title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½ÑÐºÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð³Ð»ÑÐ±Ð¾ÐºÐ¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Development of models and algorithms for segmenting medical images using deep neural networks
Creators: name: ÐÐ°ÑÐ°Ð», ÐÐ°ÐºÑÐ¸Ð¼
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: 2020
Subjects: Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ ÑÐ¾ ÑÐ»Ð°Ð±ÑÐ¼ ÑÑÐ¸ÑÐµÐ»ÐµÐ¼
Ð°Ð²ÑÐ¾ÐºÐ¾Ð´Ð¸ÑÐ¾Ð²ÑÐ¸Ðº
ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ñ
Ð³Ð¸ÑÑÐ¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ðµ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ
ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ñ
Weakly supervised learning
autoencoder
clustering
histological images
segmentation

description: Ð¢ÐµÐ¼Ð° Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: Â«Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½ÑÐºÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð³Ð»ÑÐ±Ð¾ÐºÐ¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹Â». ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ð¹ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐ¸ Ð´Ð»Ñ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð³Ð¸ÑÑÐ¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹, Ð² ÑÐ°ÑÑÐ½Ð¾ÑÑÐ¸ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð¼ÐµÑÐ¾Ð´Ð°, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½Ð¾Ð³Ð¾ Ð½Ð° Ð¾Ð±ÑÑÐµÐ½Ð¸Ð¸ ÑÐ¾ ÑÐ»Ð°Ð±ÑÐ¼ ÑÑÐ¸ÑÐµÐ»ÐµÐ¼. ÐÐ°Ð´Ð°ÑÐ¸, ÐºÐ¾ÑÐ¾ÑÑÐµ ÑÐµÑÐ°Ð»Ð¸ÑÑ Ð² ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ: 1. Ð°Ð½Ð°Ð»Ð¸Ð· ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¸ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð¾Ð² ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ð¹ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐ¸ Ð´Ð»Ñ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½ÑÐºÐ¸Ñ Ð³Ð¸ÑÑÐ¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹; 2. ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼ÐµÑÐ¾Ð´Ð°, ÐºÐ¾ÑÐ¾ÑÑÐ¹ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¸ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ¸ÑÐ¾Ð²Ð°ÑÑ Ð³Ð¸ÑÑÐ¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ðµ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð´Ð°Ð½Ð½ÑÑ ÑÐ¾ ÑÐ»Ð°Ð±ÑÐ¼Ð¸ Ð¼ÐµÑÐºÐ°Ð¼Ð¸; 3. ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¹, ÐºÐ¾ÑÐ¾ÑÑÐµ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÑÑ ÑÐ²ÐµÐ»Ð¸ÑÐ¸ÑÑ ÑÐ¾ÑÐ½Ð¾ÑÑÑ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð² ÑÑÐ»Ð¾Ð²Ð¸ÑÑ Ð¼Ð°Ð»Ð¾Ð³Ð¾ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð° Ð´Ð°Ð½Ð½ÑÑ Ð´Ð»Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ; 4. ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ Ð²Ð¸Ð·ÑÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² Ð´Ð»Ñ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¾Ð±ÑÑÑÐ½Ð¸ÑÐµÐ»ÑÐ½Ð¾Ð³Ð¾ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÐ°; 5. Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð². Ð Ð°Ð±Ð¾ÑÐ° Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð° Ð½Ð° Ð±Ð°Ð·Ðµ Ð¾ÑÐºÑÑÑÐ¾Ð³Ð¾ Ð½Ð°Ð±Ð¾ÑÐ° Ð´Ð°Ð½Ð½ÑÑ Ð³Ð¸ÑÑÐ¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð¿ÑÐµÐ´ÑÑÐ°ÑÐµÐ»ÑÐ½Ð¾Ð¹ Ð¶ÐµÐ»ÐµÐ·Ñ TCIA. ÐÐ° ÑÐ·ÑÐºÐµ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Python 3.7 Ð±ÑÐ» ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½ Ð¼ÐµÑÐ¾Ð´ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð³Ð¸ÑÑÐ¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ñ Ð¾Ð±ÑÐ°Ð·ÑÐ¾Ð² Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð°Ð²ÑÐ¾ÐºÐ¾Ð´Ð¸ÑÐ¾Ð²ÑÐ¸ÐºÐ° Ð¸ ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ð¸, Ð° ÑÐ°ÐºÐ¶Ðµ ÐµÐ³Ð¾ Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸, ÐºÐ¾ÑÐ¾ÑÑÐµ ÑÐ»ÑÑÑÐ°ÑÑ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ Ð² ÑÑÐ»Ð¾Ð²Ð¸ÑÑ Ð¼Ð°Ð»Ð¾Ð³Ð¾ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð° Ð´Ð°Ð½Ð½ÑÑ. Ð Ð¸ÑÐ¾Ð³Ðµ Ð±ÑÐ»Ð¾ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð¾ ÐÐ, ÐºÐ¾ÑÐ¾ÑÐ¾Ðµ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ Ð²Ð¸Ð·ÑÐ°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°ÑÑ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ ÑÐ°Ð±Ð¾ÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð². ÐÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¾ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¸ ÐµÐ³Ð¾ Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¹. ÐÐ° Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð¸Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ð¹ Ð¼Ð¾Ð¶Ð½Ð¾ ÑÐ´ÐµÐ»Ð°ÑÑ Ð²ÑÐ²Ð¾Ð´, ÑÑÐ¾ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½ÑÐµ Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÑÑ Ð½Ð° 2.6% (20.5%) ÑÐ¾ÑÐ½ÐµÐµ Ð²ÑÐ¿Ð¾Ð»Ð½ÑÑÑ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ñ Ð³Ð¸ÑÑÐ¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ñ Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½ÑÐºÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹.
description: The subject of the graduate qualification work is: Â«Development of models and algorithms for segmentation of medical images using deep neural networksÂ». The given work is devoted to the study of methods and algorithms of computer diagnostics for the analysis of histological images, in particular, the devel-opment of a method based on weakly supervised learning. The research set the following goals: 1. analysis of existing methods and approaches of computer diagnostics for segmentation of medical histological images; 2. development of a method that allows automatic segmentation of histological images based on data with weak labels; 3. development of modifications that increase segmentation accuracy in a small amount of training data; 4. development of results visualization software for implementing ex-plainable knowledge; 5. research on the perfomance of implemented methods. The work was performed on the basis of an open dataset of prostate histological images TCIA. In the Python 3.7 programming language, the method of auto-matic segmentation of histological samples using autoencoder and clustering was implemented, as well as its modifications, which improve the efficiency in condi-tions of a small amount of data. As a result, Software was implemented that allows to visualize the results of algorithms. A study of the accuracy of segmentation methods and its modifications was performed. Based on research, it can be concluded that the developed modifications allow for 2.6% (20.5%) more accurate segmentation of histological medical images.