使用CT图像检测COVID-19的偏差深度学习方法:由主体智能分割ISFCT数据集构成的挑战
Shiva Parsarad1,2, Narges Saeedizadeh1,3, Ghazaleh Jamalipour Soufi4
1Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan JM76+5M3, Iran.
Journal of imaging
|August 25, 2023
概括
使用CT扫描检测COVID-19的深度学习 (DL) 模型往往缺乏可重复性. 一个新的主题分类数据集 (ISFCT) 揭示了复杂的模型不能保证准确性,并突出了当前数据分类方法的问题.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机辅助诊断 计算机辅助诊断
背景情况:
- 深度学习 (DL) 模型对于检测呼吸系统损伤至关重要,包括COVID-19,使用CT图像.
- 现有研究经常报告由于切片式的数据分割,导致训练和测试集之间产生依赖性,因此准确性不可靠,并且缺乏可重复性.
- 这个问题影响了DL模型在医学诊断中的实际性能验证.
研究的目的:
- 引入一个新的CT图像数据集 (ISFCT数据集) 进行主题分类,以进行公正的DL模型培训和测试.
- 评估现有DL模型的实际性能,使用对象分类,解决切片式验证的局限性.
- 探索数据分割策略对呼吸道疾病检测模型性能和可重复性的影响.
主要方法:
- 开发ISFCT数据集,为CT图像提供主题智能标签,使得DL模型能够进行可靠的评估.
- 实施对象分类数据以训练和测试DL算法,确保数据集之间的数据独立性.
- 在ISFCT数据集上验证先前发布的DL模型,使用主题wise分割和与切片wise结果进行比较.
- 使用t-分布的随机邻居嵌入 (t-SNE) 来可视化和演示数据分割之间的分布差异.
主要成果:
- 据报道,现有DL模型在切片式分割上的高准确度在使用主体式分割时是不可重复的.
- 观察到切片式和主体式数据分割之间存在显著的分布差异,这影响了模型的概括.
- 与复杂模型相比,较不复杂的DL模型在被训练和测试对主体智能的分割时获得了竞争性和可重复的结果.
- 该研究表明,复杂的DL模型本身并不能保证准确和可重复的性能.
结论:
- 根据主体划分数据对于公正的评估和确保DL模型在医学成像中的可重复性至关重要,特别是在呼吸系统疾病中.
- ISFCT数据集为基于CT的呼吸道疾病检测开发和验证强大的DL模型提供了宝贵的资源.
- 简单的DL模型可以有效和可靠,挑战模型复杂性与诊断准确性和可重复性直接相关的概念.
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