在计算机视觉中对可解释的人工智能 (XAI) 的全面审查
Zhihan Cheng1,2, Yue Wu1,2, Yule Li3,4
1Department of Mathematics, College of Science, Mathematics and Technology, Wenzhou-Kean University, Wenzhou 325060, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
在计算机视觉中的可解释的人工智能 (XAI) 方法进行了比较. 基于变压器的方法在医学成像中表现有前途,尽管需要仔细解释.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 可解释的人工智能 (XAI)
背景情况:
- 可解释的人工智能 (XAI) 对于理解复杂的计算机视觉模型至关重要.
- 现有的XAI方法缺乏标准化的比较,阻碍了有效的应用.
研究的目的:
- 进行计算机视觉中突出的XAI方法的比较分析.
- 在关键指标上评估XAI技术并提出分类系统.
主要方法:
- 将XAI方法分为基于归属的,基于激活的,基于扰动的和基于变压器的方法.
- 使用诸如忠实度,本地化准确度,效率等指标进行评估,并与医学注释重叠.
- 为 XAI 技术进行分类,制定一个层次分类法.
主要成果:
- 基于扰乱的方法 (例如,RISE) 显示出高可靠性,但在计算上是密集的.
- 基于变压器的方法在医学成像任务 (IoU分数) 中实现了高性能.
- 没有任何一种方法在所有评估的指标中脱而出,突出了权衡.
结论:
- 环境意识评估和混合XAI方法是必要的,以平衡可解释性和效率.
- 标准化的基准和特定领域的调整对于推进XAI至关重要.
- 解决伦理和实际挑战对于可靠的XAI部署至关重要.
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