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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 2: AAAI-22 Technical Tracks 2

Single-Domain Generalization in Medical Image Segmentation via Test-Time Adaptation from Shape Dictionary

February 1, 2023

Authors

Quande Liu

Department of Computer Science and Engineering, The Chinese University of Hong Kong


Cheng Chen

Department of Computer Science and Engineering, The Chinese University of Hong Kong


Qi Dou

Department of Computer Science and Engineering, The Chinese University of Hong Kong


Pheng-Ann Heng

Department of Computer Science and Engineering, The Chinese University of Hong Kong; Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences


Proceedings:

No. 2: AAAI-22 Technical Tracks 2

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Technical Track on Computer Vision II

Downloads:

Download PDF

Abstract:

Domain generalization typically requires data from multiple source domains for model learning. However, such strong assumption may not always hold in practice, especially in medical field where the data sharing is highly concerned and sometimes prohibitive due to privacy issue. This paper studies the important yet challenging single domain generalization problem, in which a model is learned under the worst-case scenario with only one source domain to directly generalize to different unseen target domains. We present a novel approach to address this problem in medical image segmentation, which extracts and integrates the semantic shape prior information of segmentation that are invariant across domains and can be well-captured even from single domain data to facilitate segmentation under distribution shifts. Besides, a test-time adaptation strategy with dual-consistency regularization is further devised to promote dynamic incorporation of these shape priors under each unseen domain to improve model generalizability. Extensive experiments on two medical image segmentation tasks demonstrate the consistent improvements of our method across various unseen domains, as well as its superiority over state-of-the-art approaches in addressing domain generalization under the worst-case scenario.

DOI:

10.1609/aaai.v36i2.20068


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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