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Authors: Yassir Houreh 1 ; Mahsa Mahdinejad 2 ; 1 ; Enrique Naredo 2 ; 1 ; Douglas Mota Dias 2 ; 3 ; 1 and Conor Ryan 2 ; 1

Affiliations: 1 University of Limerick, Castletroy, Limerick, Ireland ; 2 Lero – Science Foundation Ireland Research Centre for Software, Ireland ; 3 UERJ – Rio de Janeiro State University, Brazil

Keyword(s): Neural Architecture Search, Image Segmentation, U-Net.

Abstract: Deep learning is a well suited approach to successfully address image processing and there are several Neural Networks architectures proposed on this research field, one interesting example is the U-net architecture and and its variants. This work proposes to automatically find the best architecture combination from a set of the current most relevant U-net architectures by using a genetic algorithm (GA) applied to solve the Retinal Blood Vessel Segmentation (RVS), which it is relevant to diagnose and cure blindness in diabetes patients. Interestingly, the experimental results show that avoiding human-bias in the design, GA finds novel combinations of U-net architectures, which at first sight seems to be complex but it turns out to be smaller, reaching competitive performance than the manually designed architectures and reducing considerably the computational effort to evolve them.

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Paper citation in several formats:
Houreh, Y.; Mahdinejad, M.; Naredo, E.; Dias, D. and Ryan, C. (2021). HNAS: Hyper Neural Architecture Search for Image Segmentation. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-484-8; ISSN 2184-433X, SciTePress, pages 246-256. DOI: 10.5220/0010260902460256

@conference{icaart21,
author={Yassir Houreh. and Mahsa Mahdinejad. and Enrique Naredo. and Douglas Mota Dias. and Conor Ryan.},
title={HNAS: Hyper Neural Architecture Search for Image Segmentation},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2021},
pages={246-256},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010260902460256},
isbn={978-989-758-484-8},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - HNAS: Hyper Neural Architecture Search for Image Segmentation
SN - 978-989-758-484-8
IS - 2184-433X
AU - Houreh, Y.
AU - Mahdinejad, M.
AU - Naredo, E.
AU - Dias, D.
AU - Ryan, C.
PY - 2021
SP - 246
EP - 256
DO - 10.5220/0010260902460256
PB - SciTePress

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