Active Learning Performance in Labeling Radiology Images Is 90% Effective - Bangert, Patrick; Moon, Hankyu; Woo, Jae Oh; Didari, Sima; Hao, Heng (Frontiers in Radiology, 2021)
To train AI systems on radiology images, an image labeling step is necessary. Labeling for radiology images usually involves a human radiologist manually drawing a (polygonal) shape onto the image and attaching a word to it. As datasets are typically large, this task is repetitive, time-consuming, error-prone, and expensive. The AI methodology of active learning can assist human labelers by continuously sorting the unlabeled images in order of information gain and thus getting the labeler always to label the most informative image next. We find that after about 10%, depending on the dataset, of the images in a realistic dataset are labeled, virtually all the information content has been learnt and the remaining images can be automatically labeled. These images can then be checked by the radiologist, which is far easier and faster to do. In this way, the entire dataset is labeled with much less human effort. We introduce active learning in detail and expose the effectiveness using several three real-life datasets. We contribute five distinct elements to the standard active learning workflow creating an advanced methodology.