  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.
