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
this https URL

通过在皮肤病分类图像中考虑皮肤色调差异来解决公平性问题是至关重要的，然而公共数据集中缺乏皮肤色调标签导致无法构建公平的分类器。目前，这些皮肤色调标签的估计是在独立研究中使用个体类型角度（ITA）进行公正性分析之前进行的。本文回顾并比较了四种基于 ITA 的皮肤色调分类方法在 ISIC18 数据集上的应用，该数据集是评估皮肤癌分类公平性的常用基准。我们的分析揭示了先前发表的研究中存在较大分歧，从而展示了基于 ITA 的皮肤色调估计方法存在的风险。此外，我们调查了这些方法之间差异如此之大的原因，并发现 ISIC18 数据集中缺乏多样性，限制了其作为公平性分析测试平台的使用。最后，我们建议进一步研究鲁棒的 ITA 估计方法和带有肤色注释的多样性数据集采集，以促进对皮肤科人工智能工具的公平评估。我们的代码可在此 https URL 访问。