MST-AI: Skin Color Estimation in Skin Cancer Datasets
Vahid Khalkhali, Hayan Lee, Joseph Nguyen, Sergio Zamora-Erazo, Camille Ragin, Abhishek Aphale, Alfonso Bellacosa, Ellis P. Monk, Saroj K. Biswas
TL;DR
This paper introduces MST-AI, a new method to estimate skin color in skin cancer datasets to improve AI diagnosis accuracy for diverse populations.
Contribution
The novel MST-AI method uses the Monk Skin Tone scale and advanced AI techniques to address skin color bias in skin cancer datasets.
Findings
MST-AI outperformed K-means clustering with Kendall’s Tau, Spearman’s Rho, and NDGC scores of 0.68, 0.69, and 1.00.
The method successfully modeled normal skin tones using a Variational Bayesian Gaussian Mixture Model.
MST-AI provides a foundation for unbiased AI models in early skin cancer diagnosis.
Abstract
The absence of skin color information in skin cancer datasets poses a significant challenge for accurate diagnosis using artificial intelligence models, particularly for non-white populations. In this paper, based on the Monk Skin Tone (MST) scale, which is less biased than the Fitzpatrick scale, we propose MST-AI, a novel method for detecting skin color in images of large datasets, such as the International Skin Imaging Collaboration (ISIC) archive. The approach includes automatic frame, lesion removal, and lesion segmentation using convolutional neural networks, and modeling normal skin tones with a Variational Bayesian Gaussian Mixture Model (VB-GMM). The distribution of skin color predictions was compared with MST scale probability distribution functions (PDFs) using the Kullback-Leibler Divergence (KLD) metric. Validation against manual annotations and comparison with K-means…
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Taxonomy
TopicsCutaneous Melanoma Detection and Management · Skin Protection and Aging · AI in cancer detection