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Stomach helicobacter pylori infection pathological diagnosis support system and method

A technology of Helicobacter pylori and support system, applied in the direction of medical automation diagnosis, medical equipment, instruments, etc., to solve the effect of uneven distribution of medical resources, high accuracy and long working duration

Inactive Publication Date: 2018-12-14
万香波 +11
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Aiming at the shortcomings of manual reading in pathological histology, this invention intends to use a computer to conduct deep learning on a large number of pathological images of gastric Helicobacter pylori infection to establish an intelligent mathematical model for the pathological diagnosis of gastric Helicobacter pylori infection, and to build a model based on big data and deep learning. The artificial intelligence platform for gastric Helicobacter pylori infection-assisted pathological diagnosis based on algorithms can realize high-accuracy and high-efficiency intelligent film reading to assist clinical pathological diagnosis of gastric Helicobacter pylori infection and improve its accuracy, work efficiency and work efficiency. continuous state

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  • Stomach helicobacter pylori infection pathological diagnosis support system and method
  • Stomach helicobacter pylori infection pathological diagnosis support system and method
  • Stomach helicobacter pylori infection pathological diagnosis support system and method

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Embodiment 1

[0033] see figure 1 , an embodiment of the gastric Helicobacter pylori infection pathological diagnosis support system 1 of the present invention, which includes:

[0034] The image data obtaining unit 2 is used to obtain the normal slice image of the stomach and the pathological slice image of the confirmed gastric Helicobacter pylori infection case as the input image data;

[0035] An image data labeling unit 3, configured to label the input image data, and ensure that the label of the image is consistent with the real pathological diagnosis result of the image;

[0036] The image database construction unit 7 is used to classify and organize the labeled image data provided by the image data labeling unit, and construct a pathological image database;

[0037] A convolutional neural network construction unit 4, configured to construct a first convolutional neural network model;

[0038] The convolutional neural network model training unit 5 uses the image data of the patholo...

Embodiment 2

[0047] see figure 2 , an embodiment of the gastric Helicobacter pylori infection diagnostic support method of the present invention, which comprises the following steps:

[0048] (1) Collect image data

[0049] Using the medical biobank data of the Sixth Affiliated Hospital of Sun Yat-sen University as the data source, 14,000 pathological slice images were collected, including 7,000 gastric normal tissue slice images and 7,000 gastric Helicobacter pylori infected tissue slices, and respectively according to the training set: verification set : test set = 3:1:1 ratio of the number of random groups. As shown in Table 1 below:

[0050] Table 1 Specific data of pathological slice images.

[0051]

[0052] The collected images were digitally scanned and stored, serial numbered and archived to create a pathological image database of gastric Helicobacter pylori infection.

[0053] (2) Annotate image information

[0054] Use the existing ASAP image labeling software to perfor...

Embodiment 4

[0079] Example 4 Comparison between the method for supporting the pathological diagnosis of gastric Helicobacter pylori infection of the present invention and existing methods

[0080] At present, the clinical pathological diagnosis is performed by the pathologists who have undergone standardized training to manually read the pathological tissue slides, and combine their long-term accumulated clinical diagnosis experience to make analysis and diagnosis. Since this method of manual naked eye image reading is closely related to the pathologist's own experience, working status, subjective emotions and other factors, the accuracy rate is not high, but it takes a long time and the working duration is limited, which is prone to missed diagnosis, misdiagnosis and inconsistent diagnosis. The present invention uses a computer to perform deep learning on a large number of standardized pathological images of gastric Helicobacter pylori infection, and performs parameter adjustment and fitt...

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Abstract

The present invention discloses a stomach helicobacter pylori infection pathological diagnosis support system and method. The system comprises: an image data obtaining unit configured to obtain normalstomach section images and pathological section images of a confirmed stomach helicobacter pylori infection case as input image data; an image data marking unit configured to perform marking for theinput image data; an image database construction unit configured to perform classification and arrangement for the marked image data provided by the image data marking unit and construct a pathology image database; a convolutional neural network (CNN) construction unit configured to construct a CNN model; and a CNN model training unit configured to obtain an ideal CNN model. The stomach helicobacter pylori infection pathological diagnosis support system and method can achieve accurate, efficient and intelligent section reading to assist in the clinical pathological diagnosis work of the stomach helicobacter pylori infection so as to improve the accuracy, the working efficiency and the work persistent state.

Description

technical field [0001] The invention relates to a support system and method for pathological diagnosis of gastric Helicobacter pylori infection based on big data deep learning. Background technique [0002] Deep learning is currently the most suitable and widely used algorithm for image recognition and speech analysis in the field of artificial intelligence. Its inspiration comes from the working mechanism of the human brain. It is to automatically extract features from externally input data by establishing a convolutional neural network. , so that the machine can understand the learning data, obtain information and output. At present, artificial intelligence based on deep learning has been applied in various industries, including speech recognition, face recognition, car logo recognition, handwritten Chinese character recognition, etc. In recent years, the product research and development of artificial intelligence-assisted medical technology has also made significant prog...

Claims

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Application Information

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IPC IPC(8): G16H30/20G16H50/20G16H40/67
Inventor 万香波
Owner 万香波
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