Deep learning is becoming increasingly important in medical imaging analysis, but the ability to interpret deep learning models still lag behind. Here, based on a promising method, gradient-weighted class activation mapping (Grad-CAM), we developed new approaches to interpret arbitrary layers of a convolutional neural network (CNN). Further, using two common CNN models trained to classify brain MRI scans into 3 types, we demonstrated the promise of our new strategy. Characterizing features at low and high levels of a CNN may provide new biomarkers and new insight into disease mechanisms, deserving further validation.
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