通过以人为中心的镜头评估胸部放射学成像中的可解释的人工智能 (XAI) 技术
Izegbua E Ihongbe1, Shereen Fouad1, Taha F Mahmoud2
1School of Computer Science and Digital Technologies, Aston University, Birmingham, United Kingdom.
PloS one
|October 9, 2024
概括
放射学中的可解释AI (XAI) 提高了深度学习的透明度. 一项用户研究发现,Grad-CAM在胸部X射线图的连贯性和信任性方面优于LIME,突出了对意识和包容性设计的需求.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 深度学习 (DL) 在放射学中越来越多地用于诊断.
- 可解释AI (XAI) 旨在提高DL模型的透明度和信任.
- 由于技术和以人为中心的评估差距,医疗保健采用XAI仍然存在挑战.
研究的目的:
- 评估视觉XAI技术用于基于DL的胸部X射线诊断.
- 从人类的角度评估用户对XAI方法的看法和偏好.
- 在临床实践中确定有效的XAI系统集成的用户驱动的要求.
主要方法:
- 在胸部X射线/CT扫描中开发了用于肺炎和COVID-19检测的DL模型.
- 应用梯度加权类激活映射 (Grad-CAM) 和局部可解释模型不可知解释 (LIME) 进行视觉解释.
- 与医疗专业人员进行了一项用户研究,评估XAI的临床相关性,连贯性和信任性.
主要成果:
- 在DL模型中,肺炎的准确率达到了90%,COVID-19的准确率达到了98%.
- 参与者普遍认为XAI系统是积极的,但对其实际价值的认识有限.
- 尽管注意到临床可用性问题,但Grad-CAM在连贯性和信任性方面表现优于LIME.
结论:
- 医疗专业人员更喜欢XAI系统在胸部放射学,但意识和实际理解需要改进.
- 用户驱动的要求强调多模式可解释性和包容性设计,以更好地实现临床整合.
- 提高从业人员的认识和解决可用性对于XAI在放射学中的广泛采用至关重要.
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