@inproceedings{karaali2022drvnet,
title = {DR-VNet: Retinal Vessel Segmentation via Dense Residual UNet}, 
author = {Ali Karaali and Rozenn Dahyot and Donal J. Sexton},
year = {2022},
booktitle = {Pattern Recognition and Artificial Intelligence},
doi = {10.1007/978-3-031-09037-0_17},
note = {Github https://github.com/alikaraali/DR-VNet, ArXivDOI:10.48550/arXiv.2111.04739},
url =  {https://arxiv.org/pdf/2111.04739.pdf}, 
abstract = {Accurate retinal vessel segmentation is an important task for many computer-aided diagnosis systems. Yet, it is still a challenging problem due to the complex vessel structures of an eye. Numerous vessel segmentation methods have been proposed recently, however more research is needed to deal with poor segmentation of thin and tiny vessels. To address this, we propose a new deep learning pipeline combining the efficiency of residual dense net blocks and, residual squeeze and excitation blocks. We validate experimentally our approach on three datasets and show that our pipeline outperforms current state of the art techniques on the sensitivity metric relevant to assess capture of small vessels.},
publisher = {Springer International Publishing},
editor = {El Yacoubi, Moun{\^i}m
and Granger, Eric
and Yuen, Pong Chi
and Pal, Umapada
and Vincent, Nicole},
isbn = {978-3-031-09037-0},
volume =  {abs/2111.04739},
month = {June},
address = {Paris, France},
archivePrefix = {arXiv},
primaryClass = {eess.IV}
}

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