@inproceedings{e7ca8cbea87643f7b4fd6ed3a6ed7945,
title = "Deep Convolutional Artery/Vein Classification of Retinal Vessels",
abstract = "The classification of retinal vessels into arteries and veins in eye fundus images is a relevant task for the automatic assessment of vascular changes. This paper presents a new approach to solve this problem by means of a Fully-Connected Convolutional Neural Network that is specifically adapted for artery/vein classification. For this, a loss function that focuses only on pixels belonging to the retinal vessel tree is built. The relevance of providing the model with different chromatic components of the source images is also analyzed. The performance of the proposed method is evaluated on the RITE dataset of retinal images, achieving promising results, with an accuracy of 96 \% on large caliber vessels, and an overall accuracy of 84 \%.",
author = "Meyer, \{Maria Ines\} and Adrian Galdran and Pedro Costa and Mendon{\c c}a, \{Ana Maria\} and Aur{\'e}lio Campilho",
note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG, part of Springer Nature.; 15th International Conference on Image Analysis and Recognition, ICIAR 2018 ; Conference date: 27-06-2018 Through 29-06-2018",
year = "2018",
doi = "10.1007/978-3-319-93000-8\_71",
language = "English",
isbn = "9783319929996",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "622--630",
editor = "\{ter Haar Romeny\}, Bart and Fakhri Karray and Aurelio Campilho",
booktitle = "Image Analysis and Recognition - 15th International Conference, ICIAR 2018, Proceedings",
address = "Germany",
}