Transfer learning improves resting-state functional connectivity pattern analysis using convolutional neural networks.

ABSTRACT:

Background

Deep learning is gaining importance in the prediction of cognitive states and brain pathology based on neuroimaging data. Including multiple hidden layers in artificial neural networks enables unprecedented predictive power; however, the proper training of deep neural networks requires thousands of exemplars. Collecting this amount of data is not feasible in typical neuroimaging experiments. A handy solution to this problem, which has largely fallen outside the scope of deep learning applications in neuroimaging, is to repurpose deep networks that have already been trained on large datasets by fine-tuning them to target datasets/tasks with fewer exemplars. Here, we investigated how this method, called transfer learning, can aid age category classification and regression

SUBMITTER: Vakli P 

PROVIDER: S-EPMC6283213 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature