ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentation

• URL: http://arxiv.org/abs/2211.11514v1
• Date: Mon, 21 Nov 2022 14:57:04 GMT
• Title: ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentation
• Authors: Shishuai Hu, Zehui Liao, Yong Xia
• Abstract summary: We propose a textbfPrompt learning based textbfSFDA (textbfProSFDA) method for medical image segmentation. Our results indicate that the proposed ProSFDA outperforms substantially other SFDA methods and is even comparable to UDA methods.
• Score: 21.079667938055668
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The domain discrepancy existed between medical images acquired in different situations renders a major hurdle in deploying pre-trained medical image segmentation models for clinical use. Since it is less possible to distribute training data with the pre-trained model due to the huge data size and privacy concern, source-free unsupervised domain adaptation (SFDA) has recently been increasingly studied based on either pseudo labels or prior knowledge. However, the image features and probability maps used by pseudo label-based SFDA and the consistent prior assumption and the prior prediction network used by prior-guided SFDA may become less reliable when the domain discrepancy is large. In this paper, we propose a \textbf{Pro}mpt learning based \textbf{SFDA} (\textbf{ProSFDA}) method for medical image segmentation, which aims to improve the quality of domain adaption by minimizing explicitly the domain discrepancy. Specifically, in the prompt learning stage, we estimate source-domain images via adding a domain-aware prompt to target-domain images, then optimize the prompt via minimizing the statistic alignment loss, and thereby prompt the source model to generate reliable predictions on (altered) target-domain images. In the feature alignment stage, we also align the features of target-domain images and their styles-augmented counterparts to optimize the source model, and hence push the model to extract compact features. We evaluate our ProSFDA on two multi-domain medical image segmentation benchmarks. Our results indicate that the proposed ProSFDA outperforms substantially other SFDA methods and is even comparable to UDA methods. Code will be available at \url{https://github.com/ShishuaiHu/ProSFDA}.

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