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Abstract #3275

Multi-layer backpropagation of classification information with Grad-CAM to enhance the interpretation of deep learning modelsĀ 

Daphne Hong1 and Yunyan Zhang1
1University of Calgary, Calgary, AB, Canada

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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