胸部X射线中的结核病语义细分:一个新的数据集和概括评估
Karthik Kantipudi1, Vy Bui2, Hang Yu2
1National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
本研究介绍了TB-Portals SIFT数据集,用于结核病 (TB) 病变细分在胸部X射线 (CXRs) 中. 细分模型显示了比分类模型更好的概括性,整体实现了跨任务的最高性能.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 公共卫生 公共卫生
背景情况:
- 结核病 (TB) 诊断严重依赖于胸部X射线 (CXR),但自动化方法缺乏解释性.
- 在CXR中对结核病变的语义细分为改善人类对诊断的监督提供了一条道路.
- 全球结核病负担需要改进和透明的诊断工具.
研究的目的:
- 引入TB-Portals SIFT数据集,用于CXR中结核病变的语义细分.
- 评估结核病病变细分的深度学习模型,并比较它们的概括能力.
- 评估基于细分的分类器与传统的图像级分类器的性能.
主要方法:
- 开发了TB-Portals SIFT数据集,其中包含6,328个CXR和10,435个伪标记的TB病变实例.
- 使用五倍交叉验证评估了十个语义细分模型 (UNet和YOLOv8-seg架构).
- 在分类和对象检测任务上,将顶部细分模型及其集体的概括与DenseNet121分类器进行了比较.
主要成果:
- 与DenseNet121分类器相比,基于细分的方法显示出更高的概括性.
- 一组UNet和YOLOv8-seg模型实现了最稳定的性能,在细分,分类和对象检测方面表现出色.
- 从每个架构 (nnUNet ((ResEnc XL) 和YOLOv8m-seg) 中确定了最佳的细分模型.
结论:
- 在CXR中结核病变的语义细分是改善诊断准确性和可解释性的有希望的方法.
- 结核病门户网站的SIFT数据集和评估模型为推进自动结核病检测提供了宝贵的资源.
- 组合方法结合了不同的架构,为医学成像中的结核病分析提供了强大的和可泛化的解决方案.
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