Programming and Computer SoftwareProgramming and Computer SoftwareПрограммирование0132-34743034-5847The Russian Academy of Sciences67569610.31857/S0132347424030075QAGUUMDATA ANALYSISАНАЛИЗ ДАННЫХResearch ArticleNeural Network Method for Detecting Blur in Histological ImagesНейросетевой метод детектирования размытия на гистологических изображенияхNazarenkoG. S.НазаренкоГ. С.
Russian Federation
s02190303@gse.cs.msu.ruKrylovA. S.КрыловА. С.
Russian Federation
kryl@cs.msu.ruLomonosov Moscow State UniversityМосковский государственный университет имени М.В. Ломоносова281120243677428022025Copyright ©; 2024, Russian Academy of SciencesCopyright ©; 2024, Российская академия наук2024Russian Academy of SciencesРоссийская академия наукhttps://journals.eco-vector.com/0132-3474/article/view/675696

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].

В работе рассматривается задача обнаружения размытых областей на полнослайдовых гистологических изображениях высокого разрешения. Предлагаемый метод основан на использовании нейронного оператора Фурье, обучаемого на результатах двух одновременно использованых подходов: обнаружения размытия с помощью многомасштабного анализа коэффициентов дискретного косинусного преобразования и оценки степени резкости границ объектов на изображении. Эффективность алгоритма подтверждена на изображениях из наборов данных PATH-DT-MSU [1] и FocusPath [2].

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