建立皮肤病理学百科全书DermpathNet与基于人工智能的工作流程
Ziyang Xu1, Mingquan Lin2, Yiliang Zhou2
1Perelman Department of Dermatology, NYU Grossman School of Medicine, New York, USA.
Scientific data
|February 6, 2026
概括
使用混合工作流程创建了一个新的开放式皮肤病理图像数据集 - - DermpathNet. 这个经过同行评审的资源帮助临床医生和研究人员学习和机器学习应用.
科学领域:
- 皮肤病理学 皮肤病理学
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
背景情况:
- 临床医生和实习生面临的挑战是获得高质量,开放的皮肤病理图像数据集,用于教育和研究.
- 现有的资源往往缺乏全面的注释,或者不易用于机器学习应用程序.
研究的目的:
- 为教育,交叉引用和机器学习目的建立一个全面的,开放的皮肤病理学图像数据集.
- 开发和验证一个强大的工作流程,用于策划和分类皮肤病理图像.
主要方法:
- 采用了混合工作流,结合了基于深度学习的图像模式分类和图片标题分析.
- 图像从PubMed Central (PMC) 存储库中使用特定的关键字进行策划和分类.
- 工作流的稳定性在651个手动注释图像上得到验证.
主要成果:
- 混合方法获得了90.4%的F分,明显超过了深度学习 (89.6%) 和关键字检索 (61.0%).
- 超过7772张图像覆盖166个诊断被检索和注释.
- 开发的数据集DermpathNet被发现是当前图像分析算法的一个具有挑战性的基准.
结论:
- 一个大型的,经过同行评审的,开放的皮肤病理学图像数据集 (DermpathNet) 已经成功开发出来.
- 半自动化策划工作流提供了一个可扩展的方法来创建有价值的医疗图像数据集.
- 皮肤病理学教育,研究和AI在医疗图像分析中的进步,DermpathNet是重要的资源.
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