Automated Method for Optimum Scale Search when Using Trained Models for Histological Image Analysis

被引：0

作者：

Penkin, M. A. ^{[1
]}

Khvostikov, A. V. ^{[1
]}

Krylov, A. S. ^{[1
]}

机构：

[1] Moscow MV Lomonosov State Univ, Fac Computat Math & Cybernet, Lab Math Methods Image Proc, Moscow, Russia

来源：

PROGRAMMING AND COMPUTER SOFTWARE | 2023年 / 49卷 / 03期

基金：

俄罗斯科学基金会;

关键词：

Compendex;

D O I：

10.1134/S0361768823030039

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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 pre-trained on PATH-DT-MSU data, which implements patch classification of whole-slide histological images.

引用

页码：172 / 177

页数：6