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pro vyhledávání: '"Yechan Mun"'
Publikováno v:
npj Digital Medicine, Vol 4, Iss 1, Pp 1-9 (2021)
Abstract The Gleason score contributes significantly in predicting prostate cancer outcomes and selecting the appropriate treatment option, which is affected by well-known inter-observer variations. We present a novel deep learning-based automated Gl
Externí odkaz:
https://doaj.org/article/08a6d46ceef34a1fb3100d3ff605886b
Publikováno v:
NPJ Digital Medicine
npj Digital Medicine, Vol 4, Iss 1, Pp 1-9 (2021)
npj Digital Medicine, Vol 4, Iss 1, Pp 1-9 (2021)
The Gleason score contributes significantly in predicting prostate cancer outcomes and selecting the appropriate treatment option, which is affected by well-known inter-observer variations. We present a novel deep learning-based automated Gleason gra
Autor:
Yechan Mun, Sun Woo Kim, Jeong Hwan Park, Tae-Yeong Kwak, Hyeyoon Chang, Han Suk Ryu, Sangjun Oh, Min Sun Jin, Cho Joon Young, Junwoo Isaac Woo, Soohyun Hwang, Sanghun Lee, Su Jin Shin
Publikováno v:
Cancers
Volume 11
Issue 12
Volume 11
Issue 12
The Gleason grading system, currently the most powerful prognostic predictor of prostate cancer, is based solely on the tumor&rsquo
s histological architecture and has high inter-observer variability. We propose an automated Gleason scoring syst
s histological architecture and has high inter-observer variability. We propose an automated Gleason scoring syst