Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis

XiaoFeng Liu, Xiong Chang Liu, Bo Hu, Wenxuan Ji, Fangxu Xing, Jun Lu, Jia You, C.C. Jay Kuo, Georges El Fakhri, Jonghye Woo

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Recent advances in unsupervised domain adaptation (UDA) show that transferable prototypical learning presents a powerful means for class conditional alignment, which encourages the closeness of cross-domain class centroids. However, the cross-domain inner-class compactness and the underlying fine-grained subtype structure remained largely underexplored. In this work, we propose to adaptively carry out the fine-grained subtype-aware alignment by explicitly enforcing the class-wise separation and subtype-wise compactness with intermediate pseudo labels. Our key insight is that the unlabeled subtypes of a class can be divergent to one another with different conditional and label shifts, while inheriting the local proximity within a subtype. The cases of with or without the prior information on subtype numbers are investigated to discover the underlying subtype structure in an online fashion. The proposed subtype-aware dynamic UDA achieves promising results on medical diagnosis tasks.
Original languageEnglish
Title of host publicationProc. 35th AAAI Conf. on Artificial Intelligence (AAAI-2021, Virtual Conference)
Pages2187-2197
Number of pages10
Publication statusPublished - 2021

Publication series

NameProc. 35th AAAI Conf. on Artificial Intelligence (AAAI-2021, Virtual Conference
PublisherAAAI
Number3
Volume35

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