Multi-scale attention-based pseudo-3D convolution neural network for Alzheimer’s disease diagnosis using structural MRI
作者：
Highlights：
• We proposed a novel method termed the “PKG-Net” to accurately predict Alzheimer’s disease.
• The input is designed to be multi-dimensional and collaboratively represents lesion area from multiple scales via the pyramid representation.
• The joint loss function is utilized to improves the practicability of the proposed network and its stability in training.
• Our method demonstrates good generalization ability, and has achieved excellent results of 97.28% in accuracy on the ADNI dataset.
摘要
•We proposed a novel method termed the “PKG-Net” to accurately predict Alzheimer’s disease.•The input is designed to be multi-dimensional and collaboratively represents lesion area from multiple scales via the pyramid representation.•The joint loss function is utilized to improves the practicability of the proposed network and its stability in training.•Our method demonstrates good generalization ability, and has achieved excellent results of 97.28% in accuracy on the ADNI dataset.
论文关键词：Diagnosis of Alzheimer’s disease,Pseudo-3D,Attention mechanism,Multi-scale,Joint loss function
论文评审过程：Received 8 September 2021, Revised 11 April 2022, Accepted 2 June 2022, Available online 3 June 2022, Version of Record 14 June 2022.
论文官网地址：https://doi.org/10.1016/j.patcog.2022.108825
