DDFP: Data-dependent Frequency Prompt for Source Free Domain Adaptation of Medical Image Segmentation

• URL: http://arxiv.org/abs/2505.09927v1
• Date: Thu, 15 May 2025 03:24:54 GMT
• Title: DDFP: Data-dependent Frequency Prompt for Source Free Domain Adaptation of Medical Image Segmentation
• Authors: Siqi Yin, Shaolei Liu, Manning Wang,
• Abstract summary: Domain adaptation addresses the challenge of model performance degradation caused by domain gaps.<n>Access to labeled source domain data, particularly in medical datasets, can be restricted due to privacy policies.<n>We propose a novel source-free domain adaptation (SFDA) framework to address these challenges.
• Score: 15.107136785491482
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Domain adaptation addresses the challenge of model performance degradation caused by domain gaps. In the typical setup for unsupervised domain adaptation, labeled data from a source domain and unlabeled data from a target domain are used to train a target model. However, access to labeled source domain data, particularly in medical datasets, can be restricted due to privacy policies. As a result, research has increasingly shifted to source-free domain adaptation (SFDA), which requires only a pretrained model from the source domain and unlabeled data from the target domain data for adaptation. Existing SFDA methods often rely on domain-specific image style translation and self-supervision techniques to bridge the domain gap and train the target domain model. However, the quality of domain-specific style-translated images and pseudo-labels produced by these methods still leaves room for improvement. Moreover, training the entire model during adaptation can be inefficient under limited supervision. In this paper, we propose a novel SFDA framework to address these challenges. Specifically, to effectively mitigate the impact of domain gap in the initial training phase, we introduce preadaptation to generate a preadapted model, which serves as an initialization of target model and allows for the generation of high-quality enhanced pseudo-labels without introducing extra parameters. Additionally, we propose a data-dependent frequency prompt to more effectively translate target domain images into a source-like style. To further enhance adaptation, we employ a style-related layer fine-tuning strategy, specifically designed for SFDA, to train the target model using the prompted target domain images and pseudo-labels. Extensive experiments on cross-modality abdominal and cardiac SFDA segmentation tasks demonstrate that our proposed method outperforms existing state-of-the-art methods.

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