A Reparametrized CNN Model to Distinguish Alzheimer's Disease Applying Multiple Morphological Metrics and Deep Semantic Features From Structural MRI - Chen, Zhenpeng; Mo, Xiao; Chen, Rong; Feng, Pujie; Li, Haiyun (Frontiers in Aging Neuroscience, 2022)
It is of potential clinical value to improve the accuracy of AD recognition using structural MRI. We proposed a reparametrized convolutional neural network (Re-CNN) to discriminate AD from NC applying morphological metrics and deep semantic features. The deep semantic features were extracted through Re-CNN on structural MRI. Considering the high redundancy in deep semantic features, we constrained the similarity of the features and retained the most distinguishing features utilizing the reparametrized module. The Re-CNN model was trained in an end-to-end manner on structural MRI from ADNI dataset and tested on structural MRI from AIBL dataset. Our proposed model achieves better performance over some existing structural MRI-based AD recognition models. The experimental results show that morphological metrics along with the constrained deep semantic features can relatively improve AD recognition preformance. Our code is available at: https://github.com/czp19940707/Re-CNN.