A semantically flexible feature fusion network for retinal vessel segmentation

Khan, Tariq M, Robles-Kelly, Antonio and Naqvi, Syed S 2020, A semantically flexible feature fusion network for retinal vessel segmentation, in ICONIP 2020 : Proceedings of the 27th International Conference on Neural Information Processing 2020, Springer, Cham, Switzerland, pp. 159-167, doi: 10.1007/978-3-030-63820-7_18.

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Title A semantically flexible feature fusion network for retinal vessel segmentation
Author(s) Khan, Tariq MORCID iD for Khan, Tariq M orcid.org/0000-0002-7477-1591
Robles-Kelly, AntonioORCID iD for Robles-Kelly, Antonio orcid.org/0000-0002-2465-5971
Naqvi, Syed S
Conference name Neural Information Processing. International Conference (27th : 2020 : Online from Bangkok, Thailand)
Conference location Online from Bangkok, Thailand
Conference dates 2020/11/18 - 2020/11/22
Title of proceedings ICONIP 2020 : Proceedings of the 27th International Conference on Neural Information Processing 2020
Editor(s) Yang, H
Pasupa, K
Leung, AC-S
Kwok, JT
Chan, JH
King, I
Publication date 2020
Series Neural Information Processing International Conference
Start page 159
End page 167
Total pages 9
Publisher Springer
Place of publication Cham, Switzerland
Keyword(s) Image segmentation
Retinal vessels
Deep neural networks
Medical image analysis
Summary The automatic detection of retinal blood vessels by computer aided techniques plays an important role in the diagnosis of diabetic retinopathy, glaucoma, and macular degeneration. In this paper we present a semantically flexible feature fusion network that employs residual skip connections between adjacent neurons to improve retinal vessel detection. This yields a method that can be trained employing residual learning. To illustrate the utility of our method for retinal blood vessel detection, we show results on two publicly available data sets, i.e. DRIVE and STARE. In our experimental evaluation we include widely used evaluation metrics and compare our results with those yielded by alternatives elsewhere in the literature. In our experiments, our method is quite competitive, delivering a margin of sensitivity and accuracy improvement as compared to the alternatives under consideration.
Notes This conference was originally scheduled to be held in Bangkok, Thailand, however due to the 2020 COVID Pandemic, the event was held online.
ISBN 9783030638191
ISSN 1865-0929
1865-0937
Language eng
DOI 10.1007/978-3-030-63820-7_18
Indigenous content off
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30146511

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