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Titlebook: Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse; Third MICCAI Worksho Shadi Albarqouni

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發(fā)表于 2025-3-21 16:51:15 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse
副標(biāo)題Third MICCAI Worksho
編輯Shadi Albarqouni,M. Jorge Cardoso,Ziyue Xu
視頻videohttp://file.papertrans.cn/283/282483/282483.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse; Third MICCAI Worksho Shadi Albarqouni
描述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...?..?.
出版日期Conference proceedings 2021
關(guān)鍵詞artificial intelligence; bioinformatics; color image processing; computer vision; deep learning; image an
版次1
doihttps://doi.org/10.1007/978-3-030-87722-4
isbn_softcover978-3-030-87721-7
isbn_ebook978-3-030-87722-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

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Francesco Alberti,Antonella Radicchiing aims at optimising machine learning models using weaker forms of annotations, such as scribbles, which are easier and faster to collect. Unfortunately, training with weak labels is challenging and needs regularisation. Herein, we introduce a novel self-supervised multi-scale consistency loss, wh
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Gakwaya P. Isingizwe,Giuseppe T. Cirellal learning aims to train in sequential order, as and when data is available. The main challenge that continual learning methods face is to prevent catastrophic forgetting, i.e., a decrease in performance on the data encountered earlier. This issue makes continuous training of segmentation models for
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https://doi.org/10.1007/978-3-031-23759-1geneous from previous ones. This common medical imaging scenario is rarely considered in the domain adaptation literature, which handles shifts across domains of the same dimensionality. In our work we rely on stochastic generative modeling to translate across two heterogeneous domains at pixel spac
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