Brain age prediction method based on deep convolutional generative adversarial network
10.19745/j.1003-8868.2023241
- VernacularTitle:基于深度卷积生成对抗网络的大脑年龄预测方法研究
- Author:
Min XIONG
1
;
Wen-Jie KANG
;
Lan LIN
Author Information
1. 北京工业大学化学与生命科学学院生物医学工程系智能化生理测量与临床转化北京市国际科研合作基地,北京 100124
- Keywords:
brain age prediction;
deep convolutional generative adversarial network;
convolutional neural network;
deep learning;
machine learning
- From:
Chinese Medical Equipment Journal
2023;44(12):1-6
- CountryChina
- Language:Chinese
-
Abstract:
Objective To propose a brain age prediction method based on deep convolutional generative adversarial networks(DCGAN)for objective assessment of brain health status.Methods The DCGAN model was extended from 2D to 3D and improved by integrating the concept of residual block to enhance the ability for feature extraction.The classifiers were pre-trained with unsupervised adversarial learning and fine-tuned with migration learning to eliminate the overfitting of 3D convolutional neural network(CNN)due to small sample size.To verify the effectiveness of the improved model,comparison analyses based on UK Biobank(UKB)database were carried out between the improved model and least absolute shrinkage and selection operator(LASSO)model,machine learning model,3D CNN model and graph convolutional network model by using mean absolute error(MAE)as the evaluation metric.Results The model proposed gained advantages over LASSO model,machine learning model,3D CNN model and graph convolutional network model in predicting brain age with a MAE error of 2.896 years.Conclusion The method proposed behaves well for large-scale datasets,which can predict brain age accurately and assess brain health status objectively.
