Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis
Keywords:Applications, Healthcare, Medicine & Wellness, Classification and Regression, Clustering
AbstractRecent 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 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 a medical diagnosis task.
How to Cite
Liu, X., Liu, X., Hu, B., Ji, W., Xing, F., Lu, J., You, J., Kuo, C.-C. J., El Fakhri, G., & Woo, J. (2021). Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis. Proceedings of the AAAI Conference on Artificial Intelligence, 35(3), 2189-2197. https://doi.org/10.1609/aaai.v35i3.16317
AAAI Technical Track on Computer Vision II