用条件扩散生成皮肤病图像，提升分类准确率并适配移动端部署
DerMAE: Improving skin lesion classification through conditioned latent diffusion and MAE distillation
- 用条件扩散模型生成罕见恶性病变图像以缓解数据不平衡
- 在合成数据上预训练MAE，使ViT模型学到强健的皮肤特征
- 通过知识蒸馏将大模型能力迁移到轻量ViT，支持手机端推理
皮肤病变分类数据集普遍存在严重类别不平衡问题，恶性病例显著稀少，导致深度学习训练时决策边界产生偏差。本文提出使用类条件扩散模型生成合成皮肤科图像，随后在合成数据上进行自监督MAE预训练，使大型ViT模型学习到鲁棒且领域相关的特征表示。为支持实际临床场景中的部署需求（要求轻量化模型），我们采用知识蒸馏技术，将这些特征表示迁移到更小的ViT学生模型中，该模型适用于移动设备。实验表明，在合成数据上进行MAE预训练并结合蒸馏，不仅提升了分类性能，还实现了高效的本地化推理，满足实际临床应用需求。
原文摘要 · Abstract (English)
Skin lesion classification datasets often suffer from severe class imbalance, with malignant cases significantly underrepresented, leading to biased decision boundaries during deep learning training. We address this challenge using class-conditioned diffusion models to generate synthetic dermatological images, followed by self-supervised MAE pretraining to enable huge ViT models to learn robust, domain-relevant features. To support deployment in practical clinical settings, where lightweight models are required, we apply knowledge distillation to transfer these representations to a smaller ViT student suitable for mobile devices. Our results show that MAE pretraining on synthetic data, combined with distillation, improves classification performance while enabling efficient on-device inference for practical clinical use.
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