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AUTOMATED METHOD FOR OPTIMUM SCALE SEARCH WHEN USING TRAINED MODELS FOR HISTOLOGICAL IMAGE ANALYSIS
- Authors: PENKIN M.A.1, KHVOSTIKOV A.V.1, KRYLOV A.S.1
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Affiliations:
- Laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and Cybernetics, Moscow State University
- Issue: No 3 (2023)
- Pages: 49-55
- Section: COMPUTER GRAFICS AND VISUALIZATION
- URL: https://ruspoj.com/0132-3474/article/view/675748
- DOI: https://doi.org/10.31857/S0132347423030032
- EDN: https://elibrary.ru/DEIZNX
- ID: 675748
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Abstract
Preparation of input data for an artificial neural network is a key step to achieve a high accuracy of its predictions. It is well known that convolutional neural models have low invariance to changes in the scale of input data. For instance, processing multiscale whole-slide histological images by convolutional neural networks naturally poses a problem of choosing an optimal processing scale. In this paper, this problem is solved by iterative analysis of distances to a separating hyperplane that are generated by a convolutional classifier at different input scales. The proposed method is tested on the DenseNet121 deep architecture pretrained on PATH-DT-MSU data, which implements patch classification of whole-slide histological images.
About the authors
M. A. PENKIN
Laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and Cybernetics, Moscow State University
Email: penkin97@gmail.com
Moscow, Russia
A. V. KHVOSTIKOV
Laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and Cybernetics, Moscow State University
Email: khvostikov@cs.msu.ru
Moscow, Russia
A. S. KRYLOV
Laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and Cybernetics, Moscow State University
Author for correspondence.
Email: kryl@cs.msu.ru
Moscow, Russia
References
- Park S., Pantanowitz L., Parwani A.V. Digital imaging in pathology // Clinics in laboratory medicine. 2012. M. 32. № 4. C. 557–584.
- Pantanowitz L., Valenstein P.N., Evans A.J., Kaplan K.J., Pfeifer J.D., Wilbur D.C., Collins L.C., Colgan T.J. Review of the current state of whole slide imaging in pathology // Journal of pathology informatics. 2012. V. 2. № 1. P. 36.
- Saco A., Bombi J.A., Garcia A., RamГrez J., Ordi J. Current status of whole-slide imaging in education // Pathobiology. 2016. V. 83. № 2–3. P. 79–88.
- Farahani N., Parwani A.V., Pantanowitz L. Whole slide imaging in pathology: advantages, limitations, and emerging perspectives // Pathol Lab Med Int. 2015. V. 7. № 23–33. P. 4321.
- Rojo M.G., GarcГa G.B., Mateos C.P., GarcГa J.G., Vicente M.C. Critical comparison of 31 commercially available digital slide systems in pathology // International journal of surgical pathology. 2006. V. 14. № 4. P. 285–305.
- Ronneberger O., Fischer P., Brox T. U-net: Convolutional networks for biomedical image segmentation // In International Conference on Medical image computing and computer-assisted intervention. 2015. P. 234–241.
- Khvostikov A., Krylov A.S., Mikhailov I., Malkov P. CNN Assisted Hybrid Algorithm for Medical Images Segmentation // In Proceedings of the 2020 5th International Conference on Biomedical Signal and Image Processing. 2020. P. 14–19.
- Getmanskaya A.A., Sokolov N.A., Turlapov V.E. Multiclass U-Net Segmentation of Brain Electron Microscopy Data Using Original and Semi-Synthetic Training Datasets // Programming and Computer Software. 2022. V. 48. № 3. P. 164–171.
- Gong Y., Wang L., Guo R., Lazebnik S. Multi-scale orderless pooling of deep convolutional activation features // In European conference on computer vision. 2014. P. 392–407.
- Khvostikov A.V., Krylov A.S., Mikhailov I.A., Malkov P.G. Visualization of Whole Slide Histological Images with Automatic Tissue Type Recognition // Pattern Recognition and Image Analysis. 2022. V. 32. № 3. P. 483–488.
- Huang G., Liu Z., Van Der Maaten L., Weinberger K.Q. Densely connected convolutional networks // In Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. P. 4700–4708.
- Kingma D.P., Ba J. Adam: A method for stochastic optimization // arXiv preprint arXiv:1412.6980. 2014.
- He K., Zhang X., Ren S., Sun J. Deep residual learning for image recognition // Proceedings of the CVPR IEEE Conference. 2016. P. 770–778.
- Simonyan K., Zisserman A. Very deep convolutional networks for large-scale image recognition // arXiv preprint arXiv:1409.1556. 2014.
- Szegedy C., Liu W., Jia Y., Sermanet P., Reed S., Anguelov D., Erhan D., Vanhoucke V., Rabinovich A. Going deeper with convolutions // In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. P. 1–9.
- Penkin M.A., Khvostikov A.V., Krylov A.S. Optimal Input Scale Transformation Search for Deep Classification Neural Networks // In Graphicon-Conference on Computer Graphics and Vision. 2022. V. 32. P. 668–677.
- Krizhevsky A., Sutskever I., Hinton G.E. Imagenet classification with deep convolutional neural networks // Communications of the ACM. 2017. V. 60. № 6. P. 84–90.
- Deng J., Dong W., Socher R., Li L.J., Li K., Fei-Fei L. Imagenet: A large-scale hierarchical image database // In 2009 IEEE Conference on Computer Vision and Pattern Recognition. 2009. P. 248–255.
- Bolme D.S., Beveridge J.R., Draper B.A., Lui Y.M. Visual object tracking using adaptive correlation filters // IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2010. P. 2544–2550.
- Mohri M., Rostamizadeh A., Talwalkar A. Foundations of machine learning. MIT Press, 2018. 475 p.
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