对CAM方法进行比较评估,以提高兽医放射学中的可解释性
Piotr Dusza1,2, Tommaso Banzato3, Silvia Burti3
1AGH University of Krakow, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Krakow, 30059, Poland. pdusza@student.agh.edu.pl.
Scientific reports
|August 13, 2025
概括
这项研究评估了11种类型激活映射 (CAM) 方法,用于在兽医X射线中解释AI. 虽然一些方法显示出希望,但没有一种方法可以普遍提高兽医的诊断信心.
科学领域:
- 兽医辐射学 兽医辐射学
- 可解释的人工智能 (XAI)
- 深度学习的解释性 深度学习的解释性
背景情况:
- 可解释的人工智能 (XAI) 方法,特别是类激活映射 (CAM),对于理解医学成像中的深度学习模型透明度至关重要.
- 在兽医放射学中对CAM方法的系统评估有限,这阻碍了它们的临床采用.
- 这项研究通过比较多种CAM技术来解决动物X射线视觉解释性的差距.
研究的目的:
- 进行对11种类型激活映射 (CAM) 方法进行比较分析,以解释兽医放射学中的AI.
- 评估通过不同的CAM技术在狗和猫的X射线图像上生成的热图的可解释性和临床相关性.
- 评估这些方法对兽医诊断信心的影响.
主要方法:
- 十一种CAM方法,包括GradCAM,XGradCAM,ScoreCAM和EigenCAM,应用于7362张狗和猫的X射线图像的数据集.
- 使用了一个ResNet18模型,根据其在特定的兽医数据集上的表现而选择.
- 进行了定量和定性评估,以评估热图的解释性和临床相关性.
主要成果:
- 与FullGrad和XGradCAM等方法相比, EigenGradCAM, EigenCAM 和 GradCAM++ 在解释性方面显示出更高的平均得分.
- 尽管性能差异很大,但没有一种单一的CAM方法能够在所有病例中始终提高兽医的诊断信心.
- 虽然一些CAM技术为特定病理提供了更好的视觉线索,但整体解释性和诊断支持有限.
结论:
- 目前的CAM方法在兽医放射学中提供了不同程度的视觉解释性,但并不能显著提高诊断信心.
- 需要进一步的研究来开发更有效的XAI技术,以适应兽医诊断成像.
- 该研究强调,需要在专业医疗领域为XAI建立强大的评估框架.
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