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Stain variation is a phenomenon observed when distinct pathology laboratories stain tissue slides that exhibit similar but not identical color appearance.
Image processing by linear interpolation and extrapolation
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Exploring automatic prostate histopathology image gleason grading via local structure modeling, in: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE. pp. 2649–2652
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Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images
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Stain specific standardization of whole-slide histopathological images
Bejnordi, B.E., Litjens, G., Timofeeva, N., Otte-Höller, I., Homeyer, A., Karssemeijer, N., van der Laak, J.A., 2016 · 2016
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Deep Learning
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Multi-class texture analysis in colorectal cancer histology
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Deconvolution and checkerboard artifacts
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Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
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Context-based normalization of histological stains using deep convolutional features, in: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Springer, pp. 135–142
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Gland segmentation in colon histology images: The glas challenge contest
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Machine learning methods for histopathological image analysis
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Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks
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Stain normalization of histopathology images using generative adversarial networks, in: International Symposium on Biomedical Imaging, IEEE. pp. 573–577
Zanjani, F.G., Zinger, S., Bejnordi, B.E., van der Laak, J.A., de With, P.H., 2018 · 2018
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From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge
Bándi, P., Geessink, O., Manson, Q., van Dijk, M., Balkenhol, M., Hermsen, M., Bejnordi, B.E., Lee, B., Paeng, K., Zhong, A., et al., 2019 · 2019
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Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard
Bulten, W., Bándi, P., Hoven, J., van de Loo, R., Lotz, J., Weiss, N., van der Laak, J., van Ginneken, B., Hulsbergen-van de Kaa, C., Litjens, G., 2019 · 2019
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Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge
Veta, M., Heng, Y.J., Stathonikos, N., Bejnordi, B.E., Beca, F., Wollmann, T., Rohr, K., Shah, M.A., Wang, D., Rousson, M., et al., 2019 · 2019
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