用手机拍的皮肤病变图，AI能准确分类，助力基层诊疗。
Toward Accessible Dermatology: Skin Lesion Classification Using Deep Learning Models on Mobile-Acquired Images
- 用手机拍摄50多种皮肤病图像，构建真实场景数据集。
- Transformer模型（如Swin）比传统CNN更擅长捕捉全局特征。
- 结合Grad-CAM可视化关键区域，提升AI诊断可解释性。
皮肤疾病是全球最普遍的健康问题之一，但传统诊断方法成本高、流程复杂，且在资源匮乏地区难以获取。基于深度学习的自动分类成为有前景的替代方案，但现有研究多局限于皮肤镜数据集，且病种范围狭窄。本文收集了超过50种皮肤疾病类别的移动设备拍摄图像，更贴近真实临床场景。我们评估了多种卷积神经网络与基于Transformer的架构，结果表明Transformer模型（特别是Swin Transformer）通过有效捕捉全局上下文特征，表现更优。为增强可解释性，引入梯度加权类激活映射（Grad-CAM），突出临床相关区域，提升模型决策透明度。实验结果证明，基于Transformer的方法在移动设备采集的皮肤病变分类中具有巨大潜力，有助于实现资源受限环境下的AI辅助皮肤科筛查与早期诊断。
原文摘要 · Abstract (English)
Skin diseases are among the most prevalent health concerns worldwide, yet conventional diagnostic methods are often costly, complex, and unavailable in low-resource settings. Automated classification using deep learning has emerged as a promising alternative, but existing studies are mostly limited to dermoscopic datasets and a narrow range of disease classes. In this work, we curate a large dataset of over 50 skin disease categories captured with mobile devices, making it more representative of real-world conditions. We evaluate multiple convolutional neural networks and Transformer-based architectures, demonstrating that Transformer models, particularly the Swin Transformer, achieve superior performance by effectively capturing global contextual features. To enhance interpretability, we incorporate Gradient-weighted Class Activation Mapping (Grad-CAM), which highlights clinically relevant regions and provides transparency in model predictions. Our results underscore the potential of Transformer-based approaches for mobile-acquired skin lesion classification, paving the way toward accessible AI-assisted dermatological screening and early diagnosis in resource-limited environments.
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