DerMAE: Improving skin lesion classification through conditioned latent diffusion and MAE distillation
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866911740674965504 |
|---|---|
| author | Filho, Francisco Cunha, Kelvin Papais, Fábio Santos, Emanoel dos Mota, Rodrigo Bezerra, Thales Medeiros, Erico Borba, Paulo Ren, Tsang Ing |
| author_facet | Filho, Francisco Cunha, Kelvin Papais, Fábio Santos, Emanoel dos Mota, Rodrigo Bezerra, Thales Medeiros, Erico Borba, Paulo Ren, Tsang Ing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19848 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DerMAE: Improving skin lesion classification through conditioned latent diffusion and MAE distillation Filho, Francisco Cunha, Kelvin Papais, Fábio Santos, Emanoel dos Mota, Rodrigo Bezerra, Thales Medeiros, Erico Borba, Paulo Ren, Tsang Ing Computer Vision and Pattern Recognition 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. |
| title | DerMAE: Improving skin lesion classification through conditioned latent diffusion and MAE distillation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.19848 |