arXiv
Open Access
2026
LesionTABE: Equitable AI for Skin Lesion Detection
Rocio Mexia Diaz
Yasmin Greenway
Petru Manescu
Abstrak
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.
Topik & Kata Kunci
Penulis (3)
R
Rocio Mexia Diaz
Y
Yasmin Greenway
P
Petru Manescu
Akses Cepat
Informasi Jurnal
- Tahun Terbit
- 2026
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