Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification

TL;DR

This paper introduces an adversarial counterfactual augmentation method that enhances Alzheimer's disease classification by generating targeted synthetic brain images to improve model robustness and generalization.

Contribution

It proposes a novel adversarial augmentation scheme that identifies and overcomes classifier weaknesses using a pre-trained generative model in medical image analysis.

Findings

Improves Alzheimer's disease classification accuracy.

Reduces spurious correlations and catastrophic forgetting.

Demonstrates effectiveness through extensive experiments.

Abstract

Due to the limited availability of medical data, deep learning approaches for medical image analysis tend to generalise poorly to unseen data. Augmenting data during training with random transformations has been shown to help and became a ubiquitous technique for training neural networks. Here, we propose a novel adversarial counterfactual augmentation scheme that aims at finding the most \textit{effective} synthesised images to improve downstream tasks, given a pre-trained generative model. Specifically, we construct an adversarial game where we update the input \textit{conditional factor} of the generator and the downstream \textit{classifier} with gradient backpropagation alternatively and iteratively. This can be viewed as finding the `\textit{weakness}' of the classifier and purposely forcing it to \textit{overcome} its weakness via the generative model. To demonstrate the effectiveness of the proposed approach, we validate the method with the classification of Alzheimer's Disease (AD) as a downstream task. The pre-trained generative model synthesises brain images using age as conditional factor. Extensive experiments and ablation studies have been performed to show that the proposed approach improves classification performance and has potential to alleviate spurious correlations and catastrophic forgetting. Code will be released upon acceptance.