Transfer Learning for Neuroimaging via Re-use of Deep Neural Network Features

A major problem in the application of machine learning to neuroimaging is the technological variability of MRI scanners and differences in the subject populations across studies. Transfer learning (TL) attempts to alleviate this problem. TL refers to a family of methods which acquire knowledge from related tasks to improve generalization in the tasks of interest. In this work, we pre-train a deep neural network on UK Biobank MRI data on age and sex prediction, and study the predictions of the network on three small MRI data sets. We find that the neural networks can extract meaningful features from unseen data sets under the necessary but also sufficient condition that the network was pre-trained to predict the label of interest (e.g. pre-trained on age prediction if age prediction is the task of interest). Based on this, we propose a transfer learning pipeline which relies on the re-use of deep neural network features across data sets for the same tasks. We find that our method outperforms classical regression methods and training a network from scratch. In particular, we improve state-of-the-art results on age and sex prediction. Our transfer learning method may therefore provide a simple and efficient pipeline to achieve high performance on small MRI data sets. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by the Rhodes Trust in support of Peter Holderrieth. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study used (or will use) ONLY openly available human data that were originally located at - http://www.ukbiobank.ac.uk - https://fcon_1000.projects.nitrc.org/indi/abide/ - https://brain-development.org/ixi-dataset/ - https://www.oasis-brains.org/ I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced are available online at - http://www.ukbiobank.ac.uk - https://fcon_1000.projects.nitrc.org/indi/abide/ - https://brain-development.org/ixi-dataset/ - https://www.oasis-brains.org/