E pluribus unum可以解释的卷积神经网络
George Dimas1, Eirini Cholopoulou1, Dimitris K Iakovidis2
1Department of Computer Science and Biomedical Informatics, School of Science, University of Thessaly, Lamia, Greece.
Scientific reports
|July 14, 2023
概括
本研究介绍了E pluribus unum可解释的CNN (EPU-CNN),这是一个透明的人工智能决策的新框架. EPU-CNN提供了人类可感知的解释以及准确的预测,增强了对卷积神经网络模型的信任.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 卷积神经网络 (CNN) 在高风险领域的采用受到缺乏透明度的限制.
- 现有的可解释的CNN往往无法将解释与人类感知保持一致或保持高性能.
研究的目的:
- 为了引入一个一般的框架,E pluribus unum可解释的CNN (EPU-CNN),用于创建内在可解释的CNN模型.
- 为了使CNN能够提供人类可以感知和性能竞争的解释.
主要方法:
- 开发了EPU-CNN,一个包含CNN子网络的框架,每一个处理输入图像的不同感知特征表示.
- 输出包括基于图像区域间感知特征的相对贡献的分类预测和解释.
主要成果:
- EPU-CNN模型的分类性能与现有的CNN架构相当或优于CNN架构.
- 该框架成功地产生了人类感知到的模型决策的解释.
- 根据包括医疗数据在内的各种数据集进行评估,展示了在风险敏感领域的适用性.
结论:
- 通过整合具有强大性能的可解释设计,EPU-CNN为高风险领域的透明决策提供了可行的解决方案.
- 该框架通过提供对其预测的可理解见解来提高CNN的可信度,特别是在医学等关键应用中.
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