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Proceedings Paper

Anatomically informed data augmentation for functional MRI with applications to deep learning
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Paper Abstract

The application of deep learning to build accurate predictive models from functional neuroimaging data is often hindered by limited dataset sizes. Though data augmentation can help mitigate such training obstacles, most data augmentation methods have been developed for natural images as in computer vision tasks such as CIFAR, not for medical images. This work helps to fills in this gap by proposing a method for generating new functional Magnetic Resonance Images (fMRI) with realistic brain morphology. This method is tested on a challenging task of predicting antidepressant treatment response from pre-treatment task-based fMRI and demonstrates a 26% improvement in performance in predicting response using augmented images. This improvement compares favorably to state-of-the-art augmentation methods for natural images. Through an ablative test, augmentation is also shown to substantively improve performance when applied before hyperparameter optimization. These results suggest the optimal order of operations and support the role of data augmentation method for improving predictive performance in tasks using fMRI.

Paper Details

Date Published: 10 March 2020
PDF: 6 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113130T (10 March 2020); doi: 10.1117/12.2548630
Show Author Affiliations
Kevin P. Nguyen, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Cherise Chin Fatt, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Alex Treacher, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Cooper Mellema, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Madhukar H. Trivedi, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Albert Montillo, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)


Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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