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DIAGNOSTICS (2022)
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Ke Zhao et al.
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CELLULAR ONCOLOGY (2019)
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PLOS MEDICINE (2019)
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AMERICAN JOURNAL OF NEURORADIOLOGY (2018)
Scoring the tumor-stroma ratio in colon cancer: procedure and recommendations
G. W. van Pelt et al.
VIRCHOWS ARCHIV (2018)
Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning
Nicolas Coudray et al.
NATURE MEDICINE (2018)
Novel digital signatures of tissue phenotypes for predicting distant metastasis in colorectal cancer
Korsuk Sirinukunwattana et al.
SCIENTIFIC REPORTS (2018)
Comparison of deep transfer learning strategies for digital pathology
Romain Mormont et al.
PROCEEDINGS 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW) (2018)
A survey on deep learning in medical image analysis
Geert Litjens et al.
MEDICAL IMAGE ANALYSIS (2017)
QuPath: Open source software for digital pathology image analysis
Peter Bankhead et al.
SCIENTIFIC REPORTS (2017)
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky et al.
INTERNATIONAL JOURNAL OF COMPUTER VISION (2015)
The relationship between tumour stroma percentage, the tumour microenvironment and survival in patients with primary operable colorectal cancer
J. H. Park et al.
ANNALS OF ONCOLOGY (2014)
The proportion of tumor-stroma as a strong prognosticator for stage II and III colon cancer patients: validation in the VICTOR trial
A. Huijbers et al.
ANNALS OF ONCOLOGY (2013)
Tumor-Stroma Ratio Is an Independent Predictor for Survival in Esophageal Squamous Cell Carcinoma
Kai Wang et al.
JOURNAL OF THORACIC ONCOLOGY (2012)
The proportion of tumour cells is an independent predictor for survival in colorectal cancer patients
N. P. West et al.
BRITISH JOURNAL OF CANCER (2010)