Optimising a Simple Fully Convolutional Network (SFCN) for accurate brain age prediction in the PAC 2019 challenge

• Published in: bioRxiv
• Main Authors: Gong, Weikang, Beckmann, Christian F, Vedaldi, Andrea, Smith, Stephen M, Han, Peng
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 11.11.2020; Cold Spring Harbor Laboratory
• Edition: 1.1
• Subjects: Age; Aging; Bias; Magnetic resonance imaging; Neural networks; Neuroscience; Prediction models; Transfer learning; brain imaging; deep learning; big data; predictive analysis; convolution neural network; brain age prediction
• ISSN: 2692-8205; 2692-8205
• DOI: 10.1101/2020.11.10.376970

Abstract Brain age prediction from brain MRI scans not only helps improve brain ageing modelling generally, but also provides benchmarks for predictive analysis methods. Brain-age delta, which is the difference between a subject’s predicted age and true age, has become a meaningful biomarker for the health of the brain. Here, we report the details of our brain age prediction models and results in the Predictive Analysis Challenge 2019. The aim of the challenge was to use T1-weighted brain MRIs to predict a subject’s age in multicentre datasets. We apply a lightweight deep convolutional neural network architecture, Simple Fully Convolutional Neural Network (SFCN), and combined several techniques including data augmentation, transfer learning, model ensemble, and bias correction for brain age prediction. The model achieved first places in both of the two objectives in the PAC 2019 brain age prediction challenge: Mean absolute error (MAE) = 2.90 years without bias removal, and MAE = 2.95 years with bias removal. Competing Interest Statement The authors have declared no competing interest. Footnotes * https://github.com/ha-ha-ha-han/UKBiobank_deep_pretrain