用相似病例增强提示，提升皮肤癌多模态诊断准确率
Retrieval-Augmented VLMs for Multimodal Melanoma Diagnosis
- 从病历库检索相似病例，动态构建诊断提示
- 无需微调，分类准确率显著高于传统方法
- 适合临床辅助决策系统，尤其擅长纠错
早期准确诊断恶性黑色素瘤对改善患者预后至关重要。尽管卷积神经网络在皮肤镜图像分析中展现出潜力，但通常忽略临床元数据且需大量预处理。视觉语言模型（VLMs）提供了一种多模态替代方案，但在通用领域数据上训练时难以捕捉临床特异性。为此，我们提出一种检索增强的VLM框架，将语义相似的患者病例引入诊断提示。该方法无需微调即可实现更精准的预测，并显著提升分类准确率与错误纠正能力。结果表明，检索增强提示为临床决策支持提供了稳健策略。
原文摘要 · Abstract (English)
Accurate and early diagnosis of malignant melanoma is critical for improving patient outcomes. While convolutional neural networks (CNNs) have shown promise in dermoscopic image analysis, they often neglect clinical metadata and require extensive preprocessing. Vision-language models (VLMs) offer a multimodal alternative but struggle to capture clinical specificity when trained on general-domain data. To address this, we propose a retrieval-augmented VLM framework that incorporates semantically similar patient cases into the diagnostic prompt. Our method enables informed predictions without fine-tuning and significantly improves classification accuracy and error correction over conventional baselines. These results demonstrate that retrieval-augmented prompting provides a robust strategy for clinical decision support.
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