Tensor Distribution Regression Based on the 3D Conventional Neural Networks
Dear Editor,
This letter presents a novel tensor-distribution-regression model based on 3D conventional neural networks(3D-TDR)with an appli-cation to clinical score prediction in aging-related diagnosis.The estimation of clinical scores of subjects using brain magnetic reso-nance 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 diffi-cult 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 differ-ent 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 con-ventional 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 distribu-tion 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.
distribution、tensor、based、conventional、networks、neural、regression
10
S;TP39;X591
2023-07-06（万方平台首次上网日期，不代表论文的发表时间）
共4页
1628-1631
