Ordinal Image Segmentation using Deep Neural Networks

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Kelwin Fernandes (Author) Other; Jaime S. Cardoso (Author) FEUP

Conference proceedings International

Title: Proceedings of the International Joint Conference on Neural Networks

Pages: 1-7

2018 International Joint Conference on Neural Networks, IJCNN 2018

8 July 2018 through 13 July 2018

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 6 Citations

INSPEC

Other information

Authenticus ID: P-00R-R6M

DOI: 10.1109/ijcnn.2018.8489527

Abstract (EN): Ordinal arrangement of objects is a common property in biomedical images. Traditional methods to deal with semantic image segmentation in this setting are ad-hoc and application specific. In this paper, we propose ordinal-aware deep learning architectures for image segmentation that enforce pixelwise consistency by construction. We validated the proposed architectures on several real-life biomedical datasets and achieved competitive results in all cases. © 2018 IEEE.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7