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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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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Leng, T., Zhao, Q., Yang, C., Lu, Z., Adeli, E., Pohl, K.M. (2019). Data Augmentation Based on Substituting Regional MRIs Volume Scores. In: Zhou, L., 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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