Titlebook: Domain Adaptation and Representation Transfer; 4th MICCAI Workshop, Konstantinos Kamnitsas,Lisa Koch,Sotirios Tsaftari Conference proceedin

GLAZE

書目名稱Domain Adaptation and Representation Transfer影響因子(影響力)




書目名稱Domain Adaptation and Representation Transfer影響因子(影響力)學(xué)科排名




書目名稱Domain Adaptation and Representation Transfer網(wǎng)絡(luò)公開度




書目名稱Domain Adaptation and Representation Transfer網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Domain Adaptation and Representation Transfer被引頻次




書目名稱Domain Adaptation and Representation Transfer被引頻次學(xué)科排名




書目名稱Domain Adaptation and Representation Transfer年度引用




書目名稱Domain Adaptation and Representation Transfer年度引用學(xué)科排名




書目名稱Domain Adaptation and Representation Transfer讀者反饋




書目名稱Domain Adaptation and Representation Transfer讀者反饋學(xué)科排名

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擁護者

ROOF

發(fā)芽

,Task-Agnostic Continual Hippocampus Segmentation for?Smooth Population Shifts,adual population shifts. We propose ODEx, a holistic solution that combines out-of-distribution detection with continual learning techniques. Validation on two scenarios of hippocampus segmentation shows that our proposed method reliably maintains performance on earlier tasks without losing plasticity.

Paradox

Conference proceedings 2022orum 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..?.

Paradox

0302-9743 scussion 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..?.978-3-031-16851-2978-3-031-16852-9Series ISSN 0302-9743 Series E-ISSN 1611-3349

殘忍

jocular

Conference proceedings 2022tion with MICCAI 2022, in September 2022.?.DART 2022 accepted 13 papers from the 25 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

Impugn

,Unsupervised Site Adaptation by?Intra-site Variability Alignment,and propose an . method that jointly aligns the intra-site data variability in the source and target sites while training the network on the labeled source site data. We applied our method to several medical MRI image segmentation tasks and show that it consistently outperforms state-of-the-art methods.