Active Learning Strategies on a Real-World Thyroid Ultrasound Dataset

Machine learning applications in ultrasound imaging are limited by access to ground-truth expert annotations, especially in specialized applications such as thyroid nodule evaluation. Active learning strategies seek to alleviate this concern by making more effective use of expert annotations; however, many proposed techniques do not adapt well to small-scale (i.e. a few hundred images) datasets. In this work, we test active learning strategies including an uncertainty-weighted selection approach with supervised and semi-supervised learning to evaluate the effectiveness of these tools for the prediction of nodule presence on a clinical ultrasound dataset. The results on this as well as two other medical image datasets suggest that even successful active learning strategies have limited clinical significance in terms of reducing annotation burden.