Abstract

This paper tackles the problem of developing active learning (AL) methods in the context of knee osteoarthritis (OA) diagnosis from X-ray images. OA is known to be a huge burden for society, and its associated costs are constantly rising. Automatic diagnostic methods can potentially reduce these costs, and Deep Learning (DL) methodology may be its key enabler. To date, there have been numerous studies on knee OA severity grading using DL, and all but one of them assume a large annotated dataset available for model development. In contrast, our study shows one can develop a knee OA severity grading model using AL from as little as 50 samples randomly chosen from a pool of unlabeled data. The main insight of this work is that the performance of AL improves when the model developer leverages the consistency regularization technique, commonly applied in semi-supervised learning.