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Abstract #0811

Data augmentation using features from activation maps improved performance for deep learning based automated knee prescription

Deepa Anand1, Dattesh Shanbhag1, Preetham Shankpal1, Chitresh Bhushan2, Desmond Teck Beng Yeo2, Thomas K Foo2, and Radhika Madhavan2
1GE Healthcare, Bangalore, India, 2GE Global Research, Niskayuna, NY, United States

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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