使用语义和实例细分方法进行Mpox损伤计数
Bohan Jiang1,2,3, Andrew J McNeil1,2,3, Yihao Liu3
1Dermatology Service and Research Service, Department of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, Tennessee, United States.
Journal of medical imaging (Bellingham, Wash.)
|June 23, 2025
概括
使用人工智能模型自动计数mopox病变显示出有希望的结果. 联合国网络++模型获得了最高的F1分数,表明其在病变检测和计入mopox疾病监测中的有效性.
科学领域:
- 医学成像医学成像
- 计算机视觉 计算机视觉 计算机视觉
- 传染病建模传染病模型
背景情况:
- 麻疹 (mpox) 是一种病毒性疾病,表现出类似于天花的症状.
- 准确监测mopox进展依赖于量化皮肤病变.
- 手动的病变计数是耗时的,容易出现人为错误.
研究的目的:
- 为了比较各种人工智能模型的性能,用于自动化mpox损伤计数.
- 评估实例细分 (Mask R-CNN,YOLOv8,E2EC) 和语义细分 (UNet,UNet++) 的方法.
- 确定一组模型是否可以提高病变计数的准确性.
主要方法:
- 四个人工智能模型 (Mask R-CNN,YOLOv8,E2EC,UNet++) 与一个基线UNet模型进行了比较.
- 采用了患者一级的离开一次的交叉验证策略.
- 使用F1评分和Bland-Altman分析来评估损伤数的表现.
主要成果:
- UNet++获得了最高的F1得分 (0.81),紧随其后的是基线UNet (0.79).
- 面具R-CNN和YOLOv8获得了F1分数0.75,而E2EC获得了0.70.
- 布兰德-阿尔特曼分析显示,UNet++ (62.1) 和UNet (69.1) 的协议界限最窄.
结论:
- 实例和语义细分模型都在mpox损伤计数中表现出可比的有效性.
- 一组模型的表现并没有超过最好的单一模型 (UNet++),这表明共享的错误模式.
- 该研究强调,数据质量和数量,而不是算法选择,可能是提高性能的主要限制.
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