Performance deterioration of deep learning models after clinical deployment: a case study with auto-segmentation for definitive prostate cancer radiotherapy

Our study aims to explore the long-term performance patterns for deep learning (DL) models deployed in clinic and to investigate their efficacy in relation to evolving clinical practices. We conducted a retrospective study simulating the clinical implementation of our DL model involving 1328 prostat...

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Detalles Bibliográficos

Principais autores:	Biling Wang, Michael Dohopolski, Ti Bai, Junjie Wu, Raquibul Hannan, Neil Desai, Aurelie Garant, Daniel Yang, Dan Nguyen, Mu-Han Lin, Robert Timmerman, Xinlei Wang, Steve B Jiang
Formato:	Artigo
Idioma:	Inglês
Publicado:	IOP Publishing 2024-01-01
Series:	Machine Learning: Science and Technology
Assuntos:	deep learning segmentation model performance deterioration radiotherapy
Acceso en liña:	https://doi.org/10.1088/2632-2153/ad580f
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