DermaVQA-DAS：皮肤科评估方案（DAS）及用于患者生成皮肤科图像中封闭式问答与分割的数据库
DermaVQA-DAS: Dermatology Assessment Schema (DAS) & Datasets for Closed-Ended Question Answering & Segmentation in Patient-Generated Dermatology Images
AI总结：
DermaVQA-DAS引入了皮肤科评估方案DAS，支持封闭式问答与分割任务，通过专家标注数据集和多模态模型评估，提升患者为中心的皮肤科视觉语言模型研究。
摘要：
近期在皮肤科图像分析方面的进展主要由大规模标注数据集推动；然而，大多数现有基准测试侧重于皮肤镜图像，并缺乏患者自编查询和临床背景，限制了其在以患者为中心的护理中的应用。为了解决这一差距，我们介绍了DermaVQA-DAS，这是DermaVQA数据集的扩展，支持两种互补任务：封闭式问答（QA）和皮肤病变分割。这项工作的核心是皮肤科评估方案（DAS），一种由专家开发的新框架，系统地以结构化和标准化的方式捕捉具有临床意义的皮肤科特征。DAS包含36个高级和27个细粒度评估问题，其中包含英文和中文的多项选择选项。利用DAS，我们为封闭式QA和分割提供了专家标注的数据集，并基准测试了最先进的多模态模型。在分割方面，我们评估了多种提示策略，并表明提示设计会影响性能：默认提示在Mean-of-Max和Mean-of-Mean评估聚合方案下表现最佳，而一个结合了患者查询标题和内容的增强提示在基于多数投票的微评分评估中表现最佳，使用BiomedParse时，Jaccard指数达到0.395，Dice分数达到0.566。在封闭式QA方面，各模型的整体性能表现良好，平均准确率在0.729至0.798之间；o3实现了最佳整体准确率（0.798），随后是GPT-4.1（0.796），而Gemini-1.5-Pro在Gemini系列中表现竞争性（0.783）。我们公开发布了DermaVQA-DAS、DAS方案和评估协议，以支持和加速未来以患者为中心的皮肤科视觉-语言建模研究（https://osf.io/72rp3）。
英文摘要：
Recent advances in dermatological image analysis have been driven by large-scale annotated datasets; however, most existing benchmarks focus on dermatoscopic images and lack patient-authored queries and clinical context, limiting their applicability to patient-centered care. To address this gap, we introduce DermaVQA-DAS, an extension of the DermaVQA dataset that supports two complementary tasks: closed-ended question answering (QA) and dermatological lesion segmentation. Central to this work is the Dermatology Assessment Schema (DAS), a novel expert-developed framework that systematically captures clinically meaningful dermatological features in a structured and standardized form. DAS comprises 36 high-level and 27 fine-grained assessment questions, with multiple-choice options in English and Chinese. Leveraging DAS, we provide expert-annotated datasets for both closed QA and segmentation and benchmark state-of-the-art multimodal models. For segmentation, we evaluate multiple prompting strategies and show that prompt design impacts performance: the default prompt achieves the best results under Mean-of-Max and Mean-of-Mean evaluation aggregation schemes, while an augmented prompt incorporating both patient query title and content yields the highest performance under majority-vote-based microscore evaluation, achieving a Jaccard index of 0.395 and a Dice score of 0.566 with BiomedParse. For closed-ended QA, overall performance is strong across models, with average accuracies ranging from 0.729 to 0.798; o3 achieves the best overall accuracy (0.798), closely followed by GPT-4.1 (0.796), while Gemini-1.5-Pro shows competitive performance within the Gemini family (0.783). We publicly release DermaVQA-DAS, the DAS schema, and evaluation protocols to support and accelerate future research in patient-centered dermatological vision-language modeling (https://osf.io/72rp3).
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。
