# Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets

arXiv: https://arxiv.org/abs/1910.13268 · HTML: https://mlsanity.com/paper/1910.13268/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2019.

## Built on by (most cited first, 29 of 29)

- [Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset](https://mlsanity.com/paper/2104.09957/) (2021)
- [Towards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithm](https://mlsanity.com/paper/2207.02942/) (2022)
- [Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification](https://mlsanity.com/paper/2202.02832/) (2022)
- [Consensus and Subjectivity of Skin Tone Annotation for ML Fairness](https://mlsanity.com/paper/2305.09073/) (2023)
- [Algorithm Fairness in AI for Medicine and Healthcare](https://mlsanity.com/paper/2110.00603/) (2021)
- [Balancing Biases and Preserving Privacy on Balanced Faces in the Wild](https://mlsanity.com/paper/2103.09118/) (2021)
- [Out-of-Distribution Detection in Dermatology using Input Perturbation and Subset Scanning](https://mlsanity.com/paper/2105.11160/) (2021)
- [Skin Deep: Investigating Subjectivity in Skin Tone Annotations for Computer Vision Benchmark Datasets](https://mlsanity.com/paper/2305.09072/) (2023)
- [Racial Bias within Face Recognition: A Survey](https://mlsanity.com/paper/2305.00817/) (2023)
- [Exploring Social Bias in Downstream Applications of Text-to-Image Foundation Models](https://mlsanity.com/paper/2312.10065/) (2023)
- [S-SYNTH: Knowledge-Based, Synthetic Generation of Skin Images](https://mlsanity.com/paper/2408.00191/) (2024)
- [Vision-Language Models Generate More Homogeneous Stories for Phenotypically Black Individuals](https://mlsanity.com/paper/2412.09668/) (2024)
- [Impact of Data Processing on Fairness in Supervised Learning](https://mlsanity.com/paper/2102.01867/) (2021)
- [Information Theoretic Measures for Fairness-aware Feature Selection](https://mlsanity.com/paper/2106.00772/) (2021)
- [DermSynth3D: Synthesis of in-the-wild Annotated Dermatology Images](https://mlsanity.com/paper/2305.12621/) (2023)
- [DermDiff: Generative Diffusion Model for Mitigating Racial Biases in Dermatology Diagnosis](https://mlsanity.com/paper/2503.17536/) (2025)
- [Renyi Fair Information Bottleneck for Image Classification](https://mlsanity.com/paper/2203.04950/) (2022)
- [Benchmarking the Fairness of Image Upsampling Methods](https://mlsanity.com/paper/2401.13555/) (2024)
- [Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning](https://mlsanity.com/paper/2509.00745/) (2025)
- [Classification Utility, Fairness, and Compactness via Tunable Information Bottleneck and R'enyi Measures](https://mlsanity.com/paper/2206.10043/) (2022)
- [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)
- [Methodology for Creating a Clinically Verified Dermoscopic Image Dataset](https://mlsanity.com/paper/2605.25168/) (2026)
- [TrustSkin: A Fairness Pipeline for Trustworthy Facial Affect Analysis Across Skin Tone](https://mlsanity.com/paper/2505.20637/) (2025)
- [Mitigating Individual Skin Tone Bias in Skin Lesion Classification through Distribution-Aware Reweighting](https://mlsanity.com/paper/2512.08733/) (2025)
- [LesionTABE: Equitable AI for Skin Lesion Detection](https://mlsanity.com/paper/2601.03090/) (2026)
- [Colorimeter-Supervised Skin Tone Estimation from Dermatoscopic Images for Fairness Auditing](https://mlsanity.com/paper/2602.10265/) (2026)
- [Skin Tone in the Wild: A Dataset and Benchmark for 10-tone MST Classification](https://mlsanity.com/paper/2603.02475/) (2026)
- [Exploring the Impact of Skin Color on Skin Lesion Segmentation](https://mlsanity.com/paper/2603.29694/) (2026)

## Abstract (arXiv)

Recent advances in computer vision and deep learning have led to breakthroughs in the development of automated skin image analysis. In particular, skin cancer classification models have achieved performance higher than trained expert dermatologists. However, no attempt has been made to evaluate the consistency in performance of machine learning models across populations with varying skin tones. In this paper, we present an approach to estimate skin tone in benchmark skin disease datasets, and investigate whether model performance is dependent on this measure. Specifically, we use individual typology angle (ITA) to approximate skin tone in dermatology datasets. We look at the distribution of ITA values to better understand skin color representation in two benchmark datasets: 1) the ISIC 2018 Challenge dataset, a collection of dermoscopic images of skin lesions for the detection of skin cancer, and 2) the SD-198 dataset, a collection of clinical images capturing a wide variety of skin diseases. To estimate ITA, we first develop segmentation models to isolate non-diseased areas of skin. We find that the majority of the data in the the two datasets have ITA values between 34.5deg and 48deg, which are associated with lighter skin, and is consistent with under-representation of darker skinned populations in these datasets. We also find no measurable correlation between performance of machine learning model and ITA values, though more comprehensive data is needed for further validation.