用手机采集高质量皮肤图像，提升AI诊断的准确性和泛化能力
DermAI: Clinical dermatology acquisition through quality-driven image collection for AI classification in mobile
- 手机端实时采集+本地质量检测与模型微调
- 自建数据集覆盖多种人种肤色，显著优于公开数据集
- 适合临床医生和医疗AI开发者用于真实场景训练
AI皮肤病学应用受限于有偏数据集、图像质量参差以及验证不足。我们提出DermAI，一款轻量级手机应用，可在常规问诊中实时捕获、标注并分类皮肤病变。不同于以往聚焦皮肤镜的工具，DermAI支持设备端质量检查与本地模型适配。其临床数据集涵盖广泛肤色、人种及来源设备。初步实验表明，基于公开数据训练的模型在本样本上泛化能力差，而使用本地数据微调后性能显著提升。结果凸显了与医疗需求对齐、标准化且多样化的数据收集对机器学习发展的关键作用。
原文摘要 · Abstract (English)
AI-based dermatology adoption remains limited by biased datasets, variable image quality, and limited validation. We introduce DermAI, a lightweight, smartphone-based application that enables real-time capture, annotation, and classification of skin lesions during routine consultations. Unlike prior dermoscopy-focused tools, DermAI performs on-device quality checks, and local model adaptation. The DermAI clinical dataset, encompasses a wide range of skin tones, ethinicity and source devices. In preliminary experiments, models trained on public datasets failed to generalize to our samples, while fine-tuning with local data improved performance. These results highlight the importance of standardized, diverse data collection aligned with healthcare needs and oriented to machine learning development.
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