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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://rjeid.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
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