CN113780478A - Activity classification model training method, classification method, apparatus, device and medium - Google Patents
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Abstract
The embodiment relates to the technical field of artificial intelligence, in particular to an activity classification model training method, a classification method, a device, equipment and a medium. The activity classification model training method comprises the following steps: acquiring an image sample data set of choroidal neovascularization; inputting each image sample data to a preset neural network model; extracting the characteristics of each image sample data through a plurality of network layers of a first neural network to obtain a characteristic image; performing choroidal neovascularization activity classification processing on the characteristic image through a second neural network to obtain a first loss value; performing attention enhancement processing on the characteristic image through a preset attention enhancement model to obtain a second loss value; and training the neural network model according to the first loss value and the second loss value to obtain an activity classification model of the choroid neovascularization. The scheme realizes automatic classification of the activity of the choroidal neovascularization, and improves the efficiency of identifying the activity of the choroidal neovascularization.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to an activity classification model training method, a classification method, a device, equipment and a medium.
Background
Choroidal Neovascularization (CNV) is a fibrovascular tissue that is proliferative below or above the retinal pigment epithelium, resulting from a variety of etiologies. For inactive CNVs, follow-up observation or conservative treatment is only needed, but for active CNVs, the leakage caused by the active CNVs seriously affects the vision, so that timely injection treatment is needed.
In the related art, the CNV activity can be judged by injecting a contrast agent into the retina, but the contrast agent has side effects and increases the risk of human diseases.
Disclosure of Invention
The main purpose of the embodiments of the present disclosure is to provide an activity classification model training method, a classification method, an apparatus, a device, and a medium, which can conveniently establish an activity classification model for classifying the activity of choroidal neovascularization, thereby implementing automatic classification of the activity of choroidal neovascularization, and improving the efficiency of identifying the activity of choroidal neovascularization.
To achieve the above object, a first aspect of the embodiments of the present disclosure provides an activity classification model training method for choroidal neovascularization, including:
acquiring an image sample data set of choroidal neovascularization; the image sample data set comprises a plurality of image sample data;
inputting each image sample data of the image sample data set to a preset neural network model; wherein the neural network model comprises a first neural network and a second neural network;
extracting the characteristics of each image sample data through a plurality of network layers of a first neural network to obtain a characteristic image;
carrying out choroidal neovascularization activity classification processing on the characteristic images through a second neural network to obtain a first loss value of sample data of each image; wherein the first loss value is indicative of the activity of choroidal neovascularization;
performing attention enhancement processing on the characteristic image through a preset attention enhancement model to obtain a second loss value of each image sample data; wherein the second loss value is indicative of the activity of choroidal neovascularization;
training the neural network model according to the first loss value and the second loss value to obtain an activity classification model of choroidal neovascularization; wherein the activity classification model is used to classify choroidal neovascularization activity.
In some implementations, the several network layers include: a dimension-reducing convolutional layer and at least one moving and turning bottleneck convolutional layer;
the method comprises the following steps of performing feature extraction on sample data of each image through a plurality of network layers of a first neural network to obtain a feature image, wherein the feature extraction comprises the following steps:
performing dimension reduction convolution processing on each image sample data through a dimension reduction convolution layer to obtain a dimension reduction convolution image;
and carrying out moving and turning bottleneck convolution processing on the dimensionality reduction convolution image through the moving and turning bottleneck convolution layer to obtain the characteristic image.
In some embodiments, the second neural network comprises: a global average pooling layer, a full-link layer and a normalization classification layer;
performing choroidal neovascularization activity classification processing on the feature images through a second neural network to obtain a first loss value of sample data of each image, and the method comprises the following steps:
performing pooling processing on the characteristic image through a global average pooling layer to obtain a pooling vector;
performing full-connection processing on the pooled vectors through a full-connection layer to obtain a primary classification value;
and carrying out normalization processing on the preliminary classification value through a normalization classification layer to obtain a first loss value.
In some embodiments, an actual frame is arranged in the image sample data, and the actual frame is used for marking the position of an actual leakage area of the image sample data;
performing attention enhancement processing on the feature image through a preset attention enhancement model to obtain a second loss value of each image sample data, including:
carrying out up-sampling on the characteristic image to obtain a prediction frame; the prediction frame is used for marking the position of a prediction leakage area of the image sample data;
and performing loss calculation on the predicted frame and the actual frame to obtain a second loss value.
In some embodiments, training the neural network model according to the first loss value and the second loss value to obtain an activity classification model of choroidal neovascularization comprises:
weighting the first loss value and the second loss value according to a preset weight to obtain a target loss value;
and training the neural network model according to the target loss value to obtain an activity classification model of the choroidal neovascularization.
To achieve the above object, a second aspect of the embodiments of the present disclosure provides an image classification method, including:
acquiring an image to be detected;
inputting the image to be detected into an activity classification model for classification treatment to obtain the activity class of choroidal neovascularization; wherein the activity classification model is trained according to the method of any one of the embodiments of the first aspect.
To achieve the above object, a third aspect of the embodiments of the present disclosure provides an activity classification model training apparatus for choroidal neovascularization, comprising:
the system comprises a sample acquisition module, a data acquisition module and a data acquisition module, wherein the sample acquisition module is used for acquiring an image sample data set of choroidal neovascularization; the image sample data set comprises a plurality of image sample data;
the input module is used for inputting each image sample data of the image sample data set to a preset neural network model; wherein the neural network model comprises a first neural network and a second neural network;
the characteristic extraction module is used for extracting the characteristics of each image sample data through a plurality of network layers of the first neural network to obtain a characteristic image;
the classification processing module is used for carrying out choroidal neovascularization activity classification processing on the feature images through a second neural network to obtain a first loss value of each image sample data; wherein the first loss value is indicative of the activity of choroidal neovascularization;
the enhancement processing module is used for carrying out attention enhancement processing on the characteristic image through a preset attention enhancement model to obtain a second loss value of each image sample data; wherein the second loss value is indicative of the activity of choroidal neovascularization;
the training processing module is used for training the neural network model according to the first loss value and the second loss value to obtain an activity classification model of choroid neovessels; wherein the activity classification model is used to classify choroidal neovascularization activity.
To achieve the above object, a fourth aspect of the embodiments of the present disclosure provides an image classification apparatus including:
the image acquisition module is used for acquiring an image to be detected;
the image classification module is used for inputting the image to be detected into the activity classification model for classification processing to obtain the activity category of the choroid neovascularization; wherein the activity classification model is trained according to the method of any one of the embodiments of the first aspect.
To achieve the above object, a fifth aspect of an embodiment of the present disclosure provides an electronic device, including:
at least one memory;
at least one processor;
at least one program;
the program is stored in the memory, and the processor executes at least one program to implement:
the method of any one of the embodiments of the first aspect; alternatively, the first and second electrodes may be,
as in the method of the embodiment of the second aspect.
To achieve the above object, a sixth aspect of embodiments of the present disclosure proposes a storage medium which is a computer-readable storage medium storing computer-executable instructions for causing a computer to perform:
the method of any one of the embodiments of the first aspect; alternatively, the first and second electrodes may be,
as in the method of the embodiment of the second aspect.
The activity classification model training method, the classification method, the device, the equipment and the medium provided by the embodiment of the disclosure input each image sample data into the first neural network, perform feature extraction on each image sample data through a plurality of network layers of the first neural network to obtain a corresponding feature image, and then perform choroidal neovascularization activity classification processing on the feature image according to the second neural network to obtain a first loss value representing choroidal neovascularization activity; introducing a seepage zone attention branch, and performing attention enhancement processing on the characteristic image by adopting a preset attention enhancement model to obtain a second loss value representing the activity of choroidal neovascularization; and training the neural network model according to the first loss value and the second loss value to obtain an activity classification model of the choroid neovasculature. The activity classification model of the choroidal neovascularization vessels, which is obtained by the scheme, realizes automatic classification of the activity of the choroidal neovascularization vessels, avoids the side effect of using a contrast medium, reduces the cost for judging the activity of the choroidal neovascularization vessels, reduces the qualification requirement of a doctor for judging the activity of the choroidal neovascularization vessels, and can better judge the activity of the choroidal neovascularization vessels by adding the attention branch of a leakage region and carrying out attention enhancement treatment on the characteristic image by using the attention enhancement model.
Drawings
Fig. 1 is a flowchart of an activity classification model training method for choroidal neovascularization provided by an embodiment of the present application;
FIG. 2 is a flowchart of a specific method of step S103 in FIG. 1;
FIG. 3 is a flowchart of a specific method of step S104 in FIG. 1;
FIG. 4 is a flowchart of a specific method of step S105 in FIG. 1;
FIG. 5 is a flowchart of a specific method of step S106 in FIG. 1;
fig. 6 is a schematic diagram of a specific application scenario of an activity classification model training method provided in the embodiment of the present application;
FIG. 7 is a flowchart of an image classification method provided in an embodiment of the present application;
fig. 8 is a block diagram of an activity classification model training apparatus for choroidal neovascularization according to an embodiment of the present application;
fig. 9 is a schematic diagram of a hardware structure of an electronic device provided in an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
It should be noted that although functional blocks are partitioned in a schematic diagram of an apparatus and a logical order is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the partitioning of blocks in the apparatus or the order in the flowchart. The terms first, second and the like in the description and in the claims, and the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the application.
First, several terms referred to in the present application are resolved:
artificial Intelligence (AI): is a new technical science for researching and developing theories, methods, technologies and application systems for simulating, extending and expanding human intelligence; artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produces a new intelligent machine that can react in a manner similar to human intelligence, and research in this field includes robotics, language recognition, image recognition, natural language processing, and expert systems, among others. The artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence is also a theory, method, technique and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use the knowledge to obtain the best results.
Natural Language Processing (NLP): NLP uses computer to process, understand and use human language (such as chinese, english, etc.), and belongs to a branch of artificial intelligence, which is a cross discipline between computer science and linguistics, also commonly called computational linguistics. Natural language processing includes parsing, semantic analysis, discourse understanding, and the like. Natural language processing is commonly used in the technical fields of machine translation, character recognition of handwriting and print, speech recognition and text-to-speech conversion, information retrieval, information extraction and filtering, text classification and clustering, public opinion analysis and opinion mining, and relates to data mining, machine learning, knowledge acquisition, knowledge engineering, artificial intelligence research, linguistic research related to language calculation, and the like, which are related to language processing.
Medical cloud: the medical cloud is a medical health service cloud platform established by using cloud computing on the basis of new technologies such as cloud computing, mobile technology, multimedia, 4G communication, big data, Internet of things and the like and combining medical technology, so that sharing of medical resources and expansion of medical scope are realized. Due to the combination of the cloud computing technology, the medical cloud improves the efficiency of medical institutions and brings convenience to residents to see medical advice. Like the appointment register, the electronic medical record, the medical insurance and the like of the existing hospital are all products combining cloud computing and the medical field, and the medical cloud also has the advantages of data security, information sharing, dynamic expansion and overall layout.
Optical Coherence Tomography (OCT): OCT is a new tomography technique with the greatest development prospect in recent years, especially has attractive application prospects in biological tissue biopsy and imaging, has been tried to be applied to clinical diagnosis in ophthalmology, dentistry and dermatology, is another technological breakthrough after X-CT and MRI techniques, and has been rapidly developed in recent years.
EfficientNetB0 network model: the Efficientnets network Model structure is proposed by engineers in Google brain Tan Star and chief scientist Quoc V.le in the paper "EfficientNet: Rethinking Model Scaling for volumetric Neural Networks". The underlying network architecture of the model is designed by using a neural network architecture search (neural architecture search). The EfficientNet B0 network model is a basic network model in the EfficientNet series, and the model gives consideration to the accuracy and speed of prediction. The EfficientNetB0 network model includes a plurality of repeated mobile inverted bottle neck convolution (MBCONV) modules, convolution layers (CONV), Batch Normalization layers (BN), Swish activation functions, Global Average Pooling (GAP), full connection layers (FC), Softmax classification layers, and the like. The mobile inverted bottle neck convolution (MBCONV) module is a core idea of the EfficientNetB0 network model.
Softmax function: the Softmax function is a normalized exponential function that "compresses" a K-dimensional vector z containing arbitrary real numbers into another K-dimensional real vector σ (z) such that each element ranges between (0,1) and the sum of all elements is 1, which is commonly used in multi-classification problems.
GIoU loss: the combined loss Generalized Intersection over Union loss (GIoU loss) may be used as a loss function. The GIoU is a loss calculation method for frame prediction from IoU, and in the field of target detection and the like, it is necessary to compare a predicted frame with an actual labeled frame to calculate loss.
Convex set (covex set): in convex geometry, the convex set is a subset of the affine space that is closed under the convex combination. More specifically, in Euclidean space, a convex set is that for each pair of points within a set, each point on the straight line segment connecting the pair of points is also within the set. For example, a cube is a convex set, but any hollow or pitted, e.g., crescent, is not a convex set.
Merging: given two sets A, B, a set formed by merging all the elements of them together is called a union of the set A and the set B and is called as Au B, and is read as A and B.
Intersection set: let A, B be two sets, the set composed of all the elements belonging to set A and set B, called the intersection of set A and set B, and written as A # B, read as A intersection B.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
The activity classification model training method and the image classification method for the choroidal neovascularization can be applied to artificial intelligence. The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
Choroidal Neovascularization (CNV): CNV is a fibrovascular tissue that is proliferated under or over the retinal pigment epithelium, which can be caused by a variety of causes. For inactive CNVs, follow-up observation or conservative treatment is only needed, but for active CNVs, the leakage caused by the active CNVs seriously affects the vision, so that timely injection treatment is needed. At present, in clinical medicine, when judging the activity of choroidal neovascularization, contrast medium is injected into retina to obtain a corresponding contrast image, and then a doctor can judge the activity of the choroidal neovascularization by referring to the contrast image. However, the contrast agent has side effects, which easily cause allergic reaction or impaired renal function in human body, and increase the risk of human diseases; meanwhile, the judging mode has low efficiency and has larger requirements on the qualification of doctors.
Based on this, the embodiment of the application provides an activity classification model training method, a classification method, a device, equipment and a medium, so as to realize automatic classification of the activity of choroidal neovascularization, avoid side effects caused by using a contrast agent, reduce the cost for judging the activity of choroidal neovascularization, reduce the qualification requirement of a doctor for judging the activity of choroidal neovascularization, and accelerate the efficiency of identifying the activity of choroidal neovascularization.
Specifically, the following embodiments are provided to describe a method, a device, and an apparatus for training an activity classification model, which first describe the method for training an activity classification model in the embodiments of the present disclosure.
The embodiment of the application provides an activity classification model training method, and relates to the technical field of artificial intelligence. The activity classification model training method provided by the embodiment of the application can be applied to a terminal, a server side and software running in the terminal or the server side. In some embodiments, the terminal may be a smartphone, tablet, laptop, desktop computer, smart watch, or the like; the server side can be configured as an independent physical server, or a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as cloud service, a cloud database, cloud computing, cloud functions, cloud storage, Network service, cloud communication, middleware service, domain name service, security service, Content Delivery Network (CDN) and a big data and artificial intelligence platform; the software may be an application that implements an activity classification model training method, etc., but is not limited to the above form.
The disclosed embodiments are operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
Referring to fig. 1, a detailed description is given of a specific process of the activity classification model training method for choroidal neovascularization according to the present application with reference to fig. 1.
As shown in fig. 1, in a first aspect, some embodiments of the present application provide an activity classification model training method for choroidal neovascularization, comprising step S101, step S102, step S103, step S104, step S105 and step S106. These six steps are described in detail below, it being understood that the activity classification model training method for choroidal neovascularization includes, but is not limited to, these six steps.
Step S101: acquiring an image sample data set of choroidal neovascularization; the image sample data set comprises a plurality of image sample data.
Specifically, in step S101, the image sample data of the choroidal neovascularization may be an OCT scan image of the macular region of the examinee obtained by an OCT scanning device, or a choroidal neovascularization image directly downloaded from a medical cloud server, which is not limited in this application.
It should be noted that, whether the image sample data is an OCT scanned image obtained by an OCT scanning apparatus or a choroidal neovascularization image downloaded by a medical cloud server. The image sample data set should have been classified, and each image sample data set needs to be labeled with a label. Namely, the image sample data set is divided into two parts of active CNV and inactive CNV, and for the active CNV, the position of the leakage area needs to be marked by a rectangular marking box. The inactive CNV may be represented by a number "0", the active CNV may be represented by a number "1", or the active CNV and the inactive CNV may be distinguished by other labeling methods, and the present application is not limited thereto.
In order to facilitate the training process of the model in the subsequent steps, before the model training is performed, each image sample data in the image sample data set is generally preprocessed, and each image sample data is converted into a standard training image with the same resolution in width and height. The specific operation is as follows:
and performing edge filling processing on each image sample data by using a certain standard pixel, and then scaling to a certain preset resolution by using a bilinear difference algorithm to obtain a standard training image. For example: the pixel "0" is filled in the edge position of each image sample data, so that the resolution of each image sample data is 768 × 768, and then scaled to a resolution of 512 × 512 by the linear difference algorithm. It can also be scaled to other resolutions, such as 256 × 256, and the operation is similar, which is not described herein.
Step S102: inputting each image sample data of the image sample data set to a preset neural network model; wherein, the neural network model includes: a first neural network and a second neural network.
In step S102, the preset neural network model may be an efficientNetB0 neural network model, or may be another neural network model, which is not specifically limited in this application, and in this embodiment, the efficientNetB0 neural network model is taken as an example to describe in detail the activity classification model training method for choroidal neovascularization in this embodiment of the application.
Step S103: and performing feature extraction on the sample data of each image through a plurality of network layers of the first neural network to obtain a feature image.
Step S104: carrying out choroidal neovascularization activity classification processing on the characteristic images through a second neural network to obtain a first loss value of sample data of each image; wherein the first loss value is used to characterize the activity of choroidal neovascularization.
Step S105: performing attention enhancement processing on the characteristic image through a preset attention enhancement model to obtain a second loss value of each image sample data; wherein the second loss value is used to characterize the activity of choroidal neovascularization.
Step S106: training the neural network model according to the first loss value and the second loss value to obtain an activity classification model of choroidal neovascularization; wherein the activity classification model is used to classify choroidal neovascularization activity.
The activity classification model training method for the choroidal neovascularization comprises the steps of inputting sample data of each image into a first neural network, carrying out feature extraction on the sample data of each image through a plurality of network layers of the first neural network to obtain a corresponding feature image, and then carrying out choroidal neovascularization activity classification processing on the feature image according to a second neural network to obtain a first loss value representing choroidal neovascularization activity; introducing a seepage zone attention branch, and performing attention enhancement processing on the characteristic image by adopting a preset attention enhancement model to obtain a second loss value representing the activity of choroidal neovascularization; and training the neural network model according to the first loss value and the second loss value to obtain an activity classification model of the choroid neovasculature. According to the activity classification model of the choroidal neovascularization, the activity classification model for classifying the activity of the choroidal neovascularization is established, the automatic classification of the activity of the choroidal neovascularization is realized, the side effect of using a contrast medium is avoided, the cost for judging the activity of the choroidal neovascularization is reduced, the qualification requirement of a doctor for judging the activity of the choroidal neovascularization is reduced, and the attention branch of a leakage region is added, so that the attention enhancement model is used for carrying out the attention enhancement treatment on the characteristic image, and the activity of the choroidal neovascularization can be better judged by the trained activity classification model for the choroidal neovascularization.
Referring to fig. 2, in some embodiments of the present application, the plurality of network layers include a dimension reduction convolutional layer and at least one mobile rollover bottleneck convolutional layer, and step S103 includes, but is not limited to, step S201 and step S202. These two steps are described in detail below with reference to fig. 2.
Step S201: and performing dimension reduction convolution processing on each image sample data through the dimension reduction convolution layer to obtain a dimension reduction convolution image.
In step S201, after the image sample data is input into the first neural network, dimension reduction convolution processing is performed on the dimension reduction convolution kernel image sample data to obtain a dimension reduction convolution image. By carrying out dimension reduction convolution processing on the image sample data, irrelevant information can be reduced, and the subsequent training process can pay more attention to useful information.
Step S202: and carrying out moving and turning bottleneck convolution processing on the dimensionality reduction convolution image through the moving and turning bottleneck convolution layer to obtain the characteristic image.
In step S202, a moving and flipping bottleneck convolution layer is set to perform moving and flipping bottleneck convolution processing on the reduced-dimension convolution image, so that the random depth of the model can be increased, the time required by subsequent model training can be reduced, and the performance of the model can be improved.
In general, a plurality of moving and flipping bottleneck convolution layers with the same parameters are adopted to perform moving and flipping bottleneck convolution processing on the reduced-dimension convolution image.
Referring to fig. 3, in some embodiments of the present application, the second neural network includes a global averaging pooling layer, a fully connected layer, and a normalization classification layer, and step S104 includes, but is not limited to, step S301, step S302, and step S303. These three steps are described in detail below with reference to fig. 3.
Step S301: and performing pooling processing on the characteristic image through the global average pooling layer to obtain a pooling vector.
Step S302: and carrying out full-connection processing on the pooled vectors through a full-connection layer to obtain a primary classification value.
Step S303: and carrying out normalization processing on the preliminary classification value through a normalization classification layer to obtain a first loss value.
Specifically, in steps S301 to S303, the feature image obtained by extracting the first neural network feature is input to the global average pooling layer to obtain a pooled vector, the pooled vector passes through the full-link layer to obtain a preliminary classification value, the preliminary classification value is normalized to obtain a first loss value, and the first loss value represents the accuracy of the current neural network model for predicting the CNV image sample data. For example, the first loss value is 0.5, and the success rate of representing the accuracy of the current prediction is 50%. Or, setting a certain prediction threshold, and when the first loss value exceeds the prediction threshold, regarding the CNV image sample data input into the model as active CNV; otherwise, the CNV image sample data input into the model is inactive CNV. Or may be characterized as predicting the probability that CNV image sample data input into the model is an active CNV. If the first loss value is 0.98, the probability of characterizing the CNV image sample data input into the model as active CNV is 98%.
It should be noted that the softmax classification function may be adopted to perform normalization processing on the preliminary classification value, and some other classification functions may also be adopted to perform normalization processing, which is not specifically limited in this application.
Referring to fig. 4, in some embodiments of the present application, an actual frame is set in image sample data, and the actual frame is used to mark an actual location of a leakage area of the image sample data; step S105 includes, but is not limited to, step S401 and step S402.
Step S401: carrying out up-sampling on the characteristic image to obtain a prediction frame; the prediction frame is used for marking the position of the predicted leakage area of the image sample data.
Specifically, in step S401, the first neural network performs upsampling on the feature image obtained by extracting the image sample data feature to obtain a location of a leakage area, and then takes an external rectangular frame of the location of the leakage area to obtain a predicted frame.
Step S402: and performing loss calculation on the predicted frame and the actual frame to obtain a second loss value.
In step S402, the real frame representing the position of the real leakage area of the image sample data and the predicted frame representing the position of the predicted leakage area of the image sample data are subjected to GIOU loss processing to obtain a second loss value. The specific process of GIOU loss is as follows:
the predicted bounding box is denoted by A, the actual bounding box is denoted by B, and the minimum convex set of A, B is denoted by C, and the calculation process is as follows:
GIOU loss＝1-GIOU (3)
as can be seen from the calculation process of the GIOU loss, under the condition that the shapes of the predicted frame and the actual frame are similar, the closer the center distance between the predicted frame and the actual frame is, the smaller the GIOU loss is. And the second loss value can represent the accuracy of the current neural network model for predicting the CNV image sample data.
Referring to fig. 5, in some embodiments of the present application, step S106 includes, but is not limited to, step S501 and step S502.
Step S501: and weighting the first loss value and the second loss value according to a preset weight to obtain a target loss value.
Specifically, in step S501, a first loss value and a second loss value are weighted according to a preset weight, so as to obtain a target loss value. For example, the target loss value is 0.5 × first loss value +0.5 × second loss value. It is to be understood that the preset weight may be other weights, and the present application is not particularly limited thereto, and may be set according to practical situations. For example, the prediction success rate of the image sample data is set according to the neural network model and the attention-strengthening model.
Step S502: and training the neural network model according to the target loss value to obtain an activity classification model of the choroidal neovascularization.
In step S502, the neural network model fine-tunes parameters of the neural network model according to actual conditions of the target loss value until the target loss value meets a preset condition, for example, the target loss value is continuously smaller than a preset loss threshold.
Referring to fig. 6, fig. 6 is a schematic diagram illustrating a specific application scenario of the activity classification model training method for choroidal neovascularization according to some embodiments of the present application.
In fig. 6, the activity classification model training method for choroidal neovascularization in the embodiment of the present application is described in detail by using the efficientNetB0 network model as a preset neural network model. In fig. 6, CONV denotes a dimension reduction convolution layer, and MBCONV denotes a shift flip bottleneck convolution layer.
As shown in fig. 6, the image sample data is firstly subjected to dimensionality reduction convolution through a layer of 3 × 3 CONV layer to obtain a dimensionality reduction convolution image, and then is subjected to moving and turning bottleneck convolution through an MBCONV layer with different parameters to obtain a characteristic image. After the characteristic image is obtained, on one hand, the characteristic image is trained by continuously using an efficientNet B0 network model, dimension reduction convolution is carried out through a layer of 1-by-1 CONV layer, pooling processing is carried out through a global average pooling layer, full connection processing is carried out through a full connection layer, and a first loss value is obtained through a softmax function. On the other hand, the thermodynamic diagram of the characteristic image is up-sampled, then a rectangular frame is circumscribed to obtain a predicted frame, and a second loss value is obtained by calculating GIOS loss of the predicted frame and the actual frame. And after the first loss value and the second loss value are obtained, weighting the first loss value and the second loss value to obtain a target loss value, wherein the target loss value represents the accuracy of the neural network model and the attention enhancement model for predicting the image sample data. After the target loss value is obtained, the neural network model finely adjusts parameters according to the target loss value to obtain an activity classification model of choroid neovasculature.
Referring to fig. 7, in a second aspect, some embodiments of the present application further provide an image classification method including, but not limited to, step S701 and step S702. These two steps are described in detail below in conjunction with fig. 7.
Step S701: and acquiring an image to be detected.
Step S702: inputting the image to be detected into an activity classification model for classification treatment to obtain the activity class of choroidal neovascularization; wherein the activity classification model is trained according to the method of the embodiment of the first aspect.
In the embodiment, the classification of the choroidal neovascularization of the image to be detected can be realized by directly inputting the image to be detected into the activity classification model of the choroidal neovascularization.
According to the image classification model, the activity of the choroidal neovascularization is automatically classified by using the activity classification model of the choroidal neovascularization, the side effect of using a contrast medium is avoided, the cost of judging the activity of the choroidal neovascularization is reduced, the qualification requirement of a doctor for judging the activity of the choroidal neovascularization is reduced, and the attention of a leakage region is added, so that the attention of a characteristic image is strengthened by using the attention-strengthened model, and the activity of the choroidal neovascularization can be better judged by using the activity classification model of the choroidal neovascularization.
Referring to fig. 8, in a third aspect, some embodiments of the present application further provide an activity classification model training apparatus for choroidal neovascularization, including a sample acquisition module 801, an input module 802, a feature extraction module 803, a classification processing module 804, an enhancement processing module 805, and a training processing module 806.
The sample acquisition module 801: the method comprises the steps of obtaining an image sample data set for obtaining choroidal neovascularization; the image sample data set comprises a plurality of image sample data.
The input module 802: the neural network model is used for inputting each image sample data of the image sample data set to a preset neural network model; wherein the neural network model comprises a first neural network and a second neural network.
The feature extraction module 803: the method is used for extracting the features of each image sample data through a plurality of network layers of the first neural network to obtain a feature image.
The classification processing module 804: the second neural network is used for carrying out choroidal neovascularization activity classification processing on the characteristic images to obtain a first loss value of sample data of each image; wherein the first loss value is used to characterize the activity of choroidal neovascularization.
The reinforcement processing module 805: the attention enhancement module is used for performing attention enhancement processing on the characteristic image through a preset attention enhancement model to obtain a second loss value of each image sample data; wherein the second loss value is used to characterize the activity of choroidal neovascularization.
The training processing module 806: the neural network model is trained according to the first loss value and the second loss value to obtain an activity classification model of choroidal neovascularization; wherein the activity classification model is used to classify choroidal neovascularization activity.
The activity classification model training device for the choroidal neovascularization is characterized in that each image sample data is input into a first neural network, feature extraction is carried out on each image sample data through a plurality of network layers of the first neural network to obtain a corresponding feature image, and then choroidal neovascularization activity classification processing is carried out on the feature images according to a second neural network to obtain a first loss value representing choroidal neovascularization activity; introducing a seepage zone attention branch, and performing attention enhancement processing on the characteristic image by adopting a preset attention enhancement model to obtain a second loss value representing the activity of choroidal neovascularization; and training the neural network model according to the first loss value and the second loss value to obtain an activity classification model of the choroid neovasculature. The activity classification model of the choroidal neovascularization vessels, which is obtained by the scheme, realizes automatic classification of the activity of the choroidal neovascularization vessels, avoids the side effect of using a contrast medium, reduces the cost for judging the activity of the choroidal neovascularization vessels, reduces the qualification requirement of a doctor for judging the activity of the choroidal neovascularization vessels, and can better judge the activity of the choroidal neovascularization vessels by using the attention-enhanced model to perform attention-enhanced treatment on the characteristic image due to the addition of the attention branch of the effusion region.
It should be noted that the activity classification model training device for choroidal neovascularization according to the embodiment of the present application corresponds to the aforementioned activity classification model training method for choroidal neovascularization, and for a specific process, reference is made to the aforementioned activity classification model training method for choroidal neovascularization, which is not described herein again.
In a fourth aspect, some embodiments of the present application further provide an image classification apparatus, including an image acquisition module and an image classification module. The image acquisition module is used for acquiring an image to be detected; the image classification module is used for inputting the image to be detected into the activity classification model for classification processing to obtain the activity category of the choroid neovascularization; wherein the activity classification model is trained according to the method of the embodiment of the first aspect.
An embodiment of the present disclosure further provides an electronic device, including:
at least one memory;
at least one processor;
at least one program;
a program is stored in the memory and the processor executes at least one program to implement the present disclosure to implement the above-described activity classification model training method for choroidal neovascularization or image classification method. The electronic device may be any intelligent terminal including a mobile phone, a tablet computer, a Personal Digital Assistant (PDA), a vehicle-mounted computer, and the like.
The electronic device according to the embodiment of the present application will be described in detail with reference to fig. 9.
As shown in fig. 9, fig. 9 illustrates a hardware structure of an electronic device according to another embodiment, where the electronic device includes:
the processor 901 may be implemented by a general Central Processing Unit (CPU), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solution provided by the embodiment of the present disclosure;
the Memory 902 may be implemented in the form of a Read Only Memory (ROM), a static storage device, a dynamic storage device, or a Random Access Memory (RAM). The memory 902 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present disclosure is implemented by software or firmware, the relevant program codes are stored in the memory 902 and called by the processor 901 to execute the activity classification model training method or the image classification method for choroidal neovascularization according to the embodiments of the present disclosure;
an input/output interface 903 for implementing information input and output;
a communication interface 904, configured to implement communication interaction between the device and another device, where communication may be implemented in a wired manner (e.g., USB, network cable, etc.), or in a wireless manner (e.g., mobile network, WIFI, bluetooth, etc.); and
a bus 905 that transfers information between various components of the device (e.g., the processor 901, the memory 902, the input/output interface 903, and the communication interface 904);
wherein the processor 901, the memory 902, the input/output interface 903 and the communication interface 904 enable a communication connection within the device with each other through a bus 905.
Embodiments of the present disclosure also provide a storage medium which is a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the above-mentioned activity classification model training method for choroidal neovascularization or image classification method.
The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The embodiments described in the embodiments of the present disclosure are for more clearly illustrating the technical solutions of the embodiments of the present disclosure, and do not constitute a limitation to the technical solutions provided in the embodiments of the present disclosure, and it is obvious to those skilled in the art that the technical solutions provided in the embodiments of the present disclosure are also applicable to similar technical problems with the evolution of technology and the emergence of new application scenarios.
Those skilled in the art will appreciate that the solutions shown in the figures are not intended to limit embodiments of the present disclosure, and may include more or less steps than those shown, or some of the steps may be combined, or different steps.
The above-described embodiments of the apparatus are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may also be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
One of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
The terms "first," "second," "third," "fourth," and the like in the description of the application and the above-described figures, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes multiple instructions for enabling an electronic device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method of the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing programs, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The preferred embodiments of the present disclosure have been described above with reference to the accompanying drawings, and therefore do not limit the scope of the claims of the embodiments of the present disclosure. Any modifications, equivalents and improvements within the scope and spirit of the embodiments of the present disclosure should be considered within the scope of the claims of the embodiments of the present disclosure by those skilled in the art.
Claims (10)
1. An activity classification model training method for choroidal neovascularization, comprising:
acquiring an image sample data set of choroidal neovascularization; wherein the image sample data set comprises a plurality of image sample data;
inputting each image sample data of the image sample data set to a preset neural network model; wherein the neural network model comprises: a first neural network and a second neural network;
extracting the characteristics of each image sample data through a plurality of network layers of the first neural network to obtain a characteristic image;
performing choroidal neovascularization activity classification processing on the feature images through the second neural network to obtain a first loss value of sample data of each image; wherein the first loss value is used to characterize the activity of the choroidal neovascularization;
performing attention enhancement processing on the characteristic image through a preset attention enhancement model to obtain a second loss value of each image sample data; wherein the second loss value is used to characterize the activity of the choroidal neovascularization;
training the neural network model according to the first loss value and the second loss value to obtain an activity classification model of choroidal neovascularization; wherein the activity classification model is used to classify choroidal neovascularization activity.
2. The method of claim 1, wherein the number of network layers comprises: a dimension-reducing convolutional layer and at least one moving and turning bottleneck convolutional layer;
the extracting the features of each image sample data through a plurality of network layers of the first neural network to obtain a feature image comprises the following steps:
performing dimension reduction convolution processing on each image sample data through the dimension reduction convolution layer to obtain a dimension reduction convolution image;
and carrying out moving and overturning bottleneck convolution processing on the dimensionality reduction convolution image through the moving and overturning bottleneck convolution layer to obtain a characteristic image.
3. The method of claim 1, wherein the second neural network comprises: a global average pooling layer, a full-link layer and a normalization classification layer;
the obtaining a first loss value of each image sample data by performing choroidal neovascularization activity classification processing on the feature image through the second neural network comprises:
performing pooling processing on the characteristic image through the global average pooling layer to obtain a pooling vector;
performing full-connection processing on the pooled vectors through the full-connection layer to obtain a primary classification value;
and normalizing the preliminary classification value through the normalization classification layer to obtain a first loss value.
4. The method according to any one of claims 1 to 3, wherein an actual frame is provided in the image sample data, and the actual frame is used for marking the actual position of the leaking region of the image sample data;
the performing attention enhancement processing on the feature image through a preset attention enhancement model to obtain a second loss value of each image sample data includes:
performing upsampling on the characteristic image to obtain a predicted frame; the prediction frame is used for marking the position of a prediction leakage region of the image sample data;
and performing loss calculation on the predicted frame and the actual frame to obtain a second loss value.
5. The method according to any one of claims 1 to 3, wherein the training of the neural network model according to the first loss value and the second loss value to obtain an activity classification model of choroidal neovascularization comprises:
weighting the first loss value and the second loss value according to a preset weight to obtain a target loss value;
and training the neural network model according to the target loss value to obtain an activity classification model of choroidal neovascularization.
6. A method of image classification, the method comprising:
acquiring an image to be detected;
inputting the image to be detected into an activity classification model for classification treatment to obtain the activity class of choroidal neovascularization; wherein the activity classification model is trained according to the method of any one of claims 1 to 5.
7. An activity classification model training device for choroidal neovascularization, comprising:
the system comprises a sample acquisition module, a data acquisition module and a data acquisition module, wherein the sample acquisition module is used for acquiring an image sample data set of choroidal neovascularization; wherein the image sample data set comprises a plurality of image sample data;
the input module is used for inputting each image sample data of the image sample data set to a preset neural network model; wherein the neural network model comprises a first neural network and a second neural network;
the characteristic extraction module is used for extracting the characteristics of each image sample data through a plurality of network layers of the first neural network to obtain a characteristic image;
the classification processing module is used for carrying out choroidal neovascularization activity classification processing on the characteristic images through the second neural network to obtain a first loss value of each image sample data; wherein the first loss value is used to characterize the activity of the choroidal neovascularization;
the enhancement processing module is used for carrying out attention enhancement processing on the characteristic image through a preset attention enhancement model to obtain a second loss value of each image sample data; wherein the second loss value is used to characterize the activity of the choroidal neovascularization;
the training processing module is used for training the neural network model according to the first loss value and the second loss value to obtain an activity classification model of choroidal neovascularization; wherein the activity classification model is used to classify choroidal neovascularization activity.
8. An image classification apparatus, comprising:
the image acquisition module is used for acquiring an image to be detected;
the image classification module is used for inputting the image to be detected into an activity classification model for classification processing to obtain the activity category of the choroidal neovascularization; wherein the activity classification model is trained according to the method of any one of claims 1 to 5.
9. An electronic device, comprising:
at least one memory;
at least one processor;
at least one program;
the programs are stored in the memory, and the processor executes the at least one program to implement:
the method of any one of claims 1 to 5; alternatively, the first and second electrodes may be,
the method of claim 6.
10. A storage medium that is a computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform:
the method of any one of claims 1 to 5; alternatively, the first and second electrodes may be,
the method of claim 6.
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