Adversarial counterfactual augmentation: application in Alzheimer's disease classification

Area of Science:

• Medical Image Analysis
• Deep Learning
• Artificial Intelligence

Background:

• Limited medical data hinders deep learning generalization in medical image analysis.
• Data augmentation with random transformations is a common but potentially suboptimal technique.
• Existing methods may not generate the most effective synthetic data for specific downstream tasks.

Purpose of the Study:

• To propose a novel adversarial counterfactual augmentation scheme for improving deep learning models in medical image analysis.
• To generate effective synthetic medical images that enhance the performance of downstream classification tasks.
• To address the generalization limitations of deep learning models due to data scarcity.

Main Methods:

• Developed an adversarial game between a generator and a classifier.
• Iteratively updated the generator's conditional factor and the classifier using gradient backpropagation.
• Utilized a pre-trained generative model to synthesize brain images conditioned on age for Alzheimer's Disease classification.
• Validated the approach on the Alzheimer's Disease classification task.

Main Results:

• The proposed adversarial counterfactual augmentation significantly improved classification performance.
• Demonstrated the method's potential to alleviate spurious correlations in medical data.
• Showcased the approach's ability to mitigate catastrophic forgetting in deep learning models.
• Ablation studies confirmed the effectiveness of the adversarial augmentation strategy.

Conclusions:

• Adversarial counterfactual augmentation is an effective strategy for enhancing deep learning models in medical image analysis.
• The method successfully generates targeted synthetic data to overcome classifier weaknesses.
• This approach offers a promising direction for improving model robustness and generalization in data-limited medical domains.