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A semantically flexible feature fusion network for retinal vessel segmentation
conference contribution
posted on 2020-01-01, 00:00 authored by Tariq Khan, Antonio Robles-KellyAntonio Robles-Kelly, S S NaqviThe 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.
History
Event
Neural Information Processing. International Conference (27th : 2020 : Online from Bangkok, Thailand)Volume
1332Series
Neural Information Processing International ConferencePagination
159 - 167Publisher
SpringerLocation
Online from Bangkok, ThailandPlace of publication
Cham, SwitzerlandPublisher DOI
Start date
2020-11-18End date
2020-11-22ISSN
1865-0929eISSN
1865-0937ISBN-13
9783030638191Language
engPublication classification
E1 Full written paper - refereedEditor/Contributor(s)
H Yang, K Pasupa, A Leung, J Kwok, J Chan, I KingTitle of proceedings
ICONIP 2020 : Proceedings of the 27th International Conference on Neural Information Processing 2020Usage metrics
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