HNAS: Hyper neural architecture search for image segmentation

Yassir Houreh, Mahsa Mahdinejad, Enrique Naredo, Douglas Mota Dias, Conor Ryan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence
EditorsAna Paula Rocha, Luc Steels, Jaap van den Herik
PublisherSciTePress
Pages246-256
Number of pages11
ISBN (Electronic)9789897584848
Publication statusPublished - 2021
Event13th International Conference on Agents and Artificial Intelligence, ICAART 2021 - Virtual, Online
Duration: 4 Feb 20216 Feb 2021

Publication series

NameICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence
Volume2

Conference

Conference13th International Conference on Agents and Artificial Intelligence, ICAART 2021
CityVirtual, Online
Period4/02/216/02/21

Keywords

  • Image segmentation
  • Neural architecture search
  • U-Net

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