title: ÐÐµÑÐ¾Ð´ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½Ð¾Ð¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð³Ð¸ÑÑÐ¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¿ÑÐ¾ÑÑÑÐ°Ð½ÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð¿Ð¾Ð»Ð¾Ð¶ÐµÐ½Ð¸Ñ Ð¾ÑÐ´ÐµÐ»ÑÐ½ÑÑ ÑÐ»ÐµÐ¼ÐµÐ½ÑÐ¾Ð²
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: A method of intelligent classification of histological images using spatial position of individual elements
Creators: name: ÐÑÐ¾Ð·Ð´Ð¾Ð², Ð¡ÐµÑÐ³ÐµÐ¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2021
Subjects: ÑÐ°Ð·Ð¼ÐµÑÐºÐ° Ð³Ð¸ÑÑÐ¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ
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Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
markup of histological image
convolutional neural network
convolutional autoencoder
k means method
classification
semisupervised learning
machine learning

description: Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÐµÑÑÑ Ð¼ÐµÑÐ¾Ð´ ÑÐ°ÑÑÐ¸ÑÐ½Ð¾Ð¹ ÑÐ°Ð·-Ð¼ÐµÑÐºÐ¸ Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð³Ð¸ÑÑÐ¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹. ÐÑ-Ð½Ð¾Ð²Ð½Ð°Ñ Ð¸Ð´ÐµÑ Ð¼ÐµÑÐ¾Ð´Ð° Ð·Ð°ÐºÐ»ÑÑÐ°ÐµÑÑÑ Ð² Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ð¸ Ð´Ð¾Ð¿Ð¾Ð»Ð½Ð¸-ÑÐµÐ»ÑÐ½Ð¾Ð¹ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸, ÐºÐ¾ÑÐ¾ÑÐ°Ñ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÑÐµÑÑÑ Ð¿ÑÐ¾ÑÑÑÐ°Ð½ÑÑÐ²ÐµÐ½-Ð½ÑÐ¼ ÑÐ°ÑÐ¿Ð¾Ð»Ð¾Ð¶ÐµÐ½Ð¸ÐµÐ¼ Ð¾ÑÐ´ÐµÐ»ÑÐ½ÑÑ ÐºÐ»ÐµÑÐ¾Ðº Ð³Ð¸ÑÑÐ¾Ð»Ð¾Ð³Ð¸Ð¸. ÐÐ»Ð°ÑÑÐ¸-ÑÐ¸ÐºÐ°ÑÐ¾Ñ Ð¾Ð±ÑÑÐ°ÐµÑÑÑ Ð½Ð° ÑÐ°Ð·Ð¼ÐµÑÐµÐ½Ð½ÑÑ Ð³Ð¸ÑÑÐ¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ñ Ð¸Ð·Ð¾Ð±-ÑÐ°Ð¶ÐµÐ½Ð¸ÑÑ Ð¿ÑÐµÐ´ÑÑÐ°ÑÐµÐ»ÑÐ½Ð¾Ð¹ Ð¶ÐµÐ»ÐµÐ·Ñ. ÐÐµÑÐ¾Ð´ ÑÐ°Ð±Ð¾ÑÐ°ÐµÑ ÑÐ»ÐµÐ´ÑÑ-ÑÐ¸Ð¼ Ð¾Ð±ÑÐ°Ð·Ð¾Ð¼: Ð³Ð¸ÑÑÐ¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¾Ðµ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ðµ Ð´ÐµÐ»Ð¸ÑÑÑ Ð½Ð° ÑÐ»Ðµ-Ð¼ÐµÐ½ÑÑ Ð·Ð°Ð´Ð°Ð½Ð½Ð¾Ð³Ð¾ ÑÐ°Ð·Ð¼ÐµÑÐ°, Ð¿Ð¾ÑÐ»Ðµ ÑÐµÐ³Ð¾ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ ÐºÐ¾Ð´Ð¸ÑÑÑ-ÑÐµÐ¹ ÑÐ¾ÑÑÐ°Ð²Ð»ÑÑÑÐµÐ¹ Ð°Ð²ÑÐ¾ÐºÐ¾Ð´Ð¸ÑÐ¾Ð²ÑÐ¸ÐºÐ° ÐºÐ°Ð¶Ð´Ð¾Ð¼Ñ ÑÐ»ÐµÐ¼ÐµÐ½ÑÑ ÑÐ¾-Ð¿Ð¾ÑÑÐ°Ð²Ð»ÑÐµÑÑÑ ÐµÐ³Ð¾ ÑÐ¶Ð°ÑÐ¾Ðµ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð¸Ðµ. ÐÐ¾Ð»ÑÑÐµÐ½Ð½ÑÐµ ÑÐ¶Ð°ÑÑÐµ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ ÑÐ»ÐµÐ¼ÐµÐ½ÑÐ¾Ð² Ð³Ð¸ÑÑÐ¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ Ð¿ÑÐ¾ÑÐ¾Ð´ÑÑ ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ñ, Ð² ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ ÐºÐ¾ÑÐ¾ÑÐ¾Ð¹ ÐºÐ°Ð¶Ð´Ð¾Ð¼Ñ ÑÐ»ÐµÐ¼ÐµÐ½ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ Ð¿ÑÐ¸ÑÐ²Ð°Ð¸Ð²Ð°ÐµÑÑÑ Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÑ ÑÐ¾Ð³Ð¾, ÑÑÐ¾ ÑÐ»ÐµÐ¼ÐµÐ½Ñ, ÑÐ¾Ð¿Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð½ÑÐ¹ ÐºÐ»Ð°ÑÑÐµÑÑ, Ð¿ÑÐ¸Ð½Ð°Ð´Ð»ÐµÐ¶Ð¸Ñ Ð³Ð¸ÑÑÐ¾Ð»Ð¾Ð³Ð¸-ÑÐµÑÐºÐ¾Ð¼Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ Ð¿Ð¾ÑÐ°Ð¶ÐµÐ½Ð½Ð¾Ð¹ ÑÐ°ÐºÐ¾Ð¼ ÑÐºÐ°Ð½Ð¸. ÐÐ¾ Ð·Ð°Ð´Ð°Ð½Ð½Ð¾Ð¹ Ð³ÑÐ°Ð½Ð¸ÑÐµ Ð¿ÑÐ¸Ð½ÑÑÐ¸Ñ ÑÐµÑÐµÐ½Ð¸Ð¹ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÑÐµÑÑÑ ÐºÐ»Ð°ÑÑ ÑÐ»ÐµÐ¼ÐµÐ½ÑÐ°. Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ñ Ð´Ð²Ð° ÑÐ¿Ð¾ÑÐ¾Ð±Ð° ÑÑÑÑÐ° Ð¿ÑÐ¾ÑÑÑÐ°Ð½-ÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð¿Ð¾Ð»Ð¾Ð¶ÐµÐ½Ð¸Ñ ÑÐ»ÐµÐ¼ÐµÐ½ÑÐ¾Ð²: ÑÑÐ¾ÑÐ½ÐµÐ½Ð¸Ðµ Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÐ¸ ÑÐ»ÐµÐ¼ÐµÐ½ÑÐ° Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ ÑÑÐµÐ´Ð½ÐµÐ³Ð¾ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ñ Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÐµÐ¹ ÐµÐ³Ð¾ ÑÐ¾ÑÐµÐ´ÐµÐ¹ Ð¸ ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ñ ÑÐ¶Ð°ÑÑÑ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð¸Ð¹ ÑÐ»ÐµÐ¼ÐµÐ½ÑÐ° Ð¸ ÐµÐ³Ð¾ ÑÐ¾ÑÐµÐ´ÐµÐ¹ ÑÐ¾Ð²Ð¼ÐµÑÑÐ½Ð¾, Ð½Ðµ Ð¿Ð¾ Ð¾ÑÐ´ÐµÐ»ÑÐ½Ð¾ÑÑÐ¸. Ð­ÑÐ¸ Ð¿Ð¾Ð´ÑÐ¾Ð´Ñ Ð½Ðµ Ð¿ÑÐ¸Ð²ÐµÐ»Ð¸ Ðº ÑÐ»ÑÑÑÐµÐ½Ð¸Ñ ÐºÐ°ÑÐµÑÑÐ²Ð° ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸. ÐÐ°Ð¸Ð»ÑÑÑÐ°Ñ Ð´Ð¾ÑÑÐ¸Ð³Ð½ÑÑÐ°Ñ ÑÐ¾ÑÐ½Ð¾ÑÑÑ ÑÐ¾ÑÑÐ°Ð²Ð»ÑÐµÑ 0,825.
description: In this paper, a method of partial annotation and classifica-tion of histological images is considered. The main idea of the method is to use additional information, which is determined by the spatial arrangement of individual cells in histology. The clas-sifier is trained on the annotated histological images of the pros-tate gland. The method works as follows: the histological image is divided into elements of a given size, after which, using the encoding component of the autoencoder, an embedding is as-signed to each element. The resulting embeddings of the ele-ments of the histological image undergo clustering, as a result of which each element of the image is assigned the probability that the element associated with the cluster belongs to the histological image of the cancerous tissue. The class of the element is deter-mined based on a given decision-making threshold. In this paper, two methods of taking into account the spatial position of ele-ments are considered: refining the probability of an element us-ing the average value of the probabilities of its neighbors and clustering the embeddings of an element and its neighbors to-gether, not separately. These approaches did not lead to an im-provement in the quality of the classification. The best achieved accuracy is 0.825.