Bioinformatics Core, University of Luxembourg, Luxembourg Centre for Systems Biomedicine, Av. du Swing 6, Belvaux, 4367, Luxembourg.
J Med Syst. 2023 Mar 18;47(1):37. doi: 10.1007/s10916-023-01928-1.
The self-proclaimed first publicly available dataset of Monkeypox skin images consists of medically irrelevant images extracted from Google and photography repositories through a process denominated web-scrapping. Yet, this did not stop other researchers from employing it to build Machine Learning (ML) solutions aimed at computer-aided diagnosis of Monkeypox and other viral infections presenting skin lesions. Neither did it stop the reviewers or editors from publishing these subsequent works in peer-reviewed journals. Several of these works claimed extraordinary performance in the classification of Monkeypox, Chickenpox and Measles, employing ML and the aforementioned dataset. In this work, we analyse the initiator work that has catalysed the development of several ML solutions, and whose popularity is continuing to grow. Further, we provide a rebuttal experiment that showcases the risks of such methodologies, proving that the ML solutions do not necessarily obtain their performance from the features relevant to the diseases at issue.
自称是第一个公开可用的猴痘皮肤图像数据集，由通过网络爬虫从谷歌和摄影资源库中提取的与医学无关的图像组成。然而，这并没有阻止其他研究人员利用它来构建机器学习 (ML) 解决方案，旨在辅助诊断猴痘和其他出现皮肤损伤的病毒感染。也没有阻止审稿人和编辑在同行评议的期刊上发表这些后续作品。其中一些作品声称在猴痘、水痘和麻疹的分类方面表现出色，使用了 ML 和上述数据集。在这项工作中，我们分析了引发了几个 ML 解决方案发展的创始工作，其受欢迎程度还在继续增长。此外，我们提供了一个反驳实验，展示了这种方法的风险，证明 ML 解决方案的性能不一定来自于与所讨论疾病相关的特征。
