通过频域自适应对齐，提升医学图像分割在小样本下的性能
Adaptive Frequency Domain Alignment Network for Medical image segmentation
- 在频域中动态对齐源域与目标域特征，增强跨域迁移能力
- 在新构建的VITILIGO2025数据集上达到90.9% IoU， DRIVE数据集达82.6% IoU
- 适合缺乏标注数据的医学图像分割场景，尤其适用于皮肤病和眼底血管分析
高质量标注数据对精准分割至关重要，但医学图像标注因耗时耗力而稀缺。为此，我们提出自适应频域对齐网络（AFDAN）——一种新型域适应框架，通过频域特征对齐缓解数据稀缺问题。AFDAN包含三个核心模块：对抗域学习模块实现源域到目标域的特征迁移；源-目标频域融合模块混合跨域频域表征；空间-频域融合模块结合频域与空间特征，进一步提升跨域分割精度。大量实验表明，AFDAN在新构建的VITILIGO2025数据集上实现90.9%的交并比（IoU），在视网膜血管分割基准DRIVE上达到82.6%的竞争力指标，优于现有最先进方法。
原文摘要 · Abstract (English)
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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