Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis

Autor:	Liu, Xiaofeng, Liu, Xiongchang, Hu, Bo, Ji, Wenxuan, Xing, Fangxu, Lu, Jun, You, Jane, Kuo, C. -C. Jay, Fakhri, Georges El, Woo, Jonghye
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to AAAI 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2101.00318 Zobrazit plný text záznamu View this record from Arxiv