articleMedical Image AnalysisOct 19, 2020HYBRID OA

Accurate brain age prediction with lightweight deep neural networks

HPHan PengWGWeikang GongCFChristian F. BeckmannAVAndrea VedaldiSMStephen M. Smith

Wellcome Centre for Integrative Neuroimaging · University of Oxford · +2 more institutions

PubMed
Indexed incrossrefpubmed

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…

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506
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33.87
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100%
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5

Topics & keywords

Topics
Keywords
  • Computer science
  • Deep learning
  • Artificial intelligence
  • Convolutional neural network
  • Machine learning
  • Boosting (machine learning)
  • Artificial neural network
  • Neuroimaging
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