Author Correction: Automated Gleason grading of prostate cancer tissue microarrays via deep learning
Enregistré dans:
Auteurs principaux: | Eirini Arvaniti, Kim S. Fricker, Michael Moret, Niels Rupp, Thomas Hermanns, Christian Fankhauser, Norbert Wey, Peter J. Wild, Jan H. Rüschoff, Manfred Claassen |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/f76f8f8764ee4c1e88609dc3dccbe4a5 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Yet Another Automated Gleason Grading System (YAAGGS) by weakly supervised deep learning
par: Yechan Mun, et autres
Publié: (2021) -
Sensitive detection of rare disease-associated cell subsets via representation learning
par: Eirini Arvaniti, et autres
Publié: (2017) -
Significant Inter- and Intralaboratory Variation in Gleason Grading of Prostate Cancer: A Nationwide Study of 35,258 Patients in The Netherlands
par: Rachel N. Flach, et autres
Publié: (2021) -
Hypoehoic lesions on Transrectal Ultrasound and its correlation to Gleason grade in the diagnosis of Clinically Significant Prostate Cancer: A Prospective Study
par: Manas Sharma, et autres
Publié: (2021) -
Molecular signature of cancer stem cells isolated from prostate carcinoma and expression of stem markers in different Gleason grades and metastasis
par: Castellón,Enrique A, et autres
Publié: (2012)