{"type":"ScholarlyArticle","title":"Medical Neural Architecture Search: Survey and Taxonomy","authors":[{"name":"Hadjer Benmeziane","slug":"hadjer-benmeziane","given":"Hadjer","family":"Benmeziane","affiliation":"IBM Research Europe , 8803 Rüschlikon , Switzerland."},{"name":"Imane Hamzaoui","slug":"imane-hamzaoui","given":"Imane","family":"Hamzaoui","affiliation":"École nationale Supérieure d'Informatique , Algiers , Algeria."},{"name":"Zayneb Cherif","slug":"zayneb-cherif","given":"Zayneb","family":"Cherif","affiliation":"Yorktown High school , Yorktown Heights , 10598 , USA ."},{"name":"Kaoutar El Maghraoui","slug":"kaoutar-el-maghraoui","given":"Kaoutar","family":"El Maghraoui","affiliation":"IBM T. J. Watson Research Center , Yorktown Heights , NY 10598 , USA."}],"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.","doi":"10.24963/ijcai.2024/878","url":"https://ijcai.org/proceedings/2024/878","pdf":"https://ijcai.org/proceedings/2024/0878.pdf","markdown":"https://ijcai.org/proceedings/2024/878.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/878","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Survey Track","track":"Survey Track","number":878,"pages":{"first":7932,"last":7940},"keywords":["Multidisciplinary Topics and Applications: MTA: Health and medicine","Machine Learning: ML: Automated machine learning"],"videos":[],"references":[{"position":1,"raw":"[Antonelli et al., 2022] Michela Antonelli, Annika Reinke, and al. 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