Grammar-Guided Evolution of the U-Net

Mahsa Mahdinejad, Aidan Murphy, Michael Tetteh, Allan de Lima, Patrick Healy, Conor Ryan

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

Abstract

Deep learning is an effective and efficient method for image segmentation. Several neural network designs have been investigated, a notable example being the U-Net which has outperformed other segmentation models in different challenges. The Spatial Attention U-Net is a variation of the U-Net, which utilizes DropBlock and an attention block in addition to the typical U-Net convolutional blocks, which boosted the accuracy of the U-Net and reduced over-fitting. Optimising neural networks is costly, time-consuming and often requires expert guidance to determine the best mix of hyper-parameters for a particular problem. We demonstrate that grammatical evolution (GE) can be used to create U-Net and Spatial Attention U-Net architectures and optimise its choice of hyper-parameters. Our results show improved performance over state-of-the-art models on the Retinal Blood Vessel problem, increasing both AUC, from 0.978 to 0.979 and Accuracy, from 0.964 0.966, from the base models. Crucially, GE can achieve these improvements while finding a model which is 10 times smaller than the base models. A smaller model would enable its use in smart devices, such as smart phones, or in edge computing.

Original languageEnglish
Title of host publicationApplications of Evolutionary Computation - 26th European Conference, EvoApplications 2023, Held as Part of EvoStar 2023, Proceedings
EditorsJoão Correia, Stephen Smith, Raneem Qaddoura
PublisherSpringer Science and Business Media Deutschland GmbH
Pages672-686
Number of pages15
ISBN (Print)9783031302282
DOIs
Publication statusPublished - 2023
Event26th International Conference on Applications of Evolutionary Computation, EvoApplications 2023, held as part of EvoStar 2023 - Brno, Czech Republic
Duration: 12 Apr 202314 Apr 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13989 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th International Conference on Applications of Evolutionary Computation, EvoApplications 2023, held as part of EvoStar 2023
Country/TerritoryCzech Republic
CityBrno
Period12/04/2314/04/23

Keywords

  • Deep Learning
  • Evolutionary Algorithm
  • Grammatical Evolution
  • Image Segmentation

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