# Revisiting Skin Tone Fairness in Dermatological Lesion Classification

arXiv: https://arxiv.org/abs/2308.09640 · HTML: https://mlsanity.com/paper/2308.09640/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 18 Aug 2023.

## Builds on (most cited first, 9 of 9)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset](https://mlsanity.com/paper/2104.09957/) (2021)
- [FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis](https://mlsanity.com/paper/2203.02110/) (2022)
- [On Hate Scaling Laws For Data-Swamps](https://mlsanity.com/paper/2306.13141/) (2023)
- [Estimating and Improving Fairness with Adversarial Learning](https://mlsanity.com/paper/2103.04243/) (2021)
- [Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification](https://mlsanity.com/paper/2202.02832/) (2022)
- [Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification](https://mlsanity.com/paper/2109.09818/) (2021)
- [A Generalization of Otsu's Method and Minimum Error Thresholding](https://mlsanity.com/paper/2007.07350/) (2020)
- [FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging](https://mlsanity.com/paper/2109.09658/) (2021)

## Built on by (most cited first, 9 of 9)

- [Are generative models fair? A study of racial bias in dermatological image generation](https://mlsanity.com/paper/2501.11752/) (2025)
- [PatchAlign:Fair and Accurate Skin Disease Image Classification by Alignment with Clinical Labels](https://mlsanity.com/paper/2409.04975/) (2024)
- [Towards Fairness in AI for Melanoma Detection: Systemic Review and Recommendations](https://mlsanity.com/paper/2411.12846/) (2024)
- [Skin Color Measurement from Dermatoscopic Images: An Evaluation on a Synthetic Dataset](https://mlsanity.com/paper/2504.04494/) (2025)
- [Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression](https://mlsanity.com/paper/2506.11627/) (2025)
- [Bayesian generative models can flag performance loss, bias, and out-of-distribution image content](https://mlsanity.com/paper/2503.17477/) (2025)
- [Towards Facilitated Fairness Assessment of AI-based Skin Lesion Classifiers Through GenAI-based Image Synthesis](https://mlsanity.com/paper/2507.17860/) (2025)
- [Achieving Fair Skin Lesion Detection through Skin Tone Normalization and Channel Pruning](https://mlsanity.com/paper/2509.22712/) (2025)
- [LesionTABE: Equitable AI for Skin Lesion Detection](https://mlsanity.com/paper/2601.03090/) (2026)

## Abstract (arXiv)

Addressing fairness in lesion classification from dermatological images is crucial due to variations in how skin diseases manifest across skin tones. However, the absence of skin tone labels in public datasets hinders building a fair classifier. To date, such skin tone labels have been estimated prior to fairness analysis in independent studies using the Individual Typology Angle (ITA). Briefly, ITA calculates an angle based on pixels extracted from skin images taking into account the lightness and yellow-blue tints. These angles are then categorised into skin tones that are subsequently used to analyse fairness in skin cancer classification. In this work, we review and compare four ITA-based approaches of skin tone classification on the ISIC18 dataset, a common benchmark for assessing skin cancer classification fairness in the literature. Our analyses reveal a high disagreement among previously published studies demonstrating the risks of ITA-based skin tone estimation methods. Moreover, we investigate the causes of such large discrepancy among these approaches and find that the lack of diversity in the ISIC18 dataset limits its use as a testbed for fairness analysis. Finally, we recommend further research on robust ITA estimation and diverse dataset acquisition with skin tone annotation to facilitate conclusive fairness assessments of artificial intelligence tools in dermatology. Our code is available at https://github.com/tkalbl/RevisitingSkinToneFairness.