Adaptive Frequency Domain Alignment Network for Medical image segmentation

• URL: http://arxiv.org/abs/2512.16393v2
• Date: Sat, 20 Dec 2025 01:50:11 GMT
• Title: Adaptive Frequency Domain Alignment Network for Medical image segmentation
• Authors: Zhanwei Li, Liang Li, Jiawan Zhang,
• Abstract summary: We propose the Adaptive Frequency Domain Alignment Network (AFDAN) to align features in the frequency domain and alleviate data scarcity.<n>AFDAN integrates three core components to enable robust cross-domain knowledge transfer.<n>It achieves an Intersection over Union (IoU) of 90.9% for vitiligo segmentation and an IoU of 82.6% on the retinal vessel segmentation benchmark.
• Score: 12.523227863301988
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-quality annotated data plays a crucial role in achieving accurate segmentation. However, such data for medical image segmentation are often scarce due to the time-consuming and labor-intensive nature of manual annotation. To address this challenge, we propose the Adaptive Frequency Domain Alignment Network (AFDAN)--a novel domain adaptation framework designed to align features in the frequency domain and alleviate data scarcity. AFDAN integrates three core components to enable robust cross-domain knowledge transfer: an Adversarial Domain Learning Module that transfers features from the source to the target domain; a Source-Target Frequency Fusion Module that blends frequency representations across domains; and a Spatial-Frequency Integration Module that combines both frequency and spatial features to further enhance segmentation accuracy across domains. Extensive experiments demonstrate the effectiveness of AFDAN: it achieves an Intersection over Union (IoU) of 90.9% for vitiligo segmentation in the newly constructed VITILIGO2025 dataset and a competitive IoU of 82.6% on the retinal vessel segmentation benchmark DRIVE, surpassing existing state-of-the-art approaches.

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