Big data deep learning-based stomach cancer pathological diagnosis support system and method

A technology of support system and pathological diagnosis, applied in the field of gastric cancer pathological diagnosis support system, to achieve the effect of short time-consuming, accurate pathological diagnosis service, and solving uneven distribution of medical resources

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

[0006] Aiming at the shortcomings of manual reading in pathology and histology, this invention intends to use computers to conduct deep learning on a large number of gastric cancer pathological images to establish an intelligent mathematical model for pathological diagnosis of gastric cancer, and to build an artificial intelligence system for auxiliary pathological diagnosis of gastric cancer based on big data and deep learning algorithms. platform to achieve high-accuracy and high-efficiency intelligent film reading to assist clinical pathological diagnosis of gastric cancer and improve its accuracy, work efficiency and work continuity

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  • Big data deep learning-based stomach cancer pathological diagnosis support system and method
  • Big data deep learning-based stomach cancer pathological diagnosis support system and method
  • Big data deep learning-based stomach cancer pathological diagnosis support system and method

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Embodiment 1

[0034] see figure 1 , an embodiment of the gastric cancer pathological diagnosis support system 1 of the present invention, which includes:

[0035] The image data obtaining unit 2 is used to obtain images of normal gastric tissue slices and pathological slice images of confirmed cases of gastric cancer as input image data;

[0036] 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;

[0037] 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;

[0038] A convolutional neural network construction unit 4, configured to construct a first convolutional neural network model;

[0039] The convolutional neural network model training unit 5 uses the image data of the pathological image database to adjust the parameters of the...

Embodiment 2

[0048] see figure 2 , an embodiment of the gastric cancer diagnosis support method of the present invention, which comprises the following steps:

[0049] (1) Collect image data

[0050] Using the medical biobank data of the Sixth Affiliated Hospital of Sun Yat-sen University as the data source, 11,000 pathological slice images were collected, including 5,500 gastric normal tissue slice images and 5,500 gastric cancer tissue slice images, and respectively according to the training set: verification set: test set = 3:1:1 quantity ratio for random grouping. As shown in Table 1 below:

[0051] Table 1 Specific data of pathological slice images.

[0052]

[0053]

[0054] The collected images are digitally scanned and stored, serial numbered and archived to create a gastric cancer pathological image database.

[0055] (2) Annotate image information

[0056] Use the existing ASAP image labeling software to perform data labeling on the pathological slice images of the tr...

Embodiment 4

[0081] Example 4 Comparison between the gastric cancer pathological diagnosis support method of the present invention and existing methods

[0082] 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 gastric cancer pathological images, and performs parameter adjustment and fitting training on a convolutional neural network, thereby obtaini...

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Abstract

The invention discloses a big data deep learning-based stomach cancer pathological diagnosis support system and method. The system comprises an image data obtaining unit, an image data labeling unit, an image database construction unit, a convolutional neural network (CNN) construction unit and a convolutional neural network training unit, wherein the image data obtaining unit is used for obtaining stomach normal tissue slice images and pathological slice images of definite stomach cancer cases as input image data; the image data labeling unit is used for labeling the input image data; the image database construction unit is used for classifying and arranging the labeled image data provided by the image data labeling unit so as to construct a pathological image database; the convolutional neural network (CNN) construction unit is used for constructing a first convolutional neural network model; and the convolutional neural network training unit is used for obtaining an ideal convolutional neural network model. Through the stomach cancer pathological diagnosis support system and method, accurate and efficient intelligent slice reading can be realized so as to assistant the clinical stomach cancer pathological diagnosis work and improve the correctness, work efficiency and work persistent state.

Description

technical field [0001] The present invention relates to a gastric cancer 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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00G06T7/00
CPCG06T7/0012G06T2207/20081G06T2207/20084G06T2207/30092G06T2207/30096
Inventor 万香波
Owner 万香波
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