Developing Explainable Machine Learning Model using Augmented Concept Activation Vector
This work addresses the need for explainable AI in medical imaging, though it appears incremental as it builds on existing concept activation methods.
The paper tackled the problem of explaining machine learning decisions by measuring correlations between high-level concepts and model outputs, achieving quantitative measurement of radiomic pattern impacts on fundus image classifications.
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.