# LesionTABE: Equitable AI for Skin Lesion Detection

arXiv: https://arxiv.org/abs/2601.03090 · HTML: https://mlsanity.com/paper/2601.03090/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 14 references that are in the map.
- First posted on arXiv 6 Jan 2026.

## Builds on (most cited first, 10 of 10)

- [PAD-UFES-20: a skin lesion dataset composed of patient data and clinical images collected from smartphones](https://mlsanity.com/paper/2007.00478/) (2020)
- [Towards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithm](https://mlsanity.com/paper/2207.02942/) (2022)
- [Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets](https://mlsanity.com/paper/1910.13268/) (2019)
- [Deep Learning Ensembles for Melanoma Recognition in Dermoscopy Images](https://mlsanity.com/paper/1610.04662/) (2016)
- [FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learning](https://mlsanity.com/paper/2208.10013/) (2022)
- [Crowdsourcing Dermatology Images with Google Search Ads: Creating a Real-World Skin Condition Dataset](https://mlsanity.com/paper/2402.18545/) (2024)
- [Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification](https://mlsanity.com/paper/2109.09818/) (2021)
- [A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysis](https://mlsanity.com/paper/2405.14839/) (2024)
- [Revisiting Skin Tone Fairness in Dermatological Lesion Classification](https://mlsanity.com/paper/2308.09640/) (2023)
- [Skin Cancer Machine Learning Model Tone Bias](https://mlsanity.com/paper/2410.06385/) (2024)

## Abstract (arXiv)

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.