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Abstract #2858

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

Annika Liebgott1,2, Damian Boborzi2, Sergios Gatidis1, Fritz Schick3, Konstantin Nikolaou1, Bin Yang2, and Thomas K├╝stner2,3

1Department of Diagnostic and Interventional Radiology, University Hospital of Tuebingen, Tuebingen, Germany, 2Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany, 3Section on Experimental Radiology, University Hospital of Tuebingen, Tuebingen, Germany

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.

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