Purpose of review: Skin type diversity in image datasets refers to the representation of various skin types. This diversity allows for the verification of comparable performance of a trained model across different skin types. A widespread problem in datasets involving human skin is the lack of verifiable diversity in skin types, making it difficult to evaluate whether the performance of the trained models generalizes across different skin types. For example, the diversity issues in skin lesion datasets, which are used to train deep learning-based models, often result in lower accuracy for darker skin types that are typically under-represented in these datasets. Under-representation in datasets results in lower performance in deep learning models for under-represented skin types.

Recent findings: This issue has been discussed in previous works; however, the reporting of skin types, and inherent diversity, have not been fully assessed. Some works report skin types but do not attempt to assess the representation of each skin type in datasets. Others, focusing on skin lesions, identify the issue but do not measure skin type diversity in the datasets examined.

Summary: Effort is needed to address these shortcomings and move towards facilitating verifiable diversity. Building on previous works in skin lesion datasets, this review explores the general issue of skin type diversity by investigating and evaluating skin lesion datasets specifically. The main contributions of this work are an evaluation of publicly available skin lesion datasets and their metadata to assess the frequency and completeness of reporting of skin type and an investigation into the diversity and representation of each skin type within these datasets.

Supplementary information: The online version contains material available at 10.1007/s13671-024-00440-0.

Keywords: Fitzpatrick skin type · Skin lesion datasets · Skin type diversity · Deep learning.

© The Author(s) 2024.

回顾目的:

图像数据集中皮肤类型的多样性指的是各种皮肤类型的表现。这种多样性使得可以在不同的皮肤类型上验证训练模型的可比性能。在涉及人类皮肤的数据集中，一个普遍问题是缺乏可验证的皮肤类型多样性，这使得难以评估训练模型的性能是否能在不同皮肤类型之间泛化。例如，在用于训练基于深度学习模型的皮肤病变数据集中，由于多样性问题，通常代表不足的深色皮肤类型的准确性较低。数据集中的代表性不足导致深度学习模型在代表性不足的皮肤类型上的表现较差。

最近发现:

这个问题已在以前的工作中讨论过；然而，有关皮肤类型的报告及其内在多样性的评估尚未得到充分重视。一些工作会报告皮肤类型，但不试图评估数据集中每种皮肤类型的代表性情况。另一些专注于皮肤病变的研究者则识别了这一问题，但在检查的数据集内并未测量皮肤类型多样性。

摘要:

需要努力来解决这些问题，并朝着可验证多样性的方向发展。基于之前关于皮肤病变数据集的工作，本回顾探讨了一般性皮肤类型多样性的议题，通过调查和评估特定的皮肤病变数据集来进行研究。这项工作的主要贡献是对公开可用的皮肤病变数据集及其元数据进行评估，以评估报告皮肤类型的频率和完整性，并且探索这些数据集中每种皮肤类型的多样性与代表性情况。

补充信息:

@The online version contains material available at 10.1007/s13671-024-00440-0.

关键词:

Fitzpatrick皮肤类型 · 皮肤病变数据集 · 皮肤类型多样性 · 深度学习。

© The Author(s) 2024.