-13913-0itectures. Such approaches, however, typically ignore domain specific peculiarities and lack the ability to generalize outside their training dataset. We observe that, in RGB images, teeth display a weak or unremarkable texture while exhibiting strong boundaries; similarly, in panoramic radiographs
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Vladimir I. Trukhachev,Rafkat S. Gaisin to pre-existing labels. The method involves utilizing a self-training approach by generating pseudo-labels of the target domain data. To do so, a strategy that is based on a smooth transition between domains is implemented where we initially feed easy examples to the network and gradually increase
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Public Spaces in ‘Colonized’ Urban Iberiaassing various modalities, sequences, manufacturers and machines. In this study, we propose a semi-supervised domain adaptation (SSDA) framework for automatically detecting poor quality FLAIR MRIs within a clinical data warehouse. Leveraging a limited number of labelled FLAIR and a large number of l
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