Feb. 13, 2024, 5:47 a.m. | Akshat Gautam Anurag Shandilya Akshit Srivastava Venkatapathy Subramanian Ganesh Ramakrishnan Kshitij Jadhav

cs.CV updates on arXiv.org arxiv.org

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. …

apply cs.cv data functions identify images imaging implementation labeling labelling medical medical imaging programming semi-supervised small train

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