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• W4366779462 endingPage "102825" @default.
• W4366779462 startingPage "102825" @default.
• W4366779462 abstract "Unsupervised domain adaptation is a popular method in medical image analysis, but it can be tricky to make it work: without labels to link the domains, domains must be matched using feature distributions. If there is no additional information, this often leaves a choice between multiple possibilities to map the data that may be equally likely but not equally correct. In this paper we explore the fundamental problems that may arise in unsupervised domain adaptation, and discuss conditions that might still make it work. Focusing on medical image analysis, we argue that images from different domains may have similar class balance, similar intensities, similar spatial structure, or similar textures. We demonstrate how these implicit conditions can affect domain adaptation performance in experiments with synthetic data, MNIST digits, and medical images. We observe that practical success of unsupervised domain adaptation relies on existing similarities in the data, and is anything but guaranteed in the general case. Understanding these implicit assumptions is a key step in identifying potential problems in domain adaptation and improving the reliability of the results." @default.
• W4366779462 created "2023-04-25" @default.
• W4366779462 creator A5023677285 @default.
• W4366779462 creator A5072434846 @default.
• W4366779462 date "2023-07-01" @default.
• W4366779462 modified "2023-10-14" @default.
• W4366779462 title "Unpaired, unsupervised domain adaptation assumes your domains are already similar" @default.
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• W4366779462 doi "https://doi.org/10.1016/j.media.2023.102825" @default.
• W4366779462 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37116296" @default.
• W4366779462 hasPublicationYear "2023" @default.
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