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FADiaFrame: Improving Fairness and Accuracy of Deep Learning-Based Diagnosis for Dermatological Lesions via a Novel Post-Processing Framework

Yu Gao,Da‐Wei Ding

Published 2026 in IEEE transactions on circuits and systems for video technology (Print)

ABSTRACT

Deep learning (DL) has achieved unprecedented success in precisely diagnosing dermatological lesions. However, increasing concerns over diagnostic unfairness across different demographic subgroups in DL algorithms raise issues of ethical violations and healthcare inequity. Due to limited access to the internal workings of DL algorithms, post-processing methods are widely regarded as efficient techniques for fairness enhancements in DL-based predictions. However, these methods often come at the cost of accuracy, and research exploring their application to medical images remains limited. To address these issues, we propose FADiaFrame, an innovative post-processing framework designed to enhance both fairness and accuracy in DL-based diagnosis of dermatological lesions. Specifically, our uncertainty-aware gating calibration mechanism in FADiaFrame identifies and calibrates untrustworthy samples, thereby enhancing diagnostic fairness, accuracy, and trustworthiness. Furthermore, we integrate this mechanism with an optimal transport method to further enhance group fairness. Extensive experiments on real-world datasets demonstrate that FADiaFrame outperforms existing post-processing methods in terms of both fairness and accuracy. Notably, FADiaFrame preserves diagnostic accuracy while achieving significant gains in fairness compared to pre-trained baseline models. Among all baselines, MedCLIP, with FADiaFrame, shows the most substantial improvement for the age-sensitive attribute, with accuracy increasing by 3.34% and demographic parity rising by 10.60%. Our results suggest that FADiaFrame provides universal applicability across diverse DL models for medical image diagnosis, ensuring both fair and accurate diagnosis across a wide range of devices and deployment contexts.

PUBLICATION RECORD

  • Publication year

    2026

  • Venue

    IEEE transactions on circuits and systems for video technology (Print)

  • Publication date

    2026-02-01

  • Fields of study

    Medicine, Computer Science

  • Identifiers
    DOI 10.1109/TCSVT.2025.3603409
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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