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CN108021916A - Deep learning diabetic retinopathy sorting technique based on notice mechanism - Google Patents

Deep learning diabetic retinopathy sorting technique based on notice mechanism Download PDF

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CN108021916A
CN108021916A CN201711497342.9A CN201711497342A CN108021916A CN 108021916 A CN108021916 A CN 108021916A CN 201711497342 A CN201711497342 A CN 201711497342A CN 108021916 A CN108021916 A CN 108021916A
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CN108021916B (en
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万程
于凤丽
游齐靖
刘江
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Nanjing University of Aeronautics and Astronautics
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Abstract

The present invention discloses a kind of deep learning diabetic retinopathy sorting technique based on notice mechanism, including：A series of eye fundus images are chosen as primary data sample, are divided into training set and test set after sample being normalized pretreatment, cutting；Parameter initialization and fine setting are carried out to main neutral net, training set image is inputted to main neutral net and is trained, generates characteristic pattern；The parameter of fixed main neutral net, notice network is trained using training set image, is exported lesion candidate regions degree figure and is normalized to gain attention and tries hard to, and will pay attention to trying hard to gaining attention power mechanism with characteristic pattern product；Notice mechanism is tried to achieve into result and inputs main neutral net, continues to train using training set image, finally obtains diabetic retinopathy grade separation model.By introducing notice mechanism, using diabetic retinopathy area data set pair, it is trained the present invention, and the information characteristics of lesion region are strengthened while network primitive character is retained.

Description

Deep learning diabetic retinopathy sorting technique based on notice mechanism
Technical field
The present invention relates to the deep learning diabetic retinopathy sorting technique based on notice mechanism, belong to medicine figure As process field.
Background technology
Clinically diagnosis of the doctor for diabetic retinopathy is by observing and analyzing retina eyeground figure at present As upper early stage pathology feature is as the symptoms such as aneurysms, hard exudate and bleeding carry out.In practice, diabetes view Film lesions species is more, and lesion is various, the sufferer order of severity differs, and causes oculist's difficult diagnosis.Therefore, extensive Diabetic retinopathy disorder in screening in, computer-aided diagnosis technology can mitigate the burden of doctor significantly, and quickly, Effectively auxiliary doctor realizes classification diagnosis.
In the automatic diagnosis algorithm of Most current, the classification for diabetic retinopathy eye fundus image is based primarily upon biography System manual method designs extraction feature, then carries out the structure of grader.Such as using including shape, color, brightness and priori The craft feature such as knowledge carries out diabetic retinopathy detection, these methods can only can obtain preferably on small data set As a result, since manual features extraction process is cumbersome, efficiency is low in the case of large data sets and poor robustness.With artificial intelligence The development of algorithm, has had researcher to propose the diabetic retinopathy classification diagnosis side for being directly based upon deep learning at present Method, for example, by convolutional neural networks be directly connected to eye fundus image carry out diabetic retinopathy classification task, such method Designed for all diabetic retinopathy types, only regard convolutional neural networks as a flight data recorder, not Have taking into account with diagnosing closely related lesion distributed intelligence, lack effectively and intuitively explain.
The content of the invention
Goal of the invention：Present invention aims in view of the deficiencies of the prior art, there is provided a kind of depth based on notice mechanism Degree study diabetic retinopathy sorting technique, this method introduce notice mechanism in depth convolutional network, will pay attention to Power internet startup disk into depth network, and using expert mark diabetic retinopathy area data set pair its instructed Practice, notice network can introduce expertise, and generation includes the lesion area-of-interest of candidate lesion region position, this method The information characteristics of lesion region can be strengthened while network primitive character is retained.
Technical solution：Deep learning diabetic retinopathy sorting technique of the present invention based on notice mechanism, It is characterised in that it includes following steps：
(1) a series of eyeground figures in EyePACS data sets, DiaretDB1 data sets, Messidor data sets are chosen respectively As being used as primary data sample, pretreatment is normalized to eye fundus image, cutting is carried out after pretreatment and ensures that size is identical, will Eye fundus image after cutting is divided into training set and test set；
(2) convolutional neural networks model is built, the convolutional neural networks model includes main neutral net and notice net Network；Individually main neutral net is trained using ImageNet parameters, by the obtained parameter of training to main neutral net into Row finely tunes and preserves main neural network model parameter；In the main neural network model parameter of preservation, diabetic retina is chosen The best main neural network model parameter of lesion grade separation initializes the main neural network parameter portion in convolutional neural networks Point, remaining stochastic parameter initialization；
(3) the training set image in EyePACS data sets is inputted to main neutral net and be trained, generate characteristic pattern； The parameter of fixed main neutral net, notice network, notice net are trained using the training set image in DiaretDB1 data sets Network exports a lesion candidate region gray-scale map；
(4) the lesion candidate region gray-scale map that notice network generates is normalized to gain attention and tried hard to, and will note Meaning tries hard to the characteristic pattern with the output of main neural network into row element dot product, and product gains attention power mechanism；
(5) notice mechanism is tried to achieve result to input in main neutral net, using the training set figure in EyePACS data sets As continuing to train, the parameter of main neutral net is adjusted according to the learning rate of setting when training, diabetes view may finally be obtained Film lesion grade separation model.
The training set of the classification of DR 5 is carried out with EyePACs data sets, DiaretDB1 data sets are the DR lesions of expert's mark The data set in region, for training notice network portion, Messidor data sets are the data sets of another DR classification, are used for Verify the robustness of network.
Above-mentioned technical proposal is further improved, the pretreatment operation of the step (1) is：Scheme in extraction primary data sample The foreground area of picture, using following formula to pretreatment is normalized,
Ic(x, y)=α I (x, y)+β Gaussion (x, y, ρ) * I (x, y)+γ
Wherein, I is input picture, and * represents convolution operation, and Gaussion (x, y, ρ) represents the gaussian filtering that standard deviation is ρ Device, parameter alpha, beta, gamma and ρ are rule of thumb arranged to α=4, and β=- 4, using eye fundus image center as the center of circle, erode to eyeground figure side The region of edge 5%；
It is 720 × 720 by pretreated image cropping, and the image for being divided into training set is subjected to Random-Rotation 0 °/90 °/180 °/270 ° to realize view data augmentation.
Further, using the method for transfer learning in the step (2), the parameter pair that training obtains on ImageNet Main neutral net is finely adjusted, and selects the best main neural network model parameter of diabetic retinopathy grade separation to convolution Main neutral net is initialized in neutral net, and random initializtion remainder parameter.
Further, it will be carried out in the step (4) after the output normalization of notice network plus 1 operates, acquired results Dot product operation is carried out with the characteristic pattern of main neutral net certain layer, is then input in main neutral net and carries out follow-up training. Characteristic layer is conv2d_2b_3x3 convolutional layers, and characteristic pattern is Feature map M.
Further, the notice network is a symmetrical full convolutional network, includes 5 convolution of down-sampling process 5 uncoiling laminations of layer and upsampling process.
Further, the main neutral net uses inception-resnet-v2, integrates residual error learning structure and perception Two kinds of modules of structure.
Calculated in the step (3) to (5) using optimization of the Nesterov Momentum algorithms as convolutional neural networks Method, the momentum Optimization Factor used are 0.9 all weights of renewal；In the repetitive exercise each time of network, using mean square error Loss function as diabetic retinopathy grade separation trains neutral nets at different levels, in error amount back-propagation process In, Grad, and the parameter of calculated Grad renewal network are calculated using Nesterov Momentum algorithms, L2 regularization terms are used to all parameters of network, weight decay factor is 0.0005；According to the loss function of notice network, Grad is calculated using Nesterov Momentum algorithms, the parameter of renewal notice network completes the iteration mistake successively of network Journey.
The present invention makees optimization algorithm using Nesterov Momentum algorithms (being one kind of stochastic gradient algorithm), at the same time Neutral nets at different levels are trained as loss function using mean square error (Mean Squared Error, MSD), to parameter in network Using L2Weight Decay regularization, and batch-normlization methods are used in a network.Optimization algorithm is applied to In the training process of network, for the renewal of network weight, gradient is calculated during the error back propagation of network, updates net Network parameter.
Beneficial effect：1st, notice mechanism is introduced in depth convolutional network, by notice internet startup disk to depth network In, and using expert mark diabetic retinopathy area data set pair its be trained, notice network can introduce Expertise, generation include the lesion area-of-interest of candidate lesion region position.
2nd, using the depth network based on residual error perceptual structure, and will be noted using feature Enhancement Method in the training of network The lesion candidate region knowledge of meaning power network generation is incorporated into diabetic retinopathy classification task.This method is retaining original The corresponding characteristic information of area-of-interest is strengthened on the basis of beginning characteristic information.
3rd, in network model diagnosis of diabetic retinopathy result is improved using the loss function based on mean square error. The model is a kind of multi-task learning model, at the same time can be to the lesion of eye fundus image lifting classification performance using expertise Area carries out coarse positioning, has good robustness.
Brief description of the drawings
Fig. 1：The present invention's realizes flow frame diagram
Fig. 2：Notice function structure chart
Fig. 3：Comparison diagram before and after eye fundus image normalization
Fig. 4：The lesion candidate region that notice structural network produces and actual expert's tab area comparison diagram.
Embodiment
Technical solution of the present invention is described in detail below by attached drawing, but protection scope of the present invention is not limited to The embodiment.
Embodiment 1：Deep learning diabetic retinopathy grade separation provided by the invention based on notice mechanism Method is detected diabetic retinopathy grade identification, and concrete operations carry out as follows：
1st, data set is chosen；
(1) EyePACS data sets
Five categorized data set of diabetic retinopathy disease grade includes 88702 coloured silks from 44315 patients Color eye fundus image, the resolution ratio of image is between.The data set is divided into two parts：Training set 35126, which is opened, (comes from 17563 Patient), test set 53576 is opened and (comes from 26788 patients).The DR menace levels of every eye fundus image are by doctor according to ETDRS Table is labeled：' 0 ' represents no diabetic retinopathy, and ' 1 ' represents slight non-appreciation diabetic retinopathy, ' 2 ' represent medium diabetes mellitus retinopathy, and ' 3 ' represent severe diabetic retinopathy, and ' 4 ' expression appreciation diabetes regard Retinopathy.EyePACS data sets have following features：(1) as shown in table 1, the category distribution of the data set is seriously uneven, its The data accounting of middle classification 0 is 79%, and the data accounting of classification 3 and 4 is respectively 2% and 2%；(2) adopting due to the data set Collection has larger otherness from different fundus cameras, the resolution ratio and picture quality of image.
Table 1EyePACS data distributions
(2) DiaretDB1 data sets
The data set shares 89 colored eye fundus images, and every eye fundus image is marked out potential manually by 4 medical experts Focal area, including aneurysms, hard exudate, bleeding and cotton-wool spot these lesion regions.The data are concentrated with 5 Picture is no diabetic retinopathy, and in addition in 84 images, a kind of lesion region is included at least in base map of often opening one's eyes.Mark The label image of note saves as the gray level image of 0-255, and wherein gray value is bigger represents that the region lesion is more serious.
(3) Messidor data sets
The data set shares 1200 colored eye fundus images, wherein 540 normal pictures 660 open diseased image, includes three Kind resolution ratio：1440x960,2240x1488 and 2304x1536, image are TIF forms.Oozed according to microaneurysm, bleeding, hardness Go out DR lesion grade of the lesions such as thing to every image labeling from 0 to 3, the wherein image of label ' 0 ' there are 546 to account for total amount of data 46%, the image of label ' 1 ' has 154 to account for the 12.75% of total amount of data, and the image of label ' 2 ' has 247 to account for total amount of data 20.58%, the image of label ' 3 ' has 253 to account for the 21.67% of total amount of data.
2nd, data prediction
It is overseas for the useful region of interest of checkout and diagnosis due in eye fundus image, removing, it further comprises in image big The background area of amount, therefore before being diagnosed, we extract foreground area to content of interest first.
Ic(x, y)=α I (x, y)+β Gaussion (x, y, ρ) * I (x, y)+γ
Eye fundus image normalization, the normalized image pre-processed are carried out according to above formula.Wherein, I is input picture, * Represent convolution operation, Gaussion (x, y, ρ) represents the Gaussian filter that standard deviation is ρ, and parameter alpha, beta, gamma and ρ are rule of thumb set It is set to α=4, β=- 4.Since in the visual field, halation phenomenon usually occurs in the marginal portion of eyeground figure, using eye fundus image center as The center of circle, erodes to the region at eyeground figure edge 5%.Eye fundus image is as shown in Figure 3 after being normalized.
In this method, the input image size used is chosen for 720 × 720, and passes through Random-Rotation on training set (0°/90°/180°/270°) and the random data set progress data augmentation overturn to passing through pretreatment.
3rd, training convolutional neural networks model
In network training process, data are input to network in batches, and a lot data includes two queues：DiaretDB1 Data set and EyePACS data sets, in the repetitive exercise each time of network：DiaretDB1 data sets are sent into convolutional Neural net Network, produces lesion candidate region gray-scale map by notice network, the error of notice network is calculated by MSE loss functions； EyePACS data are sent into convolutional neural networks, and corresponding lesion candidate region information, notice net are generated by notice network Carried out after the output normalization of network plus 1 operates, the characteristic pattern of acquired results and main neutral net certain layer conv2d_2b_3x3 Feature Map M carry out dot product operation, are then input in main neutral net and carry out follow-up training；Utilize mean square error (mean-square error, MSE) is as calculating diabetic retinopathy (diabetic retinopathy, DR) grade The loss function of classification trains neutral nets at different levels.According to notice network losses function, the parameter of renewal notice network；Root According to DR grade separation loss functions, main neutral net corresponding part parameter is updated, completes the iterative process successively of network.
The lesion region candidate that DiaretDB1 data sets produce is schemed to be melted with clinical information by the training of notice network Close, this method can be incorporated into expertise in network and improve classification performance.Fig. 4 is the lesion that notice structural network produces Candidate region and the comparative examples of actual expert's tab area.
In the training process, the present invention uses the stochastic gradient descent method (Stochastic based on mini-batch Gradient Descent, SGD) optimize, update all weights using the SGD that momentum Optimization Factor is 0.9.Network uses Based on MSE as loss function, and L2 regularization terms are used each parameter in network, weight decay factor is 0.0005.
4th, handling result is analyzed
This method carrys out quantification treatment as a result, being respectively using following four performance metric：Classification accuracy (accuracy, ACC), specific (specificity, SPE), sensitiveness (sensitivity, SEN) and AUC (the area under ROC Curve) value.For classification results, addition is used based on secondary weighted kappa values as another performance metric, it can be with Weigh predicted value and the direct uniformity of physical tags.Kappa value calculations are as follows：
To the confusion matrix O, O of one N × N of image prediction label configurationsi,jThe amount of images of (i, j) is designated as under corresponding to, Weighting matrix is expressed as：
EN×NRepresent the confusion matrix of image true tag construction, it is assumed that without correlation between prediction label and true tag Property, then secondary weighted kappa values are as follows：
Handling result of this method on EyePACS data sets used possesses 0.840 kappa values on verification collection, surveys Possess 0.835 kappa values on examination collection.Our method is further verified on Messidor data sets.We Method accuracy, AUC value indices on all achieve fabulous result.Specifically, this method is in referable/non- Achieve 91.6% accuracy rate in referable tasks, 90.3% sensitiveness, 95.2% specificity and 0.963 AUC value.
Employ the eye fundus image more than 30,000 by professional's mark in the present embodiment to be trained, 50,000 82% accuracy rate is reached in the test data set of multiple.Many experiments show, proposed by the present invention to be based on notice mechanism Deep learning method there is higher classification performance.The deep learning diabetic retina based on notice mechanism more than The disaggregated model that lesion classification method is established can carry out automation classification to diabetic retinopathy, and in category distribution There is good robustness, this has having very important significance on medical domain in unbalanced data.
As described above, although the present invention has been represented and described with reference to specific preferred embodiment, but it must not be explained For to the limitation of itself of the invention., can be right under the premise of the spirit and scope of the present invention that appended claims define are not departed from Various changes can be made in the form and details for it.

Claims (7)

1. the deep learning diabetic retinopathy sorting technique based on notice mechanism, it is characterised in that including following step Suddenly：
(1) a series of eye fundus images in EyePACS data sets, DiaretDB1 data sets, Messidor data sets are chosen respectively to make For primary data sample, pretreatment is normalized to eye fundus image, cutting is carried out after pretreatment and ensures that size is identical, will be cut Eye fundus image afterwards is divided into training set and test set；
(2) convolutional neural networks model is built, the convolutional neural networks model includes main neutral net and notice network；Adopt Individually main neutral net is trained with ImageNet parameters, the parameter obtained by training is finely adjusted main neutral net And preserve main neural network model parameter；In the main neural network model parameter of preservation, diabetic retinopathy etc. is chosen Level classifies best main neural network model parameter to initialize the main neural network parameter part in convolutional neural networks, remaining Stochastic parameter initializes；
(3) the training set image in EyePACS data sets is inputted to main neutral net and be trained, generate characteristic pattern；It is fixed The parameter of main neutral net, trains notice network, notice network is defeated using the training set image in DiaretDB1 data sets Go out lesion candidate region gray-scale map；
(4) the lesion candidate region gray-scale map that notice network generates is normalized to gain attention and tried hard to, and by notice Into row element dot product, product gains attention power mechanism the characteristic pattern of figure and the output of main neural network；
(5) notice mechanism is tried to achieve result to input in main neutral net, using the training set image in EyePACS data sets after Continuous training, adjusts the parameter of main neutral net according to the learning rate of setting when training, finally obtains diabetic retinopathy etc. Level disaggregated model.
2. the deep learning diabetic retinopathy sorting technique according to claim 1 based on notice mechanism, its It is characterized in that：The pretreatment operation of the step (1) is：The foreground area of image in primary data sample is extracted, using following formula To pretreatment is normalized,
Ic(x, y)=α I (x, y)+β Gaussion (x, y, ρ) * I (x, y)+γ
Wherein, I is input picture, and * represents convolution operation, and Gaussion (x, y, ρ) represents the Gaussian filter that standard deviation is ρ, Parameter alpha, beta, gamma and ρ are rule of thumb arranged to α=4, and β=- 4, using eye fundus image center as the center of circle, erode to eyeground figure edge 5% region；
By pretreated image cropping be 720 × 720, and by be divided into training set image carry out 0 ° of Random-Rotation/ 90 °/180 °/270 ° to realize view data augmentation.
3. the deep learning diabetic retinopathy sorting technique according to claim 1 based on notice mechanism, its It is characterized in that：Using the method for transfer learning in the step (2), the parameter that training obtains on ImageNet to main nerve net Network is finely adjusted, and selects the best main neural network model parameter of diabetic retinopathy grade separation to convolutional neural networks Middle main neutral net is initialized, and random initializtion remainder parameter.
4. the deep learning diabetic retinopathy sorting technique according to claim 1 based on notice mechanism, its It is characterized in that：It will be carried out in the step (4) after the output normalization of notice network plus 1 operates, acquired results and main nerve The characteristic pattern Feature Map M of network certain layer conv2d_2b_3x3 carry out dot product operation, are then input to main neutral net It is middle to carry out follow-up training.
5. the deep learning diabetic retinopathy sorting technique according to claim 1 based on notice mechanism, its It is characterized in that：The notice network is a symmetrical full convolutional network, including 5 convolutional layers of down-sampling process and is above adopted 5 uncoiling laminations of sample process.
6. the deep learning diabetic retinopathy sorting technique according to claim 1 based on notice mechanism, its It is characterized in that：The main neutral net uses inception-resnet-v2, integrates two kinds of residual error learning structure and perceptual structure Module.
7. the deep learning diabetic retinopathy sorting technique according to claim 1 based on notice mechanism, its It is characterized in that：Calculated in the step (3) to (5) using optimization of the Nesterov Momentum algorithms as convolutional neural networks Method, the momentum Optimization Factor used are 0.9 all weights of renewal；In the repetitive exercise each time of network, using mean square error Loss function as diabetic retinopathy grade separation trains neutral nets at different levels, in error amount back-propagation process In, Grad, and the parameter of calculated Grad renewal network are calculated using Nesterov Momentum algorithms, L2 regularization terms are used to all parameters of network, weight decay factor is 0.0005；According to the loss function of notice network, Grad is calculated using Nesterov Momentum algorithms, the parameter of renewal notice network completes the iteration mistake successively of network Journey.
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