The first step in a diffusion MRI pre-processing pipeline typically involves the manual removal of heavily motion-corrupted volumes. However, this process is both time consuming and potentially subjective. We propose to automate this process by training multiple deep convolutional neural networks (CNNs) and decision trees to achieve near human-level accuracy for rejection of outliers.
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