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Artificial intelligence approaches in histology
- Authors: Yasnov A.O.1, Remez A.I.1, Mayer A.O.1
-
Affiliations:
- UNIM Ltd
- Issue: Vol 3, No 1S (2022)
- Pages: 20-20
- Section: Conference Proceenings
- URL: https://jdigitaldiagnostics.com/DD/article/view/106787
- DOI: https://doi.org/10.17816/DD105702
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About the authors
A. O. Yasnov
UNIM Ltd
Author for correspondence.
Email: yasnov.artur@gmail.com
Russian Federation, Moscow
A. I. Remez
UNIM Ltd
Email: yasnov.artur@gmail.com
Russian Federation, Moscow
A. O. Mayer
UNIM Ltd
Email: yasnov.artur@gmail.com
Russian Federation, Moscow
References
- Ioffe S, Szegedy Ch. Batch normalization: accelerating deep network training by reducing internal covariate shift. ICML’15: Proceedings of the 32nd International Conference on Machine Learning. July 2015. Vol. 37. Available from: https://doi.org/10.48550/arXiv.1502.03167
- Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab N, Hornegger J, Wells W, Frangi A, editors. Medical Image Computing and Computer-Assisted Intervention ― MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, vol 9351. Cham: Springer; 2015. doi: 10.1007/978-3-319-24574-4_28
- Xie S, Girshick R, Dollár P, Tu Zh, He K. Aggregated Residual Transformations for Deep Neural Networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017. doi: 10.1109/cvpr.2017.634
