Data set of annotated histology images on breast pathology is provided, containing more than 40 thousand images from 104 microscopic slides and 92 patients and additional clinical data (age, TNM, grade, WHO type). The data set is prepared in compliance with relevant procedures for clinical research at Burnasyan Federal Medical Biophysical Center Of Federal Medical Biological Agency. The data set is accessible at GitHub for research and educational purposes.
References
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2. Esteva A., Kuprel B., Novoa R. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017; 542: 115–118. doi:10.1038/nature21056.
3. Tanaka T., Huang Y., Marukawa Y. et al. Differentiation of Small (≤ 4 cm) Renal Masses on Multiphase Contrast-Enhanced CT by Deep Learning [published correction appears in AJR Am J Roentgenol. 2020; 214(4): 945]. AJR Am J Roentgenol. 2020; 214(3): 605–612. doi:10.2214/AJR.19.22074.
4. Drokin I.S., Ericheva E.V., Buhvalov O.L., Pilyus P.S., Malygina T.S., Sinicyn V.E. Opyt razrabotki i vnedreniya sistemy poiska onkologicheskih obrazovanij s pomoshch'yu iskusstvennogo intellekta na primere rentgenovskoj komp'yuternoj tomografii legkih. Vrach i informacionnye tekhnologii. 2019; 3: 48–57. (In Russ).
5. Hägele M., Seegerer P., Lapuschkin S. et al. Resolving challenges in deep learning-based analyses of histopathological images using explanation methods. Sci Rep. 2020: 10: 6423. doi:10.1038/ s41598 020 62724 2.
6. Litjens G., Sánchez C., Timofeeva N. et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep. 2016; 6, 26286. doi:10.1038/srep26286
7. Holzinger A. et al. Towards the augmented pathologist: Challenges of explainable-ai in digital pathology. arXiv 2017; 1712.06657: 1–34.
8. Spanhol F.A., Oliveira L.S., Petitjean C., Heutte L. “A Dataset for Breast Cancer Histopathological Image Classification» in IEEE Transactions on Biomedical Engineering. 2016; 63(7):1455–1462. doi:10.1109/TBME.2015.2496264.
9. Aksac A., Demetrick D.J., Ozyer T., Alhajj R. BreCaHAD : a dataset for breast cancer histopathological annotation and diagnosis. BMC Res Notes. 2019; 12(1): 82. doi:10.1186/s13104 019 4121 7.
10. Aresta G., Araújo T., Kwok S. et al. BACH: Grand challenge on breast cancer histology images. Med Image Anal. 2019; 56: 122–139. doi:10.1016/j.media.2019.05.010.
11. Yao H., Zhang X., Zhou X., Liu S. Parallel Structure Deep Neural Network Using CNN and RNN with an Attention Mechanism for Breast Cancer Histology Image Classification. Cancers (Basel). 2019; 11(12): 1901. doi:10.3390/cancers11121901.
12. Alzubaidi L., Al- Shamma O., Fadhel M.A., Farhan L., Zhang J., Duan Y. Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model. Electronics. 2020; 9: 445. doi:10.3390/electronics9030445.
13. Qi Q., Li Y., Wang J., Zheng H., Huang Y., Ding X. et al. «Label-Efficient Breast Cancer Histopathological Image Classification,» in IEEE Journal of Biomedical and Health Informatics. 2019; 23(5): 2108–2116. doi : 10.1109/JBHI.2018.2885134.
14. Bankhead P., Loughrey M.B., Fernández J.A. et al. QuPath : Open source software for digital pathology image analysis. Sci Rep. 2017; 7: 16878. doi:10.1038/s41598 017 17204 5.
15. Lakhani S.R., Ellis I.O., Schnitt S.J., Tan P.H., Van de Vijver M.J., editors. WHO Classification of Tumours of the Breast. Fourth ed. IARC. Lyon, 2012. – 240 p.
16. Elston C.W., Ellis I.O. Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology. 1991; 19(5): 403–410. doi:10.1111/j.1365–2559.1991.tb00229.x.
17. Sloane J.P., Amendoeira I., Apostolikas N. et al. Consistency achieved by 23 European pathologists in categorizing ductal carcinoma in situ of the breast using five classifications. European Commission Working Group on Breast Screening Pathology. Hum Pathol. 1998; 29(10): 1056–1062. doi : 10.1007/ s004280050297.
18. Brajerli Dzh.D., Gospodarovich M. K., Vittekind K. TNM Klassifikaciya zlokachestvennyh opuholej. Logosfera , 2018. – 344 s. (In Russ).
19. Christian Szegedy S., Vanhoucke V., Ioffe S., Shlens J., Wojna Z. Rethinking the Inception Architecture for Computer Vision. arXiv. 2015. Available at: https://arxiv.org/1512.00567 Last accessed on 15.06.2020.
20. He K., Zhang X., Ren S., Sun J. Deep Residual Learning for Image Recognition. 2015. Available at : https://arxiv. org /1512.03385 Last accessed on 15.06.2020.
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