用扩散模型生成无红斑参考图，实现无需标注数据的皮肤红斑分割。
Zero-shot Segmentation of Skin Conditions: Erythema with Edit-Friendly Inversion
- 通过扩散模型生成同一患者无红斑的参考图像，实现图像对齐。
- 仅需少量用户干预，即可在颜色空间中精准识别红斑区域。
- 适合缺乏标注数据的皮肤病辅助诊断，可快速部署于临床场景。
本研究提出一种零样本图像分割框架，用于检测皮肤红斑（erythema），利用扩散模型中的可编辑反演技术生成无红斑的参考图像，并与原始图像精确对齐。通过最小化用户干预的颜色空间分析，识别出红斑区域。该方法显著降低对标注皮肤病数据集的依赖，避免了任何标注训练掩码的需求，提供了一种可扩展且灵活的诊断辅助工具。初步定性实验显示，该流程在多种病例中成功分离面部红斑，性能优于基线阈值分割方法。结果表明，结合生成式扩散模型与统计颜色分割，可在无先验训练数据条件下实现高效的红斑检测，具备计算机辅助皮肤病学应用潜力。
原文摘要 · Abstract (English)
This study proposes a zero-shot image segmentation framework for detecting erythema (redness of the skin) using edit-friendly inversion in diffusion models. The method synthesizes reference images of the same patient that are free from erythema via generative editing and then accurately aligns these references with the original images. Color-space analysis is performed with minimal user intervention to identify erythematous regions. This approach significantly reduces the reliance on labeled dermatological datasets while providing a scalable and flexible diagnostic support tool by avoiding the need for any annotated training masks. In our initial qualitative experiments, the pipeline successfully isolated facial erythema in diverse cases, demonstrating performance improvements over baseline threshold-based techniques. These results highlight the potential of combining generative diffusion models and statistical color segmentation for computer-aided dermatology, enabling efficient erythema detection without prior training data.
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