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Med Image Comput Comput Assist Interv. 2018 Sep;11072:329-337. doi: 10.1007/978-3-030-00931-1_38. Epub 2018 Sep 13.

Learning Generalizable Recurrent Neural Networks from Small Task-fMRI Datasets.

Author information

Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
Autism and Neurodevelopmental Disorders Institute, George Washington University and Children's National Health System, Washington, DC, USA.
Child Study Center, Yale School of Medicine, New Haven, CT, USA.
Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
Department of Electrical Engineering, Yale University, New Haven, CT, USA.


Deep learning has become the new state-of-the-art for many problems in image analysis. However, large datasets are often required for such deep networks to learn effectively. This poses a difficult challenge for many medical image analysis problems in which only a small number of subjects are available, e.g., patients undergoing a new treatment. In this work, we propose a number of approaches for learning generalizable recurrent neural networks from smaller task-fMRI datasets: 1) a resampling method for ROI-based fMRI analysis to create augmented data; 2) inclusion of a small number of non-imaging variables to provide subject-specific initialization of the recurrent neural network; and 3) selection of the most generalizable model from multiple reinitialized training runs using criteria based on only training loss. Using cross-validation to assess model performance, we demonstrate the effectiveness of the proposed methods to train recurrent neural networks from small datasets to predict treatment outcome for children with autism spectrum disorder (N = 21) and classify autistic vs. typical control subjects (N = 40) from task-fMRI scans.

[Available on 2019-09-01]
[Indexed for MEDLINE]

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