An investigation into the risk of population bias in deep learning autocontouring.

McQuinlan, Yasmin; Brouwer, Charlotte L; Lin, Zhixiong; Gan, Yong; Sung Kim, Jin; van Elmpt, Wouter; Gooding, Mark J

McQuinlan, Yasmin; Brouwer, Charlotte L; Lin, Zhixiong; Gan, Yong; Sung Kim, Jin; van Elmpt, Wouter; Gooding, Mark J.

Affiliation

McQuinlan Y; Mirada Medical Ltd, Oxford, United Kingdom. Electronic address: yasmin.mcquinlan@mirada-medical.com.
Brouwer CL; University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands. Electronic address: c.l.brouwer@umcg.nl.
Lin Z; Shantou University Medical Centre, Guangdong, China. Electronic address: zxlin5@qq.com.
Gan Y; Shantou University Medical Centre, Guangdong, China. Electronic address: y.gan@umcg.nl.
Sung Kim J; Yonsei University Health System, Seoul, Republic of Korea. Electronic address: jinsung@yuhs.ac.
van Elmpt W; Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands. Electronic address: wouter.vanelmpt@maastro.nl.
Gooding MJ; Mirada Medical Ltd, Oxford, United Kingdom; Inpictura Ltd, Oxford, United Kingdom. Electronic address: mark.gooding@inpicturamedica.com.

Radiother Oncol ; 186: 109747, 2023 09.

Article in En | MEDLINE | ID: mdl-37330053

Subject(s)
Key words

Autocontouring; Bias; Deep learning; Organ-at-Risk; Radiotherapy; Segmentation

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Deep Learning Type of study: Etiology_studies / Guideline / Qualitative_research / Risk_factors_studies Limits: Humans Country/Region as subject: Europa Language: En Journal: Radiother Oncol Year: 2023 Document type: Article

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