标题： 口腔GPT：一种用于口腔黏膜疾病诊断和描述的两阶段视觉-语言模型 显示英文标题

标题： OralGPT: A Two-Stage Vision-Language Model for Oral Mucosal Disease Diagnosis and Description

摘要： 口腔黏膜疾病如白斑、口腔扁平苔藓和复发性阿弗他溃疡表现出多样且重叠的视觉特征，使非专科医生的诊断变得具有挑战性。尽管视觉语言模型（VLMs）在医学图像解释方面显示出潜力，但由于缺乏大规模、良好标注的数据集，其在口腔健康领域的应用仍鲜有探索。在本工作中，我们提出了\textbf{口语GPT}，第一个针对口腔黏膜疾病诊断和描述的领域特定两阶段视觉语言框架。在第一阶段，OralGPT从分类标签中学习视觉表示和与疾病相关的概念。在第二阶段，它通过使用长篇专家撰写的描述来增强其语言生成能力。为克服标注瓶颈，我们提出了一种新颖的相似性引导数据增强策略，将专家标注图像的描述性知识传播到弱标注图像上。我们还构建了第一个口腔黏膜疾病的基准数据集，整合了多源图像数据以及结构化和非结构化的文本注释。在四种常见的口腔疾病上的实验结果表明，OralGPT在实现具有竞争力的诊断性能的同时，还能生成流畅且具有临床意义的图像描述。本研究为口腔健康领域的语言辅助诊断工具提供了基础。 显示英文摘要

摘要： Oral mucosal diseases such as leukoplakia, oral lichen planus, and recurrent aphthous ulcers exhibit diverse and overlapping visual features, making diagnosis challenging for non-specialists. While vision-language models (VLMs) have shown promise in medical image interpretation, their application in oral healthcare remains underexplored due to the lack of large-scale, well-annotated datasets. In this work, we present \textbf{OralGPT}, the first domain-specific two-stage vision-language framework designed for oral mucosal disease diagnosis and captioning. In Stage 1, OralGPT learns visual representations and disease-related concepts from classification labels. In Stage 2, it enhances its language generation ability using long-form expert-authored captions. To overcome the annotation bottleneck, we propose a novel similarity-guided data augmentation strategy that propagates descriptive knowledge from expert-labeled images to weakly labeled ones. We also construct the first benchmark dataset for oral mucosal diseases, integrating multi-source image data with both structured and unstructured textual annotations. Experimental results on four common oral conditions demonstrate that OralGPT achieves competitive diagnostic performance while generating fluent, clinically meaningful image descriptions. This study provides a foundation for language-assisted diagnostic tools in oral healthcare.