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Data Augmentation Based on Substituting Regional MRIs Volume Scores

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11851)

Abstract

Due to difficulties in collecting sufficient training data, recent advances in neural-network-based methods have not been fully explored in the analysis of brain Magnetic Resonance Imaging (MRI). A possible solution to the limited-data issue is to augment the training set with synthetically generated data. In this paper, we propose a data augmentation strategy based on regional feature substitution. We demonstrate the advantages of this strategy with respect to training a simple neural-network-based classifier in predicting when individual youth transition from no-to-low to medium-to-heavy alcohol drinkers solely based on their volumetric MRI measurements. Based on 20-fold cross-validation, we generate more than one million synthetic samples from less than 500 subjects for each training run. The classifier achieves an accuracy of 74.1% in correctly distinguishing non-drinkers from drinkers at baseline and a 43.2% weighted accuracy in predicting the transition over a three year period (5-group classification task). Both accuracy scores are significantly better than training the classifier on the original dataset.

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Notes

  1. 1.

    https://www.niaaa.nih.gov/research/major-initiatives/national-consortium-alcohol-and-neurodevelopment-adolescence.

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Acknowledgement

This research was supported in part by NIH grants U24AA021697, AA005965, AA013521, AA026762, and National Natural Science Foundation of China grants 11501352, 61573235, 11871328.

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Correspondence to Ehsan Adeli .

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Leng, T., Zhao, Q., Yang, C., Lu, Z., Adeli, E., Pohl, K.M. (2019). Data Augmentation Based on Substituting Regional MRIs Volume Scores. In: , et al. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention. LABELS HAL-MICCAI CuRIOUS 2019 2019 2019. Lecture Notes in Computer Science(), vol 11851. Springer, Cham. https://doi.org/10.1007/978-3-030-33642-4_4

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  • DOI: https://doi.org/10.1007/978-3-030-33642-4_4

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  • Publisher Name: Springer, Cham

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