Medical Neural Architecture Search: Survey and Taxonomy
Medical Neural Architecture Search: Survey and Taxonomy
Hadjer Benmeziane, Imane Hamzaoui, Zayneb Cherif, Kaoutar El Maghraoui
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Survey Track. Pages 7932-7940.
https://doi.org/10.24963/ijcai.2024/878
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.
Keywords:
Machine Learning: ML: Automated machine learning
Multidisciplinary Topics and Applications: MTA: Health and medicine