量化高阶概念对模型决策的影响，提升可解释性
Developing Explainable Machine Learning Model using Augmented Concept Activation Vector
- 引入增强概念激活向量，量化高阶概念与模型决策的相关性
- 在眼底图像上验证，准确测量影像组学模式的影响程度
- 适合关注模型可解释性与医学影像分析的研究者
机器学习模型利用高维特征空间将输入映射到类别标签，但这些特征往往无法与人类可理解的物理概念一一对应，阻碍了对模型决策的有意义解释。本文提出一种衡量高阶概念与模型决策之间相关性的方法，能够隔离并定量评估特定高阶概念的影响。此外，研究还旨在识别机器学习模型中常见于不平衡数据集的频繁模式。该方法已成功应用于眼底图像，实现了对影像组学模式影响模型决策的定量测量。
原文摘要 · Abstract (English)
Machine learning models use high dimensional feature spaces to map their inputs to the corresponding class labels. However, these features often do not have a one-to-one correspondence with physical concepts understandable by humans, which hinders the ability to provide a meaningful explanation for the decisions made by these models. We propose a method for measuring the correlation between high-level concepts and the decisions made by a machine learning model. Our method can isolate the impact of a given high-level concept and accurately measure it quantitatively. Additionally, this study aims to determine the prevalence of frequent patterns in machine learning models, which often occur in imbalanced datasets. We have successfully applied the proposed method to fundus images and managed to quantitatively measure the impact of radiomic patterns on the model decisions.
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