EMONAS: Efficient multiobjective neural architecture search framework for 3D medical image segmentation

Maria G. Baldeon Calisto, Susana K. Lai-Yuen

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

2 Scopus citations

Abstract

Deep learning plays a critical role in medical image segmentation. Nevertheless, manually designing a neural network for a specific segmentation problem is a very difficult and time-consuming task due to the massive hyperparameter search space, long training time and large volumetric data. Therefore, most designed networks are highly complex, task specific and over-parametrized. Recently, multiobjective neural architecture search (NAS) methods have been proposed to automate the design of accurate and efficient segmentation architectures. However, they only search for either the macro- or micro-structure of the architecture, do not use the information produced during the optimization process to increase the efficiency of the search, and do not consider the volumetric nature of medical images. In this work, we propose EMONAS, an Efficient MultiObjective Neural Architecture Search framework for 3D medical image segmentation. EMONAS is composed of a search space that considers both the macro- and micro-structure of the architecture, and a surrogate-assisted multiobjective evolutionary based algorithm that efficiently searches for the best hyperparameters using a Random Forest surrogate and guiding selection probabilities. EMONAS is evaluated on the task of cardiac segmentation from the ACDC MICCAI challenge. The architecture found is ranked within the top 10 submissions in all evaluation metrics, performing better or comparable to other approaches while reducing the search time by more than 50% and having considerably fewer number of parameters.

Original languageEnglish
Title of host publicationMedical Imaging 2021
Subtitle of host publicationImage Processing
EditorsIvana Isgum, Bennett A. Landman
PublisherSPIE
ISBN (Electronic)9781510640214
DOIs
StatePublished - 2021
Externally publishedYes
EventMedical Imaging 2021: Image Processing - Virtual, Online, United States
Duration: 15 Feb 202119 Feb 2021

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11596
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2021: Image Processing
Country/TerritoryUnited States
CityVirtual, Online
Period15/02/2119/02/21

Keywords

  • Deep Learning
  • Hyperparameter Optimization
  • Medical Image Segmentation
  • Multiobjective Optimization
  • Neural Architecture Search

Fingerprint

Dive into the research topics of 'EMONAS: Efficient multiobjective neural architecture search framework for 3D medical image segmentation'. Together they form a unique fingerprint.

Cite this