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Machine learning algorithms have the potential to improve patient outcomes in digital pathology.
Anderson, M., Motta, R., Chandrasekar, S., Stokes, M.: Proposal for a standard default color space for the internet-srgb. In: Color Imaging Conference. vol. 6 (1996)
1996
Earlier work this paper cites.
Macenko, M., Niethammer, M., Marron, J.S., Borland, D., Woosley, J.T., Guan, X., Schmitt, C., Thomas, N.E.: A method for normalizing histology slides for quantitative analysis (2009)
2009
Earlier work this paper cites.
Weinstein, J.N., Collisson, E.A., Mills, G.B., Shaw, K.R., Ozenberger, B.A., Ellrott, K., Shmulevich, I., Sander, C., Stuart, J.M.: The cancer genome atlas pan-cancer analysis project. Nature genetics 45
2013
Earlier work this paper cites.
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V.S.: Domain-adversarial training of neural networks. In: Journal of machine learning research (2015)
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Vahadane, A., Peng, T., Sethi, A., Albarqouni, S., Wang, L., Baust, M., Steiger, K., Schlitter, A.M., Esposito, I., Navab, N.: Structure-preserving color normalization and sparse stain separation for histological images. IEEE Transactions on Medical Imaging 35
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2017
Cited alongside, same era.
Tellez, D., Litjens, G.J.S., Bándi, P., Bulten, W., Bokhorst, J.M., Ciompi, F., van der Laak, J.: Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology. Medical image analysis 58
2019
Cited alongside, same era.
Inoue, T., Yagi, Y.: Color standardization and optimization in whole slide imaging. Clinical and diagnostic pathology 4
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Bouteldja, N., Hölscher, D., Bülow, R., Roberts, I., Coppo, R., Boor, P.: Tackling stain variability using cyclegan-based stain augmentation. Journal of Pathology Informatics 13
2022
Later among the works it cites.
Breen, J., Zucker, K., Orsi, N.M., Ravikumar, N.: Assessing domain adaptation techniques for mitosis detection in multi-scanner breast cancer histopathology images. In: Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis: MICCAI 2021 Challenges: MIDOG 2021, MOOD 2021, and Learn2Reg 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27–October 1, 2021, Proceedings, pp. 14–22. Springer (2022)
2022
Later among the works it cites.
Ingale, K., Joshi, R., Ho, I., BenTaieb, A., Stumpe, M.: Effects of color calibration via icc profile on inter-scanner generalization of ai models. In: LABORATORY INVESTIGATION. vol. 102, pp. 1076–1077. SPRINGERNATURE CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND (2022)
2022
Later among the works it cites.
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Diao, J.A., Wang, J.K., Chui, W.F., Mountain, V., Gullapally, S.C., Srinivasan, R., Mitchell, R.N., Glass, B., Hoffman, S., Rao, S.K., Maheshwari, C., Lahiri, A., Prakash, A., McLoughlin, R., Kerner, J.K., Resnick, M.B., Montalto, M.C., Khosla, A., Wapinski, I.N., Beck, A.H., Elliott, H.L., Taylor-Weiner, A.: Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes. Nature Communications 2021 12:1 12
2021
Cited alongside, same era.
Fick, R.H.J., Moshayedi, A., Roy, G., Dedieu, J., Petit, S., Hadj, S.B.: Domain-specific cycle-gan augmentation improves domain generalizability for mitosis detection. In: Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis: MICCAI 2021 Challenges: MIDOG 2021, MOOD 2021, and Learn2Reg 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27–October 1, 2021, Proceedings. p. 40–47. Springer-Verlag, Berlin, Heidelberg (2021). https://doi.org/10.1007/978-3-030-97281-3_5, https://doi.org/10.1007/978-3-030-97281-3_5
2021
Cited alongside, same era.
Runz, M., Rusche, D., Schmidt, S., Weihrauch, M., Hesser, J., Weis, C.A.: Normalization of he-stained histological images using cycle consistent generative adversarial networks. Diagnostic Pathology 16
2021
Cited alongside, same era.
Marini, N., Otalora, S., Wodzinski, M., Tomassini, S., Dragoni, A.F., Marchand-Maillet, S., Morales, J.P.D., Duran-Lopez, L., Vatrano, S., Müller, H., Atzori, M.: Data-driven color augmentation for h&e stained images in computational pathology. Journal of Pathology Informatics 14
2022
Later among the works it cites.
Aubreville, M., Stathonikos, N., Bertram, C.A., Klopfleisch, R., Ter Hoeve, N., Ciompi, F., Wilm, F., Marzahl, C., Donovan, T.A., Maier, A., et al.: Mitosis domain generalization in histopathology images—the midog challenge. Medical Image Analysis 84
2023
Closest in time.
Hetz, M., Bucher, T.C., Brinker, T.: Multi-domain stain normalization for digital pathology: A cycle-consistent adversarial network for whole slide images (01 2023). https://doi.org/10.48550/arXiv.2301.09431
2023
Closest in time.