Multi-Scale Neural Network for Classification of Histological Image Fragments
https://doi.org/10.35596/1729-7648-2025-23-6-103-109
Abstract
This paper presents the architecture of a multi-scale, multi-input neural network for simultaneously processing groups of histological image fragments acquired at different magnifications. The proposed model integrates features at the hidden layer level, which allows for efficient fusion of tissue structure information at different detail scales. Experimental results have shown that applying three magnification levels on one side of a fragment (98.58; 197.16, and 394.32 μm) provides an optimal balance between the information content of the input data and the stability of the model. Using this architecture, the average F1-score increased by 5 % compared to a single-scale approach, reaching a value of 0.8962 ± 0.0508, and in some runs – 0.9697. The observed standard deviation is due to the natural variability of medical data during the formation of training samples, rather than to model instability. The obtained results confirm the promise of multiscale analysis for digital pathology tasks and demonstrate the potential of the proposed solution as a means of automated detection of suspicious areas in fullslide images.
About the Author
A. A. Kosareva
Belarusian State University of Informatics and Radioelectronics; The United Institute of Informatics Problems of the National Academy of Sciences of Belarus
Belarus
Belarus
Kosareva Aleksandra Andreevna, Assistant at the Electronic Engineering and Technology Department; Junior Researcher
220013, Minsk, P. Brovki St., 6
Tel.: +375 17 293-88-60
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Review
For citations:
Kosareva A.A. Multi-Scale Neural Network for Classification of Histological Image Fragments. Doklady BGUIR. 2025;23(6):103-109. (In Russ.) https://doi.org/10.35596/1729-7648-2025-23-6-103-109
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