Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI

PURPOSE: To investigate the generalizability of transfer learning (TL) of automated tumor segmentation from cervical cancers toward a universal model for cervical and uterine malignancies in diffusion-weighted magnetic resonance imaging (DWI). METHODS: In this retrospective multicenter study, we ana...

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Detalles Bibliográficos
Autores principales: Lin, Yu-Chun, Lin, Yenpo, Huang, Yen-Ling, Ho, Chih-Yi, Chiang, Hsin-Ju, Lu, Hsin-Ying, Wang, Chun-Chieh, Wang, Jiun-Jie, Ng, Shu-Hang, Lai, Chyong-Huey, Lin, Gigin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2023
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9871146/
https://www.ncbi.nlm.nih.gov/pubmed/36690870
http://dx.doi.org/10.1186/s13244-022-01356-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9871146/
https://www.ncbi.nlm.nih.gov/pubmed/36690870
http://dx.doi.org/10.1186/s13244-022-01356-8

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