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Benign gastritis pathological diagnosis support system and method based on big data deep learning

A technology for supporting systems and pathological diagnosis, applied in neural learning methods, electrical digital data processing, special data processing applications, etc., to achieve the effect of short time-consuming, high accuracy, and accurate pathological diagnosis services

Inactive Publication Date: 2017-11-21
万香波 +11
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Aiming at the shortcomings of manual reading in pathological histology, the present invention intends to carry out deep learning on a large number of benign gastritis pathological images by computer, to establish an intelligent mathematical model for benign gastritis pathological diagnosis, and to build an auxiliary pathological diagnosis system for benign gastritis based on big data and deep learning algorithms. Diagnostic artificial intelligence platform, so as to achieve high accuracy and high efficiency of intelligent film reading, to assist the clinical pathological diagnosis of benign gastritis, improve its accuracy, work efficiency and work continuity

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Embodiment 1

[0033] see figure 1 , a kind of embodiment of benign gastritis pathological diagnosis support system 1 of the present invention, it comprises:

[0034] The image data obtaining unit 2 is used to obtain images of normal gastric mucosal tissue slices and pathological slice images of confirmed cases of benign gastritis as 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 pathological image database to adjust the para...

Embodiment 2

[0047] see figure 2 , a kind of embodiment of benign gastritis diagnosis support method of the present invention, it comprises the steps:

[0048] (1) Collect image data

[0049] Using the data from the Pathology Department of the Sixth Hospital Affiliated to Sun Yat-sen University and the human tissue resource bank as the data source, 10,000 pathological slice images were collected, including 5,000 normal tissue slice images and 5,000 benign gastritis 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 benign gastritis pathological image database.

[0053] (2) Annotate image information

[0054] Use the existing ASAP image labeling software to perform data labeling on the pathological...

Embodiment 4

[0079] Embodiment 4 The comparison between the benign gastritis pathological diagnosis support method of the present invention and the existing method

[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 benign gastritis pathological images, and performs parameter adjustment and fitting training on the convolutional neural network, ...

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Abstract

The invention discloses a benign gastritis pathological diagnosis support system and method based on big data deep learning. The system comprises an image data obtaining unit, an image data marking unit, an image database building unit, a CNN (Convolutional Neural Network) building unit and a CNN model training unit, wherein the image data obtaining unit is used for obtaining a normal gastric mucosa tissue slice image and a pathological section image of a previously diagnosed benign gastritis case, and the images are used as input image data; the image data marking unit is used for marking the input image data; the image database building unit is used for classifying and sorting the marked image data provided by the image data marking unit, and building a pathological image database; the CNN building unit is used for building a first CNN model; and the CNN model training unit is used for obtaining the ideal CNN model. Through the benign gastritis pathological diagnosis support system and method, the precise and efficient intelligent film reading can be realized, so that the clinic benign gastritis pathological diagnosis work can be assisted; and the accuracy, the work efficiency and the work continuous state are improved.

Description

technical field [0001] The invention relates to a benign gastritis pathological diagnosis support system and method 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 progress. For example, the artif...

Claims

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Application Information

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IPC IPC(8): G06F19/00G06N3/08
CPCG06N3/08
Inventor 万香波
Owner 万香波
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