CN107368670A - Stomach cancer pathology diagnostic support system and method based on big data deep learning - Google Patents
Stomach cancer pathology diagnostic support system and method based on big data deep learning Download PDFInfo
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Abstract
The invention discloses a kind of stomach cancer pathology diagnostic support system and method based on big data deep learning, the system includes：View data obtaining unit, for obtaining the pathological section image of stomach normal tissue sections image and the gastric cancer cases made a definite diagnosis as input image data；View data marks unit, for being labeled to input image data；Image data base construction unit, for the classification of the view data of mark, the arrangement provided view data mark unit, build pathological image database；Convolutional neural networks (CNN) structural unit, for constructing the first convolution neural network model；And convolutional neural networks model training unit, obtain preferable convolutional neural networks model.Accurate and efficient intelligent read tablet can be realized by the stomach cancer pathology diagnostic support system and method for the present invention, worked with the pathological diagnosis of stomach cancer on adjuvant clinical, improve its accuracy rate, operating efficiency and operation duration state.
Description
Technical field
The present invention relates to a kind of stomach cancer pathology diagnostic support system and method based on big data deep learning.
Background technology
Deep learning is that current artificial intelligence field is used for most agreeing with, being most widely used for image recognition and speech analysis
Algorithm, its inspiration come from the working mechanism of human brain, are that the data of outside input are entered by establishing convolutional neural networks
Row automation feature extraction, so as to make machine rational learning data, obtain information and export.At present, based on deep learning
Artificial intelligence be applied to industry-by-industry field, including speech recognition, recognition of face, vehicle-logo recognition, handwritten Kanji recognition etc..
The research and development of products of artificial intelligence medical assistance technology also makes substantial progress in recent years, is such as ground by Google's brain and Verily companies
The artificial intelligence product for breast cancer pathological diagnosis of hair can reach 89% tumor-localizing accuracy rate；Zhejiang University attached
One hospital realizes in quick analysis thyroid gland B ultrasound the position of knuckle areas and good pernicious using artificial intelligence.
During medical diagnosis, pathological diagnosis is the goldstandard of medical diagnosis on disease.The pathological tissue of the overwhelming majority is cut at present
Piece is analyzed and judged with reference to the clinical diagnosis experience of itself long term accumulation by manual manufacture, and by pathologist.Under gastroscope
It is the goldstandard for diagnosing disease of stomach that tissue biopsy, which carries out histopathologic examination,.
Stomach cancer occupies one of tumor in digestive tract incidence of disease highest cancer kind in the world, in the incidence of disease in China and dead
Rate is died then to rank first, and 5 years survival rates of early carcinoma of stomach are up to more than 90%, hence it is evident that better than advanced gastric carcinoma, therefore improve early stage
The recall rate of stomach cancer is particularly important for improving patients with gastric cancer prognosis.The diagnosis clinically to stomach cancer depends on electronics at present
Scope and Histopathology detection, wherein, Histopathology detection is cut off simultaneously by protractor biopsy under scope or in operation
Pathological section sample is prepared into, directly observation cancer cell is placed under light microscope, is diagnosis of gastric cancer " goldstandard ".Pathologic group
Knit learn check the parting that can fully understand early carcinoma of stomach, by stages, lymphatic metastasis situation etc., carried for postoperative adjuvant therapy and prognosis
For foundation.It is to realize early gastric caacer diagnosis, early treatment, improve prognosis to improve the scope of early carcinoma of stomach and pathological diagnosis accuracy rate
Key.
Therefore, it is mainly the shortcomings that histopathological methods diagnosis of gastric cancer：The result of pathology slide, which judges, to be cured by pathology
Raw to visually observe gained, the subjective factor such as this artificial diagosis method and pathologist experience, working condition is closely related,
Easily produce error.The histopathology classification of stomach cancer is various, and Pathologic specimen chip form is complicated, and early carcinoma of stomach is (on high-level
Intracutaneous knurl change/severe atypical hyperplasia) it is easier to fail to pinpoint a disease in diagnosis for the pathologist lacked experience, mistaken diagnosis.Meanwhile pathologist will
It is responsible for checking all visible biological tissues in section, and each patient can have many sections, when carrying out 40 times of amplifications
Each section has more than 100 hundred million pixel, therefore artificial diagosis workload is very big, is easily read by diagosis person's subjective emotion and fatigue
The influence of the factors such as piece.Moreover, different virologists may provide significantly different diagnosis to same patient.Therefore,
The Tissue pathological diagnosis method that this height relies on human factor has subjective differences, plus its working strength is big, the time
Cost is high and the shortcomings of diagnosis inconsistency, can largely influence the early stage diagnosis and treatment of stomach cancer so as to influence patient's prognosis.Separately
Outside, cultivating qualified professional pathologist needs to carry out long-term professional training and practice process, cultivation cycle length, and easily by working as
The influence of the social factors such as preceding social economy, culture, it is meant that China or even whole world pathologist quantity " supply falls short of demand ", duty
The big severe situation urgent need to resolve of industry breach.
The content of the invention
The shortcomings that diagosis artificial for Histopathology, the present invention intend carrying out a large amount of stomach cancer pathological images by computer
Deep learning, to establish intelligentized stomach cancer pathological diagnosis mathematical modeling, build the stomach based on big data and deep learning algorithm
Cancer aids in pathological diagnosis artificial intelligence platform, so as to realize high-accuracy and efficient intelligent read tablet, with stomach on adjuvant clinical
The pathological diagnosis work of cancer, improves its accuracy rate, operating efficiency and operation duration state.
Based on this, it is an object of the invention to overcome in place of above-mentioned the deficiencies in the prior art and clinic can be improved by providing one kind
Efficiency, the stomach cancer pathology diagnostic support system of reduction medical treatment cost during diagnosis of gastric cancer.
To achieve the above object, the technical scheme taken of the present invention is：A kind of gastric cancer based on big data deep learning
Diagnostic support system is managed, the support system includes：View data obtaining unit, for obtaining stomach normal gastric mucosa sectioning image
The pathological section image for the gastric cancer cases made a definite diagnosis is as input image data；View data marks unit, for institute
State input image data to be labeled, and ensure that the label of image is consistent with the true pathological diagnosis result of image；Image
Database sharing unit, for the classification of the view data of mark, the arrangement provided described image data mark unit, structure disease
Manage image data base；Convolutional neural networks structural unit, for constructing the first convolution neural network model；And convolutional Neural net
Network model training unit, using the view data of the pathological image database to the ginseng of the first convolution neural network model
Number is adjusted, and training the first convolution neural network model, obtains and can be used for detection patient's pathology view data
Second convolution neural network model.
Thus, doctor can be with reference to the holding equipment for the classification results that provide of patient's pathological image of input and corresponding
Probability, and doctor professional standing and experience be rapidly diagnosed to be the patient whether suffer from stomach cancer, significantly improve clinic
The efficiency of diagnosis, so as to reduce medical treatment cost；Wherein, in order to ensure that the view data that is collected into is accurate, figure can be utilized
As annotation tool ASAP, every pathological section image is labeled, to ensure that the label of image is consistent with actual value；In order to add
The speed of fast training network model, it can be trained using the GPU calculated with high-speed parallel instead of CPU；In order to accelerate
The detection speed of convolutional neural networks model, based on convolutional neural networks training unit, the network model weight that will can be trained
The new CNN disaggregated model structures for being modeled as variable step size, for the detection method in practical operation；The model is by huge
Full slice image carries out blocking processing, by the biological tissue region segmentation selected in advance into size identical ROI piecemeals, due to dividing
Detection between block can be with highly-parallel so that the speed of detection more GPU it is parallel under be significantly improved, then by can
The detection of the CNN disaggregated models of variable step, generate prediction probability distributed image；Pathological image data are divided into instruction by image data base
Practice collection, test set and checksum set etc.；The parameter of first convolution neural network model includes learning rate, frequency of training and how many layer
The network parameters such as network, training refer to when seeking optimal solution, the process of automatically adjusting parameter.
Preferably, the support system also includes convolutional neural networks model testing unit, for obtaining preferable convolution
Neural network model.It should be noted that " ideal " refers to that the accuracy rate of convolutional neural networks model is high herein, and " Shandong
Rod ".
Preferably, the convolutional neural networks model testing unit includes convolutional neural networks model checking unit and convolution
Neural network model test cell, the convolutional neural networks model checking unit are used to detect second convolutional neural networks
The accuracy rate of model；The convolutional neural networks model measurement unit, it is for detecting the second convolution neural network model
No over-fitting, to filter out the 3rd convolutional neural networks model of robust；It should be noted that if model is on test set
Accuracy rate during accuracy rate is trained with checksum set differs larger, then illustrates model over-fitting, now, can return to convolutional neural networks
In training unit, regulating networks structure or parameter, trained again to obtain more preferable network model；If on test set
Accuracy rate and checksum set train in accuracy rate be sufficiently close to, then illustrate the model more robust.
Preferably, the support system also includes convolutional neural networks model pre-training unit, for when described image number
During the deficiency of input image data being collected into according to obtaining unit, pre-training is carried out to the first convolution neural network model.
Preferably, the support system also includes pathological image data pre-processing unit, for screening and showing disease
Manage the region to be detected in image.
Preferably, in order to ensure the validity of detection, the pretreatment unit are filtered out described using Adaptive Thresholding
Region to be detected.
Preferably, the convolutional neural networks training unit trains the first convolutional neural networks mould using fine setting method
Type.
As another aspect of the present invention, present invention also offers a kind of pathological diagnosis of stomach cancer to support method, the branch
The method of holding comprises the following steps：
View data obtains：The pathological section image for obtaining stomach normal tissue sections image and the gastric cancer cases made a definite diagnosis is made
For input image data；
View data marks：The input image data is labeled, and ensures the label and image of image
True pathological diagnosis result is consistent；
Image data base is built：The classification of the view data of mark, the arrangement provided described image data mark unit, structure
Build pathological image database；
Convolutional neural networks construct：Construct the first convolution neural network model；And
Convolutional neural networks model training:Using the view data of the pathological image database to first convolution god
Parameter through network model is adjusted, and training the first convolution neural network model, obtains and can be used for detecting patient
Second convolution neural network model of pathological image data.
It should be noted that view data mark and image data base structure are considered as pathological image database sharing rank
Section.Preferably, the support method also includes convolutional neural networks model testing step：Obtain preferable convolutional neural networks mould
Type；The convolutional neural networks model testing step includes convolutional neural networks model checking and convolutional neural networks model is surveyed
Examination, the convolutional neural networks model checking are used for the accuracy rate for detecting the second convolution neural network model；The convolution
Neural network model is tested, for detect the second convolution neural network model whether over-fitting, to filter out the of robust
Three convolutional neural networks models.It should be noted that convolutional neural networks construction, convolutional neural networks model training and convolution god
The training stage of convolutional neural networks can be regarded as by being examined through network model, for obtaining preferable convolutional neural networks model.
As the third aspect of the invention, the invention further relates to clinic of the above-mentioned support system in pathological diagnosis stomach cancer
Using.
In summary, beneficial effects of the present invention are：
Compared with the artificial diagosis of existing pathologist, the gastric cancer of the invention based on big data and deep learning algorithm
The holding equipment of reason diagnosis has the advantages of high, the time-consuming short and run duration of accuracy rate is long, and this invention is in various big hospital
It will be helpful to solve that medical resource distribution is uneven, it is long-range excellent to realize including front three, the popularization of basic hospital and cloud service
Matter medical treatment etc., more convenient, more accurately pathological diagnosis service is provided for many patients；The realization of above-mentioned advantage is because the present invention
Apparatus and method using deep learning algorithm image recognition advantage, allow computer carry out big data rank gastric cancer reason
The deep learning of section, so as to train the intelligent neural network model that can be simulated pathologist diagosis and match in excellence or beauty therewith, warp
Continuous study and checking are crossed, the neural network model can realize the intelligent diagosis to stomach cancer pathological section, quick identification and obtain
Go out scientific conclusion.
Brief description of the drawings
Fig. 1 is the structured flowchart of the stomach cancer pathology diagnostic support system of the present invention；
Fig. 2 is that the flow chart of method is supported in the stomach cancer pathological diagnosis of the embodiment of the present invention two；
Fig. 3 is to stomach cancer slices figure；
Fig. 4 is the schematic diagram of the quick detection model of embodiments of the invention two；
Fig. 5 is flow chart of the stomach cancer pathology diagnostic support system of the present invention in application；
Wherein, 1, stomach cancer pathology diagnostic support system, 2, view data obtaining unit, 3, view data mark unit, 4,
Convolutional neural networks structural unit, 5, convolutional neural networks model training unit, 6, convolutional neural networks model testing unit, 7,
Image data base construction unit, 8, pathological image data pre-processing unit, 9, input terminal, 10, outlet terminal.
Embodiment
To better illustrate the object, technical solutions and advantages of the present invention, below in conjunction with the drawings and specific embodiments pair
The present invention is described further.
Embodiment 1
Referring to Fig. 1, a kind of embodiment of stomach cancer pathology diagnostic support system 1 of the invention, it includes：
View data obtaining unit 2, for obtaining the pathology of stomach normal tissue sections image and the gastric cancer cases made a definite diagnosis
Sectioning image is as input image data；
View data marks unit 3, for being labeled to input image data, and ensures the label and figure of image
The true pathological diagnosis result of picture is consistent；
Image data base construction unit 7, the view data of mark for providing view data mark unit are classified, are whole
Reason, build pathological image database；
Convolutional neural networks structural unit 4, for constructing the first convolution neural network model；
Convolutional neural networks model training unit 5, using the view data of pathological image database to the first convolutional Neural
The parameter of network model is adjusted, and the first convolution neural network model of training, obtains and can be used for detection patient's pathology figure
As the second convolution neural network model of data；
Convolutional neural networks model testing unit 6, for obtaining preferable convolutional neural networks model, including convolutional Neural
Network model verification unit (not shown) and convolutional neural networks model measurement unit (not shown), convolutional Neural net
Network model checking unit is used for the accuracy rate for detecting the second convolution neural network model；Convolutional neural networks model measurement unit,
For detect the second convolution neural network model whether over-fitting, to filter out the 3rd convolutional neural networks model of robust.
Convolutional neural networks model pre-training unit (not shown), for what is be collected into when view data obtaining unit
During input image data deficiency, pre-training is carried out to the first convolution neural network model；And
Pathological image data pre-processing unit 8, for screening and showing the region to be detected in patient's pathological image.
Wherein, pathological image data pre-processing unit 8 filters out region to be detected using Adaptive Thresholding；Convolutional Neural
Network model training unit 5 trains the first convolution neural network model using fine setting (fine-tune) method；Database include with
Lower four class data sets：Training set, disappear and test the disclosed pathological image data set of collection, test set and routine.
In addition, input terminal 9 is used for the pathology of existing stomach normal tissue sections image and the gastric cancer cases made a definite diagnosis
Sectioning image input image data obtaining unit 2, also, the data of these inputs finally will be by image data base construction unit 7
Categorised collection, for supporting follow-up clinical diagnosis to work；
And the pathological section image of patient to be detected is inputted into pathological image data pre-processing unit 8；
Outlet terminal 10, for the convolutional neural networks for the robust that will be obtained by convolutional neural networks model training unit 5
The result that model detects to the pathological section image classification for inputting the patient to be detected of pathological image data pre-processing unit 8
(histological type and corresponding probability) is presented to doctor, so that clinical diagnosis refers to.
Embodiment 2
Referring to Fig. 2, diagnosing gastric cancer of the invention supports a kind of embodiment of method, and it comprises the following steps：
(1) view data is gathered
Using ZhongShan University attached No.6 Hospital medical biotechnology storehouse data as data source, 11000 pathological section figures are gathered
Picture, including 5500 stomach normal tissue sections images and 5500 stomach cancer tissue slides, and respectively according to training set：Checksum set：
Test set=3：1：1 quantitative proportion is grouped at random.It is as shown in table 1 below：
The specific data of the pathological section image of table 1.
Acquired image is digitized scanning storage, sequence number is filed, establishment stomach cancer pathology image data base.
(2) image information is marked
Disease using existing ASAP image labelings software to the training set collected by step (1), checksum set and test set
Manage sectioning image and carry out data markers.To ensure the accuracy of information labeling, processing need to be optimized to image before mark.It is right
The mark work of image mainly includes：Various pathologic structure regions in image are sketched the contours of with different colours/thickness/actual situation lines,
According to the histopathology classification of stomach cancer, Pathologic specimen chip form, early carcinoma of stomach (intraepithelial neoplasia/severe SARS
Type hyperplasia) parting, by stages, lymphatic metastasis situations such as to image classification and assign score value, and rower is entered into the region sketched the contours
Label name.Pathological image after correct mark is digitized storage, to carry out the training of the network model of next step and verification.
Fig. 3 is the mark figure to atypical hyperplasia region in stomach cancer.
(3) training convolutional neural networks
1. design a model
(a) convolutional Neural is constructed in the way of convolutional layer, maximum sample level, nonlinear function, the cascade of full articulamentum
Network；
(b) capability of fitting of network is strengthened using multitiered network；
(c) port number of the output of the last full articulamentum of network is set to 2, and it is normal gastric normal group to represent the image respectively
Knit sectioning image, stomach cancer tissue slides image.
2. training network
(a) according to the view data being collected into step (1), (2), the parameter of convolutional neural networks model is adjusted
Section, the accuracy rate of classification is observed on checksum set；
(b) in order to accelerate the speed of training network, it is trained using the GPU calculated with high-speed parallel instead of CPU；
(c) method of the renewal of convolutional neural networks weighting parameter is solved using SGD, if convergence rate is slower, is made
Solved with optimization methods such as Adadelta, Adam；
(d) if training data (i.e. view data) number that step (1) is collected into very little, adopt by convolutional neural networks model
With elder generation fine-tune (fine setting) is used in conventional open pathological image data set pre-training, then by the view data being collected into
Method carry out training convolutional neural networks model；
(e) such as trained on existing convolutional neural networks model, the accuracy rate of classification can not rise, and can be rolled up by increasing
The depth of product neutral net network model increases the capability of fitting of convolutional neural networks model.
3. design quick detection model (as shown in Figure 4)
1. in order to improve detection efficiency, Adaptive Thresholding is used in pretreatment stage, is selected in advance from full slice image
Biological tissue region, the detection object (as shown in Fig. 4 arrows 101, representing preprocessing process) as convolutional neural networks.
2. in order to improve the degree of accuracy of detection and flexibility, based on step (3), the convolutional Neural net that will can be trained
Network is modeled as the CNN disaggregated models of variable step size again, for the detection method in practical operation；The model is by huge
Full slice image carries out blocking processing, by the biological tissue region segmentation selected in advance into size identical ROI piecemeals；Due to dividing
Detection between block can be with highly-parallel so that the speed of detection is effectively lifted (such as Fig. 4 arrows in the case of more GPU
Shown in first 102, representative model quick detection process).By the detection of convolutional neural networks model, prediction probability distribution map is generated
Picture.
3. based on the prediction probability distributed image of the 2nd step, in post processing, after screening out scattered point, analysis prediction probability divides
Butut, to obtain the prediction result of final full slice image (as shown in Fig. 4 arrows 103, representing last handling process).
(4) test set is verified
(a) the disaggregated model structure of the variable step size based on step 3., the convolutional Neural net trained in step (3) is used
Network model tests test set, accuracy rate of the observing and nursing on test set.
(b) if the convolutional neural networks model trained in step (3) is in the upper accuracy rate and training of test set
The accuracy rate difference of checksum set is larger, then illustrates model over-fitting；Now, it can return in step (3), adjust convolutional neural networks
Prototype network structure or parameter, obtain more preferable network model.
If (c) in accuracy rate and training of the convolutional neural networks model trained in step (3) on test set
The accuracy rate of checksum set is sufficiently close to, then illustrates the convolutional neural networks model more robust obtained by the training, and it is suitable to be used as
Detection sufferer pathological image network model.
Embodiment 3
A kind of application examples of the stomach cancer pathology diagnostic support system of the present invention, by pathological image to be detected by inputting eventually
Pathological image data pre-processing units 8 in the diagnosing gastric cancer holding equipments of the input of end 9 present invention, operational process afterwards referring to
Fig. 5, wherein,
(a) in order to ensure the validity of detection, Adaptive Thresholding is used in pretreatment stage, from stomach organization full slice
Biological tissue region (i.e. adaptive threshold result) is selected in image in advance, is then based on the choosing of threshold value results area Main subrack or extraction
Go out region to be detected (i.e. pathological tissue central area)；
(b) then, pathological tissue central area picture is pre-processed, pretreatment includes denoising, histogram equalization, normalization
Etc. image classification model VGG (the convolutional neural networks model previously trained --- the realities of step, afterwards input deep learning
Apply the second convolution neural network model in example 1) region to be detected (i.e. pathological tissue central area) selected to frame classifies
Detection, so as to predict stomach cancer classification and the corresponding probability belonging to the pathological section.
In Figure 5, arrow 201 represents adaptive thresholding step, and 202 represent extraction step：Based on threshold value fruiting area
Domain center, frame select or extracted region to be detected；203 represent pretreatment and pretreated picture are inputted into what is trained
The step of convolutional neural networks model；English implication in Fig. 5 is described as follows：Convolution, convolution；Conv Block, volume
Volume module；Output filter, output size；Fully connected, full articulamentum grader；Flatten, flattening；
Dense, Feature Dimension Reduction.
The comparison of method and existing method is supported in the stomach cancer pathological diagnosis of the present invention of embodiment 4
Clinically pathological diagnosis work is by being cut by pathologist manual read's pathological tissue of standardized training at present
Piece, analysis and diagnosis are made with reference to the clinical diagnosis experience of itself long term accumulation.Due to this artificial naked eyes diagosis method with
The factors such as pathologist experience, working condition, subjective emotion are closely related, therefore accuracy rate is not high, but time-consuming, and work is held
Continuous limited time, easily produce fail to pinpoint a disease in diagnosis, situations such as mistaken diagnosis and diagnosis are inconsistent.It is of the invention then using computer to the big of standardization
The deep learning of stomach cancer pathological image is measured, convolutional neural networks are carried out with parameter regulation and fitting is trained, so as to obtain more Shandong
The network model of rod.This neutral net based on big data and deep learning can simulate artificial diagosis, according to the new disease of input
Reason image draws corresponding output valve i.e. pathological diagnosis conclusion.Furthermore by Model Reconstruction, do not influenceing the feelings of accuracy in detection
Under condition, detection speed is greatly improved.
50 doctors with more than 3 years diagnosing gastric cancers and Couple herbs are chosen, everyone provides 20 doubtful stomach cancers respectively
Pathological image, judge its type, then calculate accuracy rate and average time, count diagnosis state, with examining for the present invention
Disconnected support method compares, and its result is as shown in table 2 below.
The comparison of the diagnosing gastric cancer result of table 2
It was found from upper table 2, histopathologic slide is read using the method for the present invention, its accuracy rate is than professional pathologist
Higher level, and time-consuming shorter, run duration length.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention rather than the present invention is protected
The limitation of scope is protected, although being explained in detail with reference to preferred embodiment to the present invention, one of ordinary skill in the art should
Understand, technical scheme can be modified or equivalent substitution, without departing from the essence of technical solution of the present invention
And scope.
Claims (10)
1. stomach cancer pathology diagnostic support system, it is characterised in that the support system includes：
View data obtaining unit, for obtaining the pathological section figure of stomach normal tissue sections image and the gastric cancer cases made a definite diagnosis
As input image data；
View data marks unit, for being labeled to the input image data, and ensures the label and figure of image
The true pathological diagnosis result of picture is consistent；
Image data base construction unit, the view data of mark for providing described image data mark unit are classified, are whole
Reason, build pathological image database；
Convolutional neural networks structural unit, for constructing the first convolution neural network model；And
Convolutional neural networks model training unit, using the view data of the pathological image database to first convolution god
Parameter through network model is adjusted, and training the first convolution neural network model, obtains and can be used for detecting patient
Second convolution neural network model of pathological image data.
2. support system according to claim 1, it is characterised in that the support system also includes convolutional neural networks mould
Type verification unit, for obtaining preferable convolutional neural networks model.
3. support system according to claim 2, it is characterised in that the convolutional neural networks model testing unit includes
Convolutional neural networks model checking unit and convolutional neural networks model measurement unit, the convolutional neural networks model checking list
Member is used for the accuracy rate for detecting the second convolution neural network model；The convolutional neural networks model measurement unit, is used for
Detect the second convolution neural network model whether over-fitting, to filter out the 3rd convolutional neural networks model of robust.
4. support system according to claim 1, it is characterised in that the support system also includes convolutional neural networks mould
Type pre-training unit, for be collected into when described image data acquiring unit the deficiency of input image data when, to described
One convolution neural network model carries out pre-training.
5. support system according to claim 1, it is characterised in that it is pre- that the support system also includes pathological image data
Processing unit, for screening and showing the region to be detected in patient's pathological image.
6. support system according to claim 5, it is characterised in that the pretreatment unit is sieved using Adaptive Thresholding
Select the region to be detected.
7. support system according to claim 1, it is characterised in that the convolutional neural networks training unit is using fine setting
Method trains the first convolution neural network model.
A kind of 8. support method of stomach cancer pathological diagnosis, it is characterised in that the support method comprises the following steps：
View data obtains：The pathological section image of stomach normal tissue sections image and the gastric cancer cases made a definite diagnosis is obtained as
Input image data；
View data marks：The input image data is labeled, and ensure image label and image it is true
Pathological diagnosis result is consistent；
Image data base is built：The classification of the view data of mark, the arrangement provided described image data mark unit, structure disease
Manage image data base；
Convolutional neural networks construct：Construct the first convolution neural network model；And
Convolutional neural networks model training:Using the view data of the pathological image database to the first convolution nerve net
The parameter of network model is adjusted, and training the first convolution neural network model, obtains and can be used for detection patient's pathology
Second convolution neural network model of view data.
9. support method according to claim 8, it is characterised in that the support method also includes convolutional neural networks mould
Type checking procedure：Obtain preferable convolutional neural networks model；The convolutional neural networks model testing step includes convolution god
Through network model verification and convolutional neural networks model measurement, the convolutional neural networks model checking is used to detect described second
The accuracy rate of convolutional neural networks model；The convolutional neural networks model measurement, for detecting the second convolution nerve net
Network model whether over-fitting, to filter out the 3rd convolutional neural networks model of robust.
10. according to application of any described holding equipments of claim 1-6 in diagnosis of gastric cancer.
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| CN201710423350.2A CN107368670A (en) | 2017-06-07 | 2017-06-07 | Stomach cancer pathology diagnostic support system and method based on big data deep learning |
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