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Clinical Scene Segmentation with Tiny Datasets

Smith, Thomas J.; Sharkey, Don; Crowe, John; Valstar, Michel

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Authors

Thomas J. Smith

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DON SHARKEY don.sharkey@nottingham.ac.uk
Professor of Neonatal Medicine and Technologies

John Crowe

Michel Valstar



Abstract

Many clinical procedures could benefit from automatic scene segmentation and subsequent action recognition. Using Convolutional Neural Networks to semantically segment meaningful parts of an image or video is still an unsolved problem. This becomes even more apparent when only a small dataset is available. Whilst using RGB as the input is sufficient for a large labelled dataset, achieving high accuracy on a small dataset directly from RGB is difficult. This is because the ratio of free image dimensions to the number of training images is very high, resulting in unavoidable underfitting. We show that the addition of su-perpixels to represent an image in our network improves the semantic segmentation, and that superpixels can be learned to be detected by Convolutional Neural Networks if those superpixels are appropriately represented. Here we present a novel representation for superpixels, multi-channel connected graphs (MCGs). We show how using pre-trained deep learned superpixels used in an end-to-end manner achieve good semantic segmentation results without the need for large quantities of labelled data, by training with only 20 instances for 23 classes.

Citation

Smith, T. J., Sharkey, D., Crowe, J., & Valstar, M. (2019). Clinical Scene Segmentation with Tiny Datasets. In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) (1637-1645). https://doi.org/10.1109/ICCVW.2019.00203

Conference Name 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
Conference Location Seoul, Korea (South)
Start Date Oct 27, 2019
End Date Oct 28, 2019
Acceptance Date Aug 20, 2019
Online Publication Date Mar 5, 2020
Publication Date 2019-10
Deposit Date Nov 1, 2019
Publicly Available Date Nov 1, 2019
Pages 1637-1645
Series ISSN 2473-9944
Book Title 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
ISBN 978-1-7281-5024-6
DOI https://doi.org/10.1109/ICCVW.2019.00203
Public URL https://nottingham-repository.worktribe.com/output/3010129
Publisher URL https://ieeexplore.ieee.org/document/9022549
Related Public URLs http://iccv2019.thecvf.com/
https://ieeexplore.ieee.org/Xplore/home.jsp

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