The remarkable progress of deep learning in dermatological tasks has brought
us closer to achieving diagnostic accuracies comparable to those of human
experts. However, while large datasets play a crucial role in the development
of reliable deep neural network models, the quality of data therein and their
correct usage are of paramount importance. Several factors can impact data
quality, such as the presence of duplicates, data leakage across train-test
partitions, mislabeled images, and the absence of a well-defined test
partition. In this paper, we conduct meticulous analyses of two popular
dermatological image datasets: DermaMNIST and Fitzpatrick17k, uncovering these
data quality issues, measure the effects of these problems on the benchmark
results, and propose corrections to the datasets. Besides ensuring the
reproducibility of our analysis, by making our analysis pipeline and the
accompanying code publicly available, we aim to encourage similar explorations
and to facilitate the identification and addressing of potential data quality
issues in other large datasets.

通过对 DermaMNIST 和 Fitzpatrick17k 两个常用皮肤病图像数据集的细致分析，揭示了数据质量问题对基准结果的影响，并提出数据集的修正方法，旨在鼓励类似研究和帮助其他大型数据集的数据质量问题的识别和解决。