相关实验视频
对于3D深度学习模型的内在和后期可解释性的跨领域基准
Asmita Chakraborty1, Gizem Karagoz1, Nirvana Meratnia1
1Department of Mathematics and Computer Science, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands.
Journal of imaging
|February 26, 2026
概括
这项研究引入了一个统一的框架,用于对3D数据中的可解释AI (XAI) 进行基准测试. 结果显示,没有任何一个XAI方法在所有领域都表现出色,这强调了对特定领域的评估的需要.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 医疗成像医学成像
背景情况:
- 对于3D数据的深度学习在医学成像,物体识别和机器人技术中很普遍.
- 越来越多的人工智能使用需要可解释性,因为模型的黑子性质.
- 缺乏标准化的基准标准阻碍了对3D数据可解释AI (XAI) 方法的可靠比较.
研究的目的:
- 提出一个统一的基准测试框架,用于评估各种3D数据集上的内在和后期XAI方法.
- 在医学CT扫描,CAD模型和点云中对各种XAI技术的性能进行定量评估.
- 为在不同3D数据领域选择合适的XAI方法提供比较见解.
主要方法:
- 开发了一个统一的对比框架,用于3D可解释性.
- 在CT扫描 (MosMed),CAD模型 (ModelNet40) 和点云 (ScanObjectNN) 上评估了XAI方法 (Grad-CAM,集成梯度,突出度,阻塞,ResAttNet-3D).
- 使用正确性 (AOPC),完整性 (AUPC) 和紧性指标评估解释质量.
主要成果:
- 在不同方法和领域,解释质量差异很大.
- 在医学CT扫描中,Grad-CAM和内在注意力表现最好.
- 基于梯度的方法在基于voxelized和基于点的数据上显示出优异的性能.
- 统计测试证实了方法之间的显著性能差异.
结论:
- 在所有3D数据领域中,没有一个XAI方法是普遍优越的.
- 对特定领域的评估和多度量评估对于选择有效的XAI技术至关重要.
- 拟议的框架允许对3D可解释性进行可重复和标准化的评估.
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