Medical Neural Architecture Search: Survey and Taxonomy

Hadjer Benmeziane; Imane Hamzaoui; Zayneb Cherif; Kaoutar El Maghraoui

IJCAI 2024poster3 citations

Medical Neural Architecture Search: Survey and Taxonomy

Hadjer Benmeziane, Imane Hamzaoui, Zayneb Cherif, Kaoutar El Maghraoui

Abstract

This paper presents a comprehensive survey of Medical Neural Architecture Search (MedNAS), a burgeoning field at the confluence of deep learning and medical imaging. With the increasing prevalence of FDA-approved medical deep learning models, MedNAS emerges as a key area in leveraging computational innovations for healthcare advancements. Our survey examines the paradigm shift introduced by Neural Architecture Search (NAS), which automates neural network design, replacing traditional, manual designs. We explore the unique search spaces tailored for medical tasks on different types of data from images to EEG, the methodologies of MedNAS, and their impact on medical applications.

Machine Learning: ML: Automated machine learningMultidisciplinary Topics and Applications: MTA: Health and medicine

BibTeX

@inproceedings{ijcai2024p878,
  title     = {Medical Neural Architecture Search: Survey and Taxonomy},
  author    = {Benmeziane, Hadjer and Hamzaoui, Imane and Cherif, Zayneb and El Maghraoui, Kaoutar},
  booktitle = {Proceedings of the Thirty-Third International Joint Conference on
               Artificial Intelligence, {IJCAI-24}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Kate Larson},
  pages     = {7932--7940},
  year      = {2024},
  month     = {8},
  note      = {Survey Track},
  doi       = {10.24963/ijcai.2024/878},
  url       = {https://doi.org/10.24963/ijcai.2024/878},
}