DerMAE: Improving skin lesion classification through conditioned latent diffusion and MAE distillation

• URL: http://arxiv.org/abs/2602.19848v1
• Date: Mon, 23 Feb 2026 13:52:28 GMT
• Title: DerMAE: Improving skin lesion classification through conditioned latent diffusion and MAE distillation
• Authors: Francisco Filho, Kelvin Cunha, Fábio Papais, Emanoel dos Santos, Rodrigo Mota, Thales Bezerra, Erico Medeiros, Paulo Borba, Tsang Ing Ren,
• Abstract summary: We use 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.<n>We apply knowledge distillation to transfer these representations to a smaller ViT student suitable for mobile devices.<n>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.
• Score: 1.485045763113618
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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