Accurate brain age prediction with lightweight deep neural networks.

Medical Image Analysis
Han PengStephen M Smith

Abstract

Deep learning has huge potential for accurate disease prediction with neuroimaging data, but the prediction performance is often limited by training-dataset size and computing memory requirements. To address this, we propose a deep convolutional neural network model, Simple Fully Convolutional Network (SFCN), for accurate prediction of brain age using T1-weighted structural MRI data. Compared with other popular deep network architectures, SFCN has fewer parameters, so is more compatible with small dataset size and 3D volume data. The network architecture was combined with several techniques for boosting performance, including data augmentation, pre-training, model regularization, model ensemble and prediction bias correction. We compared our overall SFCN approach with several widely-used machine learning models. It achieved state-of-the-art performance in UK Biobank data (N = 14,503), with mean absolute error (MAE) = 2.14y in brain age prediction and 99.5% in sex classification. SFCN also won (both parts of) the 2019 Predictive Analysis Challenge for brain age prediction, involving 79 competing teams (N = 2,638, MAE = 2.90y). We describe here the details of our approach, and its optimisation and validation. Our approach can eas...Continue Reading

References

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Citations

Dec 22, 2020·Frontiers in Psychiatry·Pedro F Da CostaWalter H L Pinaya
Apr 10, 2021·Frontiers in Psychiatry·Chen-Yuan KuoChing-Po Lin
Jun 4, 2021·Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society·Pierre BessonS Kathleen Bandt
Jun 15, 2021·Frontiers in Neuroscience·Angela LombardiSabina Tangaro
Jul 27, 2021·Medical Image Analysis·Tian XiaUNKNOWN Alzheimer’s Disease Neuroimaging Initiative
Oct 10, 2021·Human Brain Mapping·Dani BeckLars T Westlye

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Software Mentioned

SFCN
glmnet
scikit
ADAM
VGGNet
learn
FSLVBM
Elastic

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