Barragán-Montero, Ana https://orcid.org/0000-0002-9485-3076
Bibal, Adrien https://orcid.org/0000-0002-8650-8635
Dastarac, Margerie Huet https://orcid.org/0000-0001-5605-5973
Draguet, Camille https://orcid.org/0000-0003-4034-7896
Valdés, Gilmer
Nguyen, Dan https://orcid.org/0000-0002-9590-0655
Willems, Siri
Vandewinckele, Liesbeth
Holmström, Mats
Löfman, Fredrik
Souris, Kevin
Sterpin, Edmond https://orcid.org/0000-0001-9764-546X
Lee, John A https://orcid.org/0000-0001-5218-759X
Funding for this research was provided by:
Cancer Prevention and Research Institute of Texas (RP150485)
NIH Clinical Center (R01CA237269)
National Institute of Biomedical Imaging and Bioengineering (KO8EB026500)
Fédération Wallonie-Bruxelles (MECHATECH/BIOWIN)
Fonds Wetenschappelijk Onderzoek (1SA6121N)
Article Title: Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency
Journal Title: Physics in Medicine & Biology
Article Type: paper
Copyright Information: © 2022 The Author(s). Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd
Publication dates
Date Received: 2021-11-30
Date Accepted: 2022-04-14
Online publication date: 2022-05-27