Neural Network Method for Detecting Blur in Histological Images

In this paper we consider the problem of detecting blurred regions in high-resolution full-slide histologic images. The proposed method is based on the use of a Fourier neural operator trained on the results of two simultaneously used approaches: blur detection using multiscale analysis of the discrete cosine transform coefficients and estimation of the degree of sharpness of objects edges in the image. The efficiency of the algorithm is confirmed on images from the datasets PATH-DT-MSU [1] and FocusPath [2].

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Программирование
Published
2024-11-28
DOI
https://doi.org/10.31857/s0132347424030075
Primary Topic
AI in cancer detection
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article
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article

Neural Network Method for Detecting Blur in Histological Images

A. S. Krylov, G.S. Nazarenko
Программирование
AI in cancer detection
article

Neural Network Method for Detecting Blur in Histological Images

A. S. Krylov, G.S. Nazarenko
article en

Abstract

In this paper we consider the problem of detecting blurred regions in high-resolution full-slide histologic images. The proposed method is based on the use of a Fourier neural operator trained on the results of two simultaneously used approaches: blur detection using multiscale analysis of the discrete cosine transform coefficients and estimation of the degree of sharpness of objects edges in the image. The efficiency of the algorithm is confirmed on images from the datasets PATH-DT-MSU [1] and FocusPath [2].

Программирование(3)
Lomonosov Moscow State University (RU), Moscow State University (TJ)
Openalex Percentile: Top 80%
AI in cancer detection
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