Accurate Brain Age Prediction Model for Healthy Children and Adolescents using 3D-CNN and Dimensional Attention
Guozhen Hu, Qinjian Zhang, Zhi Yang, Baobin Li · 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · 2021
The brain age, estimated from the brain MRI data, is found to be a promising biomarker for human brain development and neuroanatomical aging processes. A well-performed brain age predicting model is in great demand for many applications like healthcare and disease diagnosis. In this paper, we proposed a dimensional-attention-based 3D convolutional neural network (DACNN) to estimate the biological age for developing normal brain from T1-weighted MRI, in which a dimensional attention module was designed and applied to restrain noises and increase the weights of effective voxels for feature maps. Experimental results indicated that our model significantly outperformed the best reported methods up to now. In particular, with the dilated convolution, the proposed DACNN achieved the state-of-the-art result of 1.01 MAE on a combined dataset consisted of 880 healthy children and adolescents.