Patch-based deep learning approaches for artefact detection of endoscopic images
Conference paper
Gao, X. and Qian, Y. 2019. Patch-based deep learning approaches for artefact detection of endoscopic images. Endoscopic artefact detection challenge 2019 (EAD2019). Venice, Italy 08 Apr 2019 CEUR Workshop Proceedings.
Type | Conference paper |
---|---|
Title | Patch-based deep learning approaches for artefact detection of endoscopic images |
Authors | Gao, X. and Qian, Y. |
Abstract | This paper constitutes the work in EAD2019 competition. In this competition, for segmentation (task 2) of five types of artefact, patch-based fully convolutional neural network (FCN) allied to support vector machine (SVM) classifier is implemented, aiming to contend with smaller data sets (i.e., hundreds) and the characteristics of endoscopic images with limited regions capturing artefact (e.g. bubbles, specularity). In comparison with conventional CNN and other state of the art approaches (e.g. DeepLab) while processed on whole images, this patch-based FCN appears to achieve the best. |
Conference | Endoscopic artefact detection challenge 2019 (EAD2019) |
ISSN | 1613-0073 |
Publisher | CEUR Workshop Proceedings |
Publication dates | |
25 May 2019 | |
Publication process dates | |
Deposited | 26 Jun 2019 |
Accepted | 08 Apr 2019 |
Output status | Published |
Publisher's version | |
Accepted author manuscript | |
Copyright Statement | Copyright © The authors. Copying permitted for private and academic purposes. |
Additional information | Paper published in Proceedings of the 2019 Challenge on Endoscopy Artefacts Detection (EAD2019), co-located with the 16th International Symposium on Biomedical Imaging (ISBI) Edited by: Sharib Ali, Felix Zhou. CEUR Workshop Proceedings vol. 2366Published on CEUR-WS: 25-May-2019, http://ceur-ws.org/Vol-2366/ |
Web address (URL) | http://ceur-ws.org/Vol-2366/EAD2019_paper_10.pdf |
Language | English |
Book title | Proceedings of the EAD 2019 Workshop on Endoscopic Artefact Detection Challenge |
https://repository.mdx.ac.uk/item/88583
Download files
62
total views21
total downloads0
views this month1
downloads this month