[論文レビュー] LesionTABE: Equitable AI for Skin Lesion Detection

Rocio Mexia Diaz, Yasmin Greenway+1 more|arXiv (Cornell University)|Jan 6, 2026

Cutaneous Melanoma Detection and Management被引用数 0

ひとこと要約

tldr: LesionTABE couples adversarial debiasing with dermatology-focused foundation model embeddings to improve fairness across skin tones in skin lesion detection, achieving notable gains in both fairness and diagnostic performance.

ABSTRACT

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.

研究の動機と目的

• objective: 【Address bias in dermatology AI due to under-representation of darker skin tones in training data.","Develop a fairness-centric framework that preserves lesion-relevant features while removing skin-tone information.","Evaluate across malignant and inflammatory skin conditions with external validation datasets.","Assess the benefit of dermatology-specific foundation model embeddings for fairness and accuracy."

提案手法

• method: 【Combine adversarial debiasing with tone-invariant learning to suppress skin-tone information while retaining lesion features.","Evaluate three debiasing architectures (TABE, VAE with adaptive resampling, FairDisCo) with a ResNet-152 backbone as baseline.","Incorporate dermatology-specific foundation model embeddings using LesionCLIP as an alternative feature extractor.","Train on Fitzpatrick17k and test externally on PAD-UFES (cancer) and SCIN (eczema/psoriasis) to assess external validity.","Use balanced accuracy as primary performance metric and Equality of Opportunity (EOM) and Predictive Quality Disparity (PQD) as fairness metrics.","Provide publicly available code and data handling scripts for replication."

実験結果

リサーチクエスチョン

• RQ1 research_questions: 【Can adversarial debiasing combined with foundation-model embeddings reduce skin-tone related disparities in malignant lesion detection without sacrificing accuracy?","How do different debiasing architectures compare internally and externally when evaluated on skin-tone diverse datasets?","Does a dermatology-focused foundation model (LesionCLIP) improve fairness and generalization across tasks (malignant vs inflammatory lesions) compared to ImageNet-based features?"

主な発見

• key_findings: 【LesionTABE (TABE + LesionCLIP) achieved the best trade-off with a balanced accuracy of 71.8% and an EOM of 0.56, representing relative improvements of 34% in fairness and 5% in performance over the baseline.","On malignant lesion detection, TABE with LesionCLIP consistently yielded higher fairness in internal and external evaluations compared to other combinations.","For eczema vs psoriasis classification, LesionTABE achieved a balanced accuracy of 62.8% and a PQD of 0.80 with relative gains of 25% in fairness and 8% in balanced accuracy over the baseline.","External validation highlighted that some methods (e.g., FairDisCo) excelled internally but underperformed externally, underscoring the importance of external validation for debiasing strategies.","Foundation-model embeddings from LesionCLIP provided robust fairness and generalization across skin tones despite training on predominantly lighter skin types."