Data augmentation techniques have been routinely used in computer vision for simulating variations in input data and avoid overfitting. Here we propose a novel method to generate simulated images using features derived from activation maps of a deep neural network, which could mimic image variations due to MRI acquisition and hardware. Gradient-weighted Class Activation Mappings were used to identify regions important to classification output, and generate images with these regions obfuscated to mimic adversarial scenarios relevant for imaging variations. Training with images using the proposed data augmentation framework resulted in improved accuracy and enhanced robustness of knee MRI image classification.
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