A deep ensemble hippocampal CNN model for brain age estimation applied to Alzheimer’s diagnosis
作者：
Highlights：
• Hippocampal age estimation using an efficient 3D CNN architecture.
• Data augmentation with an oversample over the age bins to obtain even distribution.
• End-to-end framework to process new images within less than seven minutes.
• Age-matched analyses with distinct aging effects and stages of neuro diseases.
• Significant correlation between brain-predicted age delta error and clinical score.
摘要
•Hippocampal age estimation using an efficient 3D CNN architecture.•Data augmentation with an oversample over the age bins to obtain even distribution.•End-to-end framework to process new images within less than seven minutes.•Age-matched analyses with distinct aging effects and stages of neuro diseases.•Significant correlation between brain-predicted age delta error and clinical score.
论文关键词：Brain-age estimation,Age biomarker,Alzheimer’s disease,Mild cognitive impairment,Deep Learning,Convolutional Neural Networks
论文评审过程：Received 26 October 2021, Revised 22 December 2021, Accepted 27 January 2022, Available online 9 February 2022, Version of Record 11 February 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.116622
