INSITE: labelling medical images using submodular functions and semi-supervised data programming

• URL: http://arxiv.org/abs/2402.07173v1
• Date: Sun, 11 Feb 2024 12:02:00 GMT
• Title: INSITE: labelling medical images using submodular functions and semi-supervised data programming
• Authors: Akshat Gautam, Anurag Shandilya, Akshit Srivastava, Venkatapathy Subramanian, Ganesh Ramakrishnan, Kshitij Jadhav
• Abstract summary: Large amounts of labeled data to train deep models creates an implementation bottleneck in resource-constrained settings. We apply informed subset selection to identify a small number of most representative or diverse images from a huge pool of unlabelled data. The newly annotated images are then used as exemplars to develop several data programming-driven labeling functions.
• Score: 19.88996560236578
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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