Jonathan Frawley
Segmentation of macular edema datasets with small residual 3D U-Net architectures
Frawley, Jonathan; Willcocks, Chris G.; Habib, Maged; Geenen, Caspar; Steel, David H.W.; Obara, Boguslaw
Authors
Dr Chris Willcocks christopher.g.willcocks@durham.ac.uk
Associate Professor
Maged Habib
Caspar Geenen
David H.W. Steel
Boguslaw Obara
Abstract
This paper investigates the application of deep convolutional neural networks with prohibitively small datasets to the problem of macular edema segmentation. In particular, we investigate several different heavily regularized architectures. We find that, contrary to popular belief, neural architectures within this application setting are able to achieve close to human-level performance on unseen test images without requiring large numbers of training examples. Annotating these 3D datasets is difficult, with multiple criteria required. It takes an experienced clinician two days to annotate a single 3D image, whereas our trained model achieves similar performance in less than a second. We found that an approach which uses targeted dataset augmentation, alongside architectural simplification with an emphasis on residual design, has acceptable generalization performance- despite relying on fewer than 15 training examples.
Citation
Frawley, J., Willcocks, C. G., Habib, M., Geenen, C., Steel, D. H., & Obara, B. (2020). Segmentation of macular edema datasets with small residual 3D U-Net architectures. . https://doi.org/10.1109/bibe50027.2020.00100
Presentation Conference Type | Conference Paper (Published) |
---|---|
Conference Name | 20th IEEE International Conference on BioInformatics and BioEngineering |
Start Date | Oct 26, 2020 |
End Date | Oct 28, 2020 |
Acceptance Date | Aug 12, 2020 |
Online Publication Date | Dec 16, 2020 |
Publication Date | 2020 |
Deposit Date | Aug 12, 2020 |
Publicly Available Date | Aug 12, 2020 |
Publisher | Institute of Electrical and Electronics Engineers |
DOI | https://doi.org/10.1109/bibe50027.2020.00100 |
Public URL | https://durham-repository.worktribe.com/output/1140835 |
Files
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(7.2 Mb)
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Copyright Statement
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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