使用热图可视化分析基于YOLO的乳腺癌检测的可解释性
Awika Ariyametkul1, May Phu Paing1
1Department of Biomedical Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand.
Quantitative imaging in medicine and surgery
|July 29, 2025
概括
这项研究使用YOLO11和可解释AI (XAI) 增强了乳腺癌检测. YOLO11实现了高精度,而HiResCAM提供了清晰的视觉解释,提高了AI在医学成像中的可靠性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 乳腺癌是妇女死亡的主要原因,需要通过乳房扫描进行早期检测.
- 人工智能 (AI),特别是你只看一次 (YOLO) 模型,在医疗图像分析中显示出希望.
- 当前的人工智能模型往往缺乏透明度,阻碍了临床应用中的可信度.
研究的目的:
- 用INbreast和MIAS数据集评估YOLOv9,YOLOv10和YOLO11用于乳腺癌检测的性能.
- 将七种可解释的人工智能 (XAI) 方法与表现最好的YOLO模型集成和评估.
- 为了提高人工智能驱动的乳腺癌检测的透明度和可信度.
主要方法:
- 在INbreast和MIAS乳房造 mammography数据集上接受了YOLOv9,YOLOv10和YOLO11的训练.
- 根据平均平均精度 (mAP),精度和回忆,选择了性能最高的YOLO模型.
- 整合了七种XAI方法 (Grad-CAM,Grad-CAM++,Eigen-CAM,EigenGrad-CAM,XGrad-CAM,LayerCAM,HiResCAM) 来实现模型的可解释性.
主要成果:
- YOLO11获得了最高的mAP (0.935),表现优于YOLOv9 (0.868) 和YOLOv10 (0.926).
- 对于良性病例,YOLO11的分类准确度为95%,恶性病例为80%.
- HiResCAM提供了最有效的视觉解释,达到0.49.49的最高匹配地面真相 (mGT) 得分.
结论:
- YOLO11和HiResCAM的组合为乳腺癌检测提供了高精度和更好的解释性.
- 这种综合方法通过可视化AI决策过程来增强用户的信任.
- 这些发现为改进人工智能模型和改善它们在医学诊断中的性能提供了洞察力.
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