Localized fine-tuning and clinical evaluation of deep-learning based auto-segmentation (DLAS) model...
Localized fine-tuning and clinical evaluation of deep-learning based auto-segmentation (DLAS) model for clinical target volume (CTV) and organs-at-risk (OAR) in rectal cancer radiotherapy
About this item
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Author / Creator
Geng, Jianhao , Sui, Xin , Du, Rongxu , Feng, Jialin , Wang, Ruoxi , Wang, Meijiao , Yao, Kaining , Chen, Qi , Bai, Lu , Wang, Shaobin , Li, Yongheng , Wu, Hao , Hu, Xiangmin and Du, Yi
Publisher
England: BioMed Central
Journal title
Language
English
Formats
Publication information
Publisher
England: BioMed Central
Subjects
More information
Scope and Contents
Contents
Various deep learning auto-segmentation (DLAS) models have been proposed, some of which have been commercialized. However, the issue of performance degradation is notable when pretrained models are deployed in the clinic. This study aims to enhance precision of a popular commercial DLAS product in rectal cancer radiotherapy by localized fine-tuning...
Alternative Titles
Full title
Localized fine-tuning and clinical evaluation of deep-learning based auto-segmentation (DLAS) model for clinical target volume (CTV) and organs-at-risk (OAR) in rectal cancer radiotherapy
Authors, Artists and Contributors
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_c7051cc823014f7e92f062d9aa84a7de
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_c7051cc823014f7e92f062d9aa84a7de
Other Identifiers
ISSN
1748-717X
E-ISSN
1748-717X
DOI
10.1186/s13014-024-02463-0
