SkinGEN：基于交互式视觉语言模型的可解释皮肤病诊断到生成框架
SkinGEN: an Explainable Dermatology Diagnosis-to-Generation Framework with Interactive Vision-Language Models
AI总结：
针对VLM在皮肤病诊断中可解释性不足的问题，提出SkinGEN框架，利用稳定扩散（SD）模型从诊断结果生成参考演示，经实验验证可显著提升用户理解与信任，推动透明化VLM应用。
摘要：
随着视觉语言模型（VLMs）技术的不断进步，作为人类第四大常见疾病类别的皮肤病领域已取得显著研究成果。然而，尽管有这些进展，由于皮肤病症状固有的复杂性，VLM在诊断时仍面临向用户解释的问题，现有工具对用户理解的支持相对有限。我们提出SkinGEN，一个诊断到生成框架，它利用稳定扩散（SD）模型从VLM提供的诊断结果生成参考演示，从而增强对用户的视觉可解释性。通过使用低秩适应（LoRA）的大量实验，我们确定了皮肤病图像生成的最优策略。我们对32名参与者进行了用户研究，评估系统性能和可解释性。结果表明，SkinGEN显著提高了用户对VLM预测的理解，并增强了对诊断过程的信任。这项工作为皮肤病学及其他领域更透明、以用户为中心的VLM应用铺平了道路。
英文摘要：
With the continuous advancement of vision language models (VLMs) technology, remarkable research achievements have emerged in the dermatology field, the fourth most prevalent human disease category. However, despite these advancements, VLM still faces explainable problems to user in diagnosis due to the inherent complexity of dermatological conditions, existing tools offer relatively limited support for user comprehension. We propose SkinGEN, a diagnosis-to-generation framework that leverages the stable diffusion(SD) model to generate reference demonstrations from diagnosis results provided by VLM, thereby enhancing the visual explainability for users. Through extensive experiments with Low-Rank Adaptation (LoRA), we identify optimal strategies for skin condition image generation. We conduct a user study with 32 participants evaluating both the system performance and explainability. Results demonstrate that SkinGEN significantly improves users' comprehension of VLM predictions and fosters increased trust in the diagnostic process. This work paves the way for more transparent and user-centric VLM applications in dermatology and beyond.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
