对X射线图像的视觉XAI方法可靠性的研究
Jan Stodt1, Manav Madan1, Christoph Reich1
1Institute for Data Science, Cloud Computing, and IT Security; Furtwangen University; Furtwangen; Germany.
Studies in health technology and informatics
|June 30, 2023
概括
本研究评估了医学成像中的YOLO物体检测模型的视觉可解释性方法 (Grad-CAM, Eigen-CAM). 这些技术旨在通过突出重要图像区域来提高对AI诊断工具的信任和透明度.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 计算机视觉 计算机视觉
背景情况:
- YOLOv4和YOLOv5在医学诊断中表现出高性能.
- 由于信任和可解释性问题,YOLO模型的黑盒性质阻碍了其在医疗保健中的采用.
研究的目的:
- 评估用于YOLO物体检测模型的视觉可解释性技术 (Grad-CAM, Eigen-CAM).
- 评估这些方法在医学诊断中解释AI决策方面的有效性.
主要方法:
- 将Grad-CAM和Eigen-CAM应用于YOLO模型,而不需要新的层实现.
- 在VinDrCXR胸部X射线异常检测数据集上评估了方法性能.
主要成果:
- 格拉德-CAM和Eigen-CAM适用于用于生成视觉解释的YOLO模型.
- 讨论这些方法在向数据科学家提供明确解释方面存在的局限性.
结论:
- 像Grad-CAM和Eigen-CAM这样的视觉XAI方法可以应用于YOLO模型.
- 需要进一步的研究,以解决在医学应用中向数据科学家解释AI决策的局限性.
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