The necessity of large amounts of labeled data to train deep models, especially in medical imaging creates an implementation bottleneck in resource-constrained settings. In Insite (labelINg medical imageS usIng submodular funcTions and sEmi-supervised data programming) we apply informed subset selection to identify a small number of most representative or diverse images from a huge pool of unlabelled data subsequently annotated by a domain expert. The newly annotated images are then used as exemplars to develop several data programming-driven labeling functions. These labelling functions output a predicted-label and a similarity score when given an unlabelled image as an input. A consensus is brought amongst the outputs of these labeling functions by using a label aggregator function to assign the final predicted label to each unlabelled data point. We demonstrate that informed subset selection followed by semi-supervised data programming methods using these images as exemplars perform better than other state-of-the-art semi-supervised methods. Further, for the first time we demonstrate that this can be achieved through a small set of images used as exemplars.
View on arXiv