CLARE-XR:对带有标签嵌入的胸部X射线图的基于回归的可解释分类
Joana Rocha1,2, Sofia Cardoso Pereira3,4, Pedro Sousa5
1Institute for Systems and Computer Engineering Technology and Science (INESC-TEC), Porto, 4200-465, Portugal. joana.m.rocha@inesctec.pt.
CLARE-XR是一种用于胸部X射线病理学分类的新系统. 它通过引用类似的案例提供可解释的解释,增强对医学成像分析的信任和决策.
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 放射学是一门学科.
背景情况:
- 在胸部X射线中准确的病理学分类对于临床决策至关重要.
- 现有的系统往往缺乏透明度,阻碍了最终用户的信任和监管合规性.
- 解释性对于在医学诊断中采用人工智能至关重要.
研究的目的:
- 推出CLARE-XR,一种用于可解释胸部X射线病理学分类的新方法.
- 通过检索类似的病例,为诊断提供视觉解释.
- 增强最终用户的信任和支持临床决策.
主要方法:
- 开发了一种回归模型,将胸部X射线图像映射到二维潜伏空间.
- 通过标签嵌入基础真相注释来生成病理的参考坐标.
- 推断分类基于距离参考坐标的距离和获取类似的训练图像.
主要成果:
- 与NIH ChestX-ray14数据集上的ResNet50基线相比,CLARE-XR显示出更高的性能.
- 该系统成功地识别了病理,并通过病例检索提供了解释.
- 该方法提供了固有的解释性,并披露了分类规则.
结论:
- CLARE-XR为胸部X射线分析提供了一个可解释的框架,超越了预测性能的范围.
- 该系统模仿临床实践,通过将当前扫描与类似的过去病例进行比较.
- 这种方法增强了信任,并有助于对人工智能驱动的医学诊断的监管合规.
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