AUTOMATED METHOD FOR OPTIMUM SCALE SEARCH WHEN USING TRAINED MODELS FOR HISTOLOGICAL IMAGE ANALYSIS

M. A. PENKIN; Пенкин М. А.; A. V. KHVOSTIKOV; Хвостиков А. В.; A. S. KRYLOV; Крылов А. С.

doi:10.31857/S0132347423030032

AUTOMATED METHOD FOR OPTIMUM SCALE SEARCH WHEN USING TRAINED MODELS FOR HISTOLOGICAL IMAGE ANALYSIS

Autores: PENKIN M.¹, KHVOSTIKOV A.¹, KRYLOV A.¹
Afiliações:
1. Laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and Cybernetics, Moscow State University
Edição: Nº 3 (2023)
Páginas: 49-55
Seção: COMPUTER GRAPHICS AND VISUALIZATION
URL: https://journals.rcsi.science/0132-3474/article/view/137626
DOI: https://doi.org/10.31857/S0132347423030032
EDN: https://elibrary.ru/DEIZNX
ID: 137626

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Resumo

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.

Bibliografia

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