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Titlebook: Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning; Second MICCAI Worksh Shadi Albarqouni,Spyridon B

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书目名称Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning
副标题Second MICCAI Worksh
编辑Shadi Albarqouni,Spyridon Bakas,Ziyue Xu
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning; Second MICCAI Worksh Shadi Albarqouni,Spyridon B
描述.This book constitutes the refereed proceedings of the Second MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2020, and the First MICCAI Workshop on Distributed and Collaborative Learning, DCL 2020, held in conjunction with MICCAI 2020 in October 2020. The conference was planned to take place in Lima, Peru, but changed to an online format due to the Coronavirus pandemic. ..For DART 2020, 12 full papers were accepted from 18 submissions. They deal with 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..For DCL 2020, the 8 papers included in this book were accepted from a total of 12 submissions. They focus on the comparison, evaluation and discussion of methodological advancement and practical ideas about machine learning applied to problems where data cannot be stored in centralized databases; where information privacy is a priority; where it is necessary to deliver strong guarantees on the amount and nature of private information that may be revealed by the model as a result of training; and where it
出版日期Conference proceedings 2020
关键词bioinformatics; computer networks; computer security; computer vision; deep learning; education; image ana
版次1
doihttps://doi.org/10.1007/978-3-030-60548-3
isbn_softcover978-3-030-60547-6
isbn_ebook978-3-030-60548-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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Nabanita Mukhopadhyay,Paramita De results show our approach effectively replaces manual segmentation maps and demonstrate the possibility of obtaining state of the art registration performance in real world cases where manual segmentation maps are unavailable.
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https://doi.org/10.1007/978-3-031-29422-8canner settings. We propose . (.nverse .istance .ggregation), a novel adaptive weighting approach for clients based on meta-information which handles unbalanced and non-iid data. We extensively analyze and evaluate our method against the well-known . approach, Federated Averaging as a baseline.
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Registration of Histopathology Images Using Self Supervised Fine Grained Feature Maps results show our approach effectively replaces manual segmentation maps and demonstrate the possibility of obtaining state of the art registration performance in real world cases where manual segmentation maps are unavailable.
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G. Gupta,R. Shrivastava,J. Khan,N. K. Singhasets for autism detection and healthcare insurance. We compare with two methods and achieve state of the art performance in sensitive information leakage trade-off. A discussion regarding the difficulties of applying fair representation learning to medical data and when it is desirable is presented.
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