[
Alsubaie, N., Trahearn, N., Raza, S., Snead, D. and Rajpoot, N. (2017). Stain deconvolution using statistical analysis of multi-resolution stain colour representation, PLOS ONE 12(1): e0169875.10.1371/journal.pone.0169875522679928076381
]Search in Google Scholar
[
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A. and Jemal, A. (2018). Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA: A Cancer Journal for Clinicians 68(6): 394–424.10.3322/caac.2149230207593
]Search in Google Scholar
[
Cui, Y., Zhang, G., Liu, Z., Xiong, Z. and Hu, J. (2018). A deep learning algorithm for one-step contour aware nuclei segmentation of histopathological images, arXiv: 1803.02786.
]Search in Google Scholar
[
Dudzińska, D. and Piórkowski, A. (2020). Tissue differentiation based on classification of morphometric features of nuclei, in H. Florez and S. Misra (Eds), Applied Informatics, Springer, Cham, pp. 420–432.10.1007/978-3-030-61702-8_29
]Search in Google Scholar
[
Fondón, I., Sarmiento, A., García, A.I., Silvestre, M., Eloy, C., Polónia, A. and Aguiar, P. (2018). Automatic classification of tissue malignancy for breast carcinoma diagnosis, Computers in Biology and Medicine 96: 41–51.10.1016/j.compbiomed.2018.03.00329544146
]Search in Google Scholar
[
Guan, H., Zhang, Y., Cheng, H.-D. and Tang, X. (2020). Bounded-abstaining classification for breast tumors in imbalanced ultrasound images, International Journal of Applied Mathematics and Computer Science 30(2): 325–336, DOI: 10.34768/amcs-2020-0025.
]Search in Google Scholar
[
Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning. Data Mining, Inference, and Prediction, Second Edition, Springer Series in Statistics, Springer, New York.10.1007/978-0-387-84858-7
]Search in Google Scholar
[
Hayakawa, T., Prasath, S., Kawanaka, H., Aronow, B. and Tsuruoka, S. (2019). Computational nuclei segmentation methods in digital pathology: A survey, Archives of Computational Methods in Engineering 28: 1–13.10.1007/s11831-019-09366-4
]Search in Google Scholar
[
Höfener, H., Homeyer, A., Weiss, N., Molin, J., Lundström, C.F. and Hahn, H.K. (2018). Deep learning nuclei detection: A simple approach can deliver state-of-the-art results, Computerized Medical Imaging and Graphics 70: 43–52.10.1016/j.compmedimag.2018.08.01030286333
]Search in Google Scholar
[
Husham, A., Hazim Alkawaz, M., Saba, T., Rehman, A. and Saleh Alghamdi, J. (2016). Automated nuclei segmentation of malignant using level sets, Microscopy Research and Technique 79(10): 993–997.10.1002/jemt.2273327476682
]Search in Google Scholar
[
Irshad, H., Veillard, A., Roux, L. and Racoceanu, D. (2014). Methods for nuclei detection, segmentation, and classification in digital histopathology: A review—Current status and future potential, IEEE Reviews in Biomedical Engineering 7: 97–114.10.1109/RBME.2013.229580424802905
]Search in Google Scholar
[
James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R, Springer Series in Statistics, Springer, New York.10.1007/978-1-4614-7138-7
]Search in Google Scholar
[
Jassem, J. and Krzakowski, M. (2018). Breast cancer, Oncology in Clinical Practice 14(4): 171–215.
]Search in Google Scholar
[
Kantavat, P., Kijsirikul, B., Songsiri, P., Fukui, K.-I. and Numao, M. (2018). Efficient decision trees for multi-class support vector machines using entropy and generalization error estimation, International Journal of Applied Mathematics and Computer Science 28(4): 705–717, DOI: 10.2478/amcs-2018-0054.10.2478/amcs-2018-0054
]Search in Google Scholar
[
Kowal, M. and Filipczuk, P. (2014). Nuclei segmentation for computer-aided diagnosis of breast cancer, International Journal of Applied Mathematics and Computer Science 24(1): 19–31, DOI: 10.2478/amcs-2014-0002.10.2478/amcs-2014-0002
]Search in Google Scholar
[
Kowal, M., Skobel, M. and Nowicki, N. (2018). The feature selection problem in computer-assisted cytology, International Journal of Applied Mathematics and Computer Science 28(4): 759–770, DOI: 10.2478/amcs-2018-0058.10.2478/amcs-2018-0058
]Search in Google Scholar
[
Koyuncu, C.F., Akhan, E., Ersahin, T., Cetin-Atalay, R. and Gunduz-Demir, C. (2016). Iterative h-minima-based marker-controlled watershed for cell nucleus segmentation, Cytometry Part A 89(4): 338–349.10.1002/cyto.a.2282426945784
]Search in Google Scholar
[
Landini, G., Rueden, C., Schindelin, J., Hiner, M. and Pavie, B. (2004). Image colour deconvolution, https://imagej.net/Colour_Deconvolution.
]Search in Google Scholar
[
Litherland, J.C. (2002). Should fine needle aspiration cytology in breast assessment be abandoned?, Clinical Radiology 57(2): 81–84.10.1053/crad.2001.087511977938
]Search in Google Scholar
[
Macenko, M., Niethammer, M., Marron, J.S., Borland, D., Woosley, J.T., Guan, X., Schmitt, C. and Thomas, N.E. (2009). A method for normalizing histology slides for quantitative analysis, IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), Boston, USA, pp. 1107–1110.
]Search in Google Scholar
[
Mittal, H. and Saraswat, M. (2019). An automatic nuclei segmentation method using intelligent gravitational search algorithm based superpixel clustering, Swarm and Evolutionary Computation 45: 15–32.10.1016/j.swevo.2018.12.005
]Search in Google Scholar
[
Naylor, P., Laé, M., Reyal, F. and Walter, T. (2017). Nuclei segmentation in histopathology images using deep neural networks, IEEE 14th International Symposium on Biomedical Imaging, Melbourne, Australia, pp. 933–936.
]Search in Google Scholar
[
Paramanandam, M., O‘Byrne, M., Ghosh, B., Mammen, J.J., Manipadam, M.T., Thamburaj, R. and Pakrashi, V. (2016). Automated segmentation of nuclei in breast cancer histopathology images, PLOS ONE 11(9): 1–15.10.1371/journal.pone.0162053502986627649496
]Search in Google Scholar
[
Piorkowski, A. and Gertych, A. (2019). Color normalization approach to adjust nuclei segmentation in images of hematoxylin and eosin stained tissue, in E. Piętka et al. (Eds), Information Technology in Biomedicine, Springer, Cham, pp. 393–406.10.1007/978-3-319-91211-0_35
]Search in Google Scholar
[
R Core Team (2019). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, https://www.R-project.org.
]Search in Google Scholar
[
Rabinovich, A., Agarwal, S., Laris, C., Price, J. and Belongie, S. (2004). Unsupervised color decomposition of histologically stained tissue samples, in S. Thrun et al. (Eds), Advances in Neural Information Processing Systems 16, MIT Press, Cambridge, pp. 667–674.
]Search in Google Scholar
[
Ronneberger, O., P.Fischer and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation, in N. Navab et al. (Eds), Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer, Cham, pp. 234–241.10.1007/978-3-319-24574-4_28
]Search in Google Scholar
[
Ruifrok, A.C. and Johnston, D.A. (2001). Quantification of histochemical staining by color deconvolution, Analytical & Quantitative Cytology & Histology 23(4): 291–299.
]Search in Google Scholar
[
Sadanandan, S.K., Ranefall, P., Le Guyader, S. and Wahlby, C. (2017). Automated training of deep convolutional neural networks for cell segmentation, Scientific Reports 7(7860): 1–7.10.1038/s41598-017-07599-6555280028798336
]Search in Google Scholar
[
Santanu, R., Alok, J., Shyam, L. and Jyoti, K. (2018). A study about color normalization methods for histopathology images, Micron 114: 42–61.10.1016/j.micron.2018.07.00530096632
]Search in Google Scholar
[
Schindelin, J., Arganda-Carreras, I. Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., Tinevez, J.-Y., White, D.J., Hartenstein, V., Eliceiri, K., Tomancak, P. and Albert A. (2012). FIJI: An open-source platform for biological-image analysis, Nature Methods 9: 676–682.10.1038/nmeth.2019385584422743772
]Search in Google Scholar
[
Skobel, M., Kowal, M., Korbicz, J. and Obuchowicz, A. (2019). Cell nuclei segmentation using marker-controlled watershed and Bayesian object recognition, in E. Piętka et al. (Eds), International Conference on Information Technologies in Biomedicine, Springer International Publishing, Cham, pp. 407–418.10.1007/978-3-319-91211-0_36
]Search in Google Scholar
[
Spanhol, F.A., Oliveira, L.S., Petitjean, C. and Heutte, L. (2016). Breast cancer histopathological image classification using convolutional neural networks, International Joint Conference on Neural Networks, Vancouver, Canada, pp. 2560–2567.
]Search in Google Scholar
[
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society: Series B (Methodological) 58(1): 267–288.10.1111/j.2517-6161.1996.tb02080.x
]Search in Google Scholar
[
Veta, M., van Diest, P.J., Kornegoor, R., Huisman, A., Viergever, M.A. and Pluim, J.P.W. (2013). Automatic nuclei segmentation in H&E stained breast cancer histopathology images, PLOS ONE 8(7):e70221.10.1371/journal.pone.0070221372642123922958
]Search in Google Scholar
[
Vincent, L. and Soille, P. (1991). Watersheds in digital spaces: An efficient algorithm based on immersion simulations, IEEE Transactions on Pattern Analysis and Machine Intelligence 13(6): 583–598.10.1109/34.87344
]Search in Google Scholar
[
Wang, D., Khosla, A., Gargeya, R., Irshad, H. and Beck, A.H. (2016). Deep learning for identifying metastatic breast cancer, arXiv: 1606.05718.
]Search in Google Scholar
[
Xing, F. and Yang, L. (2016). Robust nucleus/cell detection and segmentation in digital pathology and microscopy images: A comprehensive review, IEEE Reviews in Biomedical Engineering 9: 234–263.10.1109/RBME.2016.2515127523346126742143
]Search in Google Scholar
[
Yang, X., Li, H. and Zhou, X. (2006). Nuclei segmentation using marker-controlled watershed, tracking using mean-shift, and Kalman filter in time-lapse microscopy, IEEE Transactions on Circuits and Systems I: Regular Papers 53(11): 2405–2414.10.1109/TCSI.2006.884469
]Search in Google Scholar
