Single-view Cotraining and Active Learning for Concurrent Medical Activity Labeling

Labeling medical activities from visual datasets requires extensive time and effort for practitioners. Deep learning methods have been widely used to train partially labeled datasets for predicting activity labels. Cotraining, a semi-supervised learning technique that utilizes predictions from two different models, is used in conjunction with active learning to mitigate sampling bias. There is a need to optimize manual labeling efforts with active learning while improving the prediction performance. In this paper, we developed the scale-tag methodology, based on single-view cotraining and active learning, to label concurrent activities in video records cost-effectively. scale-tag utilized the cosine similarity metric to enhance diversity within the subsets and selected informative samples based on prediction uncertainty for retraining the models. We evaluated the performance of scale-tag for six trauma resuscitation activities. During the training stage, the average labeling time required for selected samples was 10.93% of the manual annotation time for all unlabeled samples. We then applied scale-tag to a test set. The F1 scores for test samples were over 0.7 for four activities, where the labeling time required for ambiguous predictions of test samples was 8% for one of them, 17% for another, and around 20% for the other two. The improvement in prediction accuracy and decrease in labeling time we obtained with scale-tag are promising for developing deep learning-based methods to support healthcare providers with effective activity labeling.