Gastric early cancer histological image classification system based on deep neural network

A deep neural network and classification system technology, applied in the field of early gastric cancer histological image classification system, can solve the problems of neural network overfitting, imbalance, inaccurate results, etc., achieve broad application prospects, improve prediction accuracy, The effect of improving overall performance

Inactive Publication Date: 2019-11-19
BEIJING CHAOYANG HOSPITAL CAPITAL MEDICAL UNIV
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Problems solved by technology

In order to adapt to the input of the neural network, there are two processing methods: image scaling and cropping. The former loses the local details of the image, and the latter lacks the global feature information of the original image, which can easily lead to over-fitting of the neural network.
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  • Gastric early cancer histological image classification system based on deep neural network
  • Gastric early cancer histological image classification system based on deep neural network
  • Gastric early cancer histological image classification system based on deep neural network

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[0025] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0026]The present invention proposes a two-branch feature fusion CNN architecture based on difficult sample mining for pathological image classification of early gastric cancer. In clinical diagnosis, the accurate diagnosis of pathological images of early gastric cancer requires not only focusing on microscopic features such as whether the nu...

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Abstract

The invention relates to a gastric early cancer histological image classification system based on a deep neural network, and the system comprises a WSI pathological section obtaining module which is used for obtaining a gastric early cancer pathological section image; an image preprocessing module which is used for preprocessing the early gastric cancer pathological section image; a data set division module which is used for dividing the data set after image preprocessing into a training set and a verification set, a training set which is used for model training, and the verification set is used for verifying a model effect; a feature extraction module which is used for extracting picture features of the training set or the verification set based on a GoogleNet double-branch network structure and outputting feature vectors of corresponding pictures; and a classifier training module which is used for training based on the neural network model to obtain a gastric early cancer classification model.

Description

technical field [0001] The invention relates to a histological image classification system for early gastric cancer based on a deep neural network, and relates to the technical field of image classification. Background technique [0002] Early gastric cancer refers to gastric cancer whose lesions only invade the mucosa or submucosa. According to statistics, there are nearly 1 million cases of gastric adenocarcinoma every year worldwide, ranking third among the causes of cancer death in the world, and it is also the main cause of infection complicated by cancer death. Among them, early gastric cancer accounts for 15% to 57% of gastric cancer cases, so early diagnosis and effective treatment of early gastric cancer are of great significance to reduce the mortality of gastric cancer. Histocytological characteristics are currently recognized and the most reliable diagnostic criteria for tumors. Usually, the process of pathological diagnosis of early gastric cancer is to first ...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/24
Inventor 金木兰王莹段佳佳彭婷祝闯刘军罗毅豪
Owner BEIJING CHAOYANG HOSPITAL CAPITAL MEDICAL UNIV
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