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1 Ergebnisse
1
Predicting lymphovascular invasion in clinically node-negat..:
Bao Feng
;
Zhuangsheng Liu
;
Yu Liu
...
https://www.frontiersin.org/articles/10.3389/fonc.2022.890659/full. , 2022
Link:
https://doi.org/10.3389/fonc.2022.890659
RT Journal T1 <Title>Predicting lymphovascular invasion in clinically node-negative breast cancer detected by abbreviated magnetic resonance imaging: Transfer learning vs. radiomics</Title> UL https://suche.suub.uni-bremen.de/peid=base-ftdoajarticles:oai:doaj.org_article:f15ba0272a3243818898ecc92328459a&Exemplar=1&LAN=DE A1 Bao Feng A1 Zhuangsheng Liu A1 Yu Liu A1 Yehang Chen A1 Haoyang Zhou A1 Enming Cui A1 Xiaoping Li A1 Xiangmeng Chen A1 Ronggang Li A1 Tianyou Yu A1 Ling Zhang A1 Wansheng Long PB Frontiers Media S.A. YR 2022 K1 magnetic resonance imaging K1 lymph nodes K1 breast neoplasms K1 radiomic analysis K1 transfer learning K1 Neoplasms. Tumors. Oncology. Including cancer and carcinogens K1 RC254-282 JF https://www.frontiersin.org/articles/10.3389/fonc.2022.890659/full LK http://dx.doi.org/https://doi.org/10.3389/fonc.2022.890659 DO https://doi.org/10.3389/fonc.2022.890659 SF ELIB - SuUB Bremen
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