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Gastrointestinal stromal tumor pathological diagnosis support system and method based on big-data deep learning

A technology of gastrointestinal stromal tumor and support system, which is applied in the field of pathological diagnosis support system for gastrointestinal stromal tumor, and achieves the effects of solving uneven distribution of medical resources, high accuracy and long working time.

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 establish an intelligent mathematical model for the pathological diagnosis of gastrointestinal stromal tumors through deep learning of a large number of pathological images of gastrointestinal stromal tumors by computer, and build a model based on big data and deep learning. Algorithm-assisted pathological diagnosis artificial intelligence platform for gastrointestinal stromal tumors, so as to realize high-accuracy and high-efficiency intelligent film reading, to assist clinical pathological diagnosis of gastrointestinal stromal tumors, improve its accuracy, work efficiency and work continuous state

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  • Gastrointestinal stromal tumor pathological diagnosis support system and method based on big-data deep learning
  • Gastrointestinal stromal tumor pathological diagnosis support system and method based on big-data deep learning
  • Gastrointestinal stromal tumor pathological diagnosis support system and method based on big-data deep learning

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Embodiment 1

[0033] see figure 1 , an embodiment of the gastrointestinal stromal tumor pathological diagnosis support system 1 of the present invention, which includes:

[0034] The image data obtaining unit 2 is used to obtain images of normal gastrointestinal tissue slices and pathological slice images of confirmed cases of gastrointestinal stromal tumors 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 imag...

Embodiment 2

[0047] see figure 2 , an embodiment of the gastrointestinal stromal tumor diagnosis support method of the present invention, which includes 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, 12,000 pathological slice images were collected, including 6,000 normal tissue slice images and 6,000 gastrointestinal stromal tumor tissue slices, and respectively according to the training set: verification set: The test set=3:1:1 ratio for random grouping. As shown in Table 1 below:

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

[0051]

[0052]

[0053] (2) Annotate image information

[0054] Use the existing ASAP image labeling software to perform data labeling on the pathological slice images of the training set, verification set and test set collected in step (1). In order to ensure the accuracy of information labeling, it is necessary t...

Embodiment 4

[0079] Example 4 Comparison between the method for supporting pathological diagnosis of gastrointestinal stromal tumors 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 gastrointestinal stromal tumor pathological images, and performs parameter adjustment and fitting training on the ...

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Abstract

The invention discloses a gastrointestinal stromal tumor pathological diagnosis support system and method based on big-data deep learning, and the system comprises an image data obtaining unit which is used for obtaining a normal stomach intestine tissue section image and a pathological section image of a diagnosed gastrointestinal stromal tumor case as the input image data; an image data marking unit which is used for marking the inputted image data; an image database construction unit which is used for carrying out the classification and arrangement of the marked image data provided by the image data marking unit, and constructing a pathological image database; a CNN (convolution neural network) construction unit which is used for constructing a first CNN model; and a CNN model training unit which is used for obtaining an ideal CNN model. According to the invention, the system and method can achieve the precise and high-efficiency intelligent reading of a film, so as to assist the pathological diagnosis of the gastrointestinal stromal tumor clinically, thereby improving the accuracy of pathological diagnosis, work efficiency and working persistent state.

Description

technical field [0001] The invention relates to a support system and method for pathological diagnosis of gastrointestinal stromal tumors 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. ...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06K9/62G06F17/30
CPCG06F16/51G06F16/5866G06T7/0012G06T2207/20004G06T2207/20021G06T2207/20084G06T2207/20081G06T2207/30096G06T2207/30092G06F18/24G06F18/214
Inventor 万香波
Owner 万香波
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