Active learning for automated reference-free MR image quality assessment: decreasing the number of required training samples by reduction of intra-batch redundancy.

Active learning aims to reduce the amount of labeled data required to adequately train a classifier by iteratively selecting samples carrying the most valuable information for the training process. In this study, we investigate the influence of redundancy within the batch of selected samples per iteration, aiming to further reduce the amount of labeled data for automated assessment of MR image quality. An SVM and a DNN are trained with images labeled by radiologists according to the perceived image quality. Approaches to reduce redundancy are compared. Results indicate that reducing the intra-batch correlation for SVM needs fewest labeled samples.