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The development of reliable imaging biomarkers for the analysis of colorectal cancer (CRC) in hematoxylin and eosin (H&E) stained histopathology images requires an accurate and reproducible classification of the main tissue components in the image.
“A method for normalizing histology slides for quantitative analysis,”
Marc Macenko et al., · 2009
Earlier work this paper cites.
“Tumor necrosis is a new promising prognostic factor in colorectal cancer,”
Marion Pollheimer et al., · 2010
Earlier work this paper cites.
“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., · 2013
Earlier work this paper cites.
“A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution,”
A. M. Khan, N. Rajpoot, D. Treanor, and D. Magee, · 2014
Cited alongside, same era.
“Very deep convolutional networks for large-scale image recognition,”
Karen Simonyan and Andrew Zisserman, · 2014
Cited alongside, same era.
“Deep Learning,”
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton, · 2015
Cited alongside, same era.
“Fully convolutional networks for semantic segmentation,”
Jonathan Long, Evan Shelhamer, and Trevor Darrell, · 2015
Later among the works it cites.
“Multi-class texture analysis in colorectal cancer histology,”
Jakob Nikolas Kather et al., · 2016
Later among the works it cites.
“Stain specific standardization of whole-slide histopathological images,”
Babak Ehteshami Bejnordi et al., · 2016
Later among the works it cites.
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