INSITE: labelling medical images using submodular functions and
  semi-supervised data programming
Akshat Gautam Anurag Shandilya Akshit Srivastava Venkatapathy Subramanian Ganesh Ramakrishnan Kshitij Jadhav
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.