Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Olasubomi J. Omoleye"'
Publikováno v:
South African Journal of Radiology, Vol 26, Iss 1 (2022)
No abstract available.
Externí odkaz:
https://doaj.org/article/405e04b87136494eb881ce8479f9d74a
Publikováno v:
South African Journal of Radiology, Vol 25, Iss 1, Pp e1-e7 (2021)
Background: Radiology subspecialisation is well-established in much of Europe, North America, and Australasia. It is a natural evolution of the radiology speciality catalysed by multiple factors. Objectives: The aim of this article is to analyse and
Externí odkaz:
https://doaj.org/article/6c0df94823454eb4a3d23290cd9d220a
Autor:
Olasubomi J. Omoleye, Anna Woodard, Fangyuan Zhao, Maksim Levental, Toshio F. Yoshimatsu, Yonglan Zheng, Olufunmilayo I. Olopade, Dezheng Huo
Publikováno v:
Cancer Research. 82:1933-1933
Imaging-based machine learning models are promising tools for breast cancer risk prediction. Validating these models across diverse cohorts is necessary to establish performance and spur clinical implementation. We conducted an independent, external
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eb2ae95a69c8a13f39263deb016a9ec7
https://doi.org/10.1158/1538-7445.am2022-1933
https://doi.org/10.1158/1538-7445.am2022-1933
Autor:
Anna Woodard, Olasubomi J. Omoleye, Rachna Gupta, Fangyuan Zhao, Aarthi Koripelly, Ian Foster, Kyle Chard, Toshio F. Yoshimatsu, Yonglan Zheng, Dezheng Huo, Olufunmilayo I. Olopade
Publikováno v:
Cancer Research. 82:5047-5047
Background: Personalized breast cancer (BC) screening adjusts the imaging modality and frequency of exams according to a woman's risk of developing BC. This can lower cost and false positives by reducing unnecessary exams and has the potential to fin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f196b74ef4f9dfdf26db69ffc59d3b00
https://doi.org/10.1158/1538-7445.am2022-5047
https://doi.org/10.1158/1538-7445.am2022-5047