Active Learning by Discrepancy Minimization: A Comparison of Active Learning Methods Motivated by Generalization Bounds

In many settings in practice it is expensive to obtain labeled data while unlabeled data is abundant. This is problematic if one wants to train accurate (supervised) predictive models. The main idea behind active learning is that models can perform better with less labeled data, if the model may choose the data from which it learns. Active...

Interactive 3D Segmentation of CT-scans of the Hip Joint, Using Active Learning to Select Delineation Planes

This paper evaluates a method that improves segmentation e ciency by intelligently suggesting planes where correction is most valuable. An existing method is extended to work for segmentation of multiple bones simultaneously. This method is evaluated because in clinical practice it is often necessary that scans are segmented very accurate. When...