LesionTABE: Equitable AI for Skin Lesion Detection
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866912805181980672 |
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| author | Diaz, Rocio Mexia Greenway, Yasmin Manescu, Petru |
| author_facet | Diaz, Rocio Mexia Greenway, Yasmin Manescu, Petru |
| contents | Bias remains a major barrier to the clinical adoption of AI in dermatology, as diagnostic models underperform on darker skin tones. We present LesionTABE, a fairness-centric framework that couples adversarial debiasing with dermatology-specific foundation model embeddings. Evaluated across multiple datasets covering both malignant and inflammatory conditions, LesionTABE achieves over a 25\% improvement in fairness metrics compared to a ResNet-152 baseline, outperforming existing debiasing methods while simultaneously enhancing overall diagnostic accuracy. These results highlight the potential of foundation model debiasing as a step towards equitable clinical AI adoption. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03090 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LesionTABE: Equitable AI for Skin Lesion Detection Diaz, Rocio Mexia Greenway, Yasmin Manescu, Petru Computer Vision and Pattern Recognition Bias remains a major barrier to the clinical adoption of AI in dermatology, as diagnostic models underperform on darker skin tones. We present LesionTABE, a fairness-centric framework that couples adversarial debiasing with dermatology-specific foundation model embeddings. Evaluated across multiple datasets covering both malignant and inflammatory conditions, LesionTABE achieves over a 25\% improvement in fairness metrics compared to a ResNet-152 baseline, outperforming existing debiasing methods while simultaneously enhancing overall diagnostic accuracy. These results highlight the potential of foundation model debiasing as a step towards equitable clinical AI adoption. |
| title | LesionTABE: Equitable AI for Skin Lesion Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.03090 |