Stochastic Geometry for Automatic Assessment of Ki-67 Index in Breast Cancer Preparations

Author:

Kowal Marek,Skobel Marcin,Korbicz Józef,Monczak Roman

Publisher

Springer International Publishing

Reference18 articles.

1. Abubakar, M., et al.: High-throughput automated scoring of Ki67 in breast cancer tissue microarrays from the Breast Cancer Association Consortium. J. Pathol: Clin. Res. 2(3), 138–153 (2016)

2. Baddeley, A.J., van Lieshout, M.N.M.: ICM for object recognition. In: Dodge, Y., Whittaker, J. (eds.) Computational Statistics, vol. 2, pp. 271–286. Physica-Verlag HD, Heidelberg (1992). https://doi.org/10.1007/978-3-642-48678-4_34

3. Baddeley, A.J., van Lieshout, M.N.M.: Stochastic geometry models in high-level vision. In: Mardia, K.V., Kanji, G.K. (eds.) Advances in Applied Statistics, Statistics and Images, vol. 1, pp. 231–256. Carfax Publishing, Abingdon (1993)

4. Bembenik, R., Jóźwicki, W., Protaziuk, G.: Methods for mining co-location patterns with extended spatial objects. Int. J. Appl. Math. Comput. Sci. 27(4), 681–695 (2017)

5. Cheang, M.C., Chia, S.K., Voduc, D., Leung, S., Snider, J.: Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. J. Natl. Cancer Inst. 101(10), 736–750 (2009)

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