胸部放射图的域移位分析 在退伍军人卫生保健管理局人口中进行分类
Mayanka Chandrashekar1, Ian Goethert2, Md Inzamam Ul Haque3
1Advanced Computing for Health Sciences, Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA. chandrashekm@ornl.gov.
域移动显著影响胸部X射线分类的准确性,研究年是一个关键因素. 解决这个问题对于强大的医学成像模型和改善患者护理至关重要.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 放射学 信息学 信息学
背景情况:
- 对于胸部X射线分类的深度学习模型对域移动敏感,影响性能.
- 标签质量和人口因素可能会影响分类准确性.
- 现有的胸部X射线数据集可能会因数据收集和标签方法而有所变化.
研究的目的:
- 评估域位移对胸部X射线分类准确性的影响.
- 分析基本真相标签质量和人口因素 (年龄,性别,学习年) 对分类绩效的影响.
- 在不同的数据集和自然语言处理 (NLP) 提取工具中比较多标签分类性能.
主要方法:
- 使用了在MIMIC-CXR数据集上预先训练的DenseNet121模型进行多标签分类.
- 使用CheXpert和CheXbert Labeler从放射学报告中提取了地面真相标签.
- 在MIMIC-CXR和退伍军人卫生保健管理局胸部X射线 (VA-CXR) 数据集上的模型性能比较.
主要成果:
- 与MIMIC-CXR相比,VA-CXR数据集显示了较低的地面真相分歧率.
- 在VA数据集中观察到最小的域转移,除了"扩大心脏介质"标签.
- 研究年成为多标签分类表现最显著变化的子组.
结论:
- 领域转移和人口因素,特别是考试的时间性 (学习年),显著影响胸部X射线分类.
- 研究结果强调需要改进转移学习策略和医疗成像AI中的强有力的模型开发.
- 解决领域转移对于推进放射学人工智能和增强患者护理至关重要.
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