Ordinal Image Segmentation using Deep Neural Networks

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2018

Autores

Kelwin Fernandes (Autor) Outra; Jaime S. Cardoso (Autor) FEUP

Ata de Conferência Internacional

Título: Proceedings of the International Joint Conference on Neural Networks

Páginas: 1-7

2018 International Joint Conference on Neural Networks, IJCNN 2018

8 July 2018 through 13 July 2018

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 9 Citações

INSPEC

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 7