INSITE: labelling medical images using submodular functions and semi-supervised data programming
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913563161919488 |
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| author | Gautam, Akshat Shandilya, Anurag Srivastava, Akshit Subramanian, Venkatapathy Ramakrishnan, Ganesh Jadhav, Kshitij |
| author_facet | Gautam, Akshat Shandilya, Anurag Srivastava, Akshit Subramanian, Venkatapathy Ramakrishnan, Ganesh Jadhav, Kshitij |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_07173 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | INSITE: labelling medical images using submodular functions and semi-supervised data programming Gautam, Akshat Shandilya, Anurag Srivastava, Akshit Subramanian, Venkatapathy Ramakrishnan, Ganesh Jadhav, Kshitij Computer Vision and Pattern Recognition 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. |
| title | INSITE: labelling medical images using submodular functions and semi-supervised data programming |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.07173 |