Tensor Distribution Regression Based on the 3D Conventional Neural Networks

Lin Chen, Xin Luo

Abstract: Dear Editor, This letter presents a novel tensor-distribution-regression model based on 3D conventional neural networks (3D-TDR) with an application to clinical score prediction in aging-related diagnosis. The estimation of clinical scores of subjects using brain magnetic resonance imaging (MRI) helps understand the pathological stage of dementia. However, clinical scores prediction is still unsolved due to the reasons of: 1) Analyzing the whole-brain MRI is extremely difficult as the high-dimensional MRI data contains millions of voxels; 2) The clinical scores prediction is formulated as a one-dimensional regression issue in the current deep-learning-based algorithms, which ignores the implicit label information between subjects with different score levels. Motivated by the above discoveries, the proposed 3D-TDR model innovatively establishes the following three-fold ideas: a) incorporating a tensor regression layer (TRL) into a 3D conventional neural network (3D-CNN) to enable its extraction of more discriminative structural changes from the high-dimensional whole-brain magnetic resonance (MR) data; b) adopting the label distribution learning (LDL) to fully utilize the label correlation among the MR images, thus emphasizing the diversity of subjects' scores; and c) combining the TRL and LDL for an end-to-end deep learning framework, thereby achieving jointly low-rank feature extraction and clinical scores prediction. Experimental results on two real-world MRI datasets of two typical clinical prediction tasks indicate that the 3D-TDR outperforms the benchmark and state-of-the-art models. The proposed 3D-TDR model can achieve significant accuracy gain in dementia score and brain age prediction.

External IDs:dblp:journals/ieeejas/Chen023