WO2021189960A1 - Procédé et appareil pour entrainer un réseau antagoniste, procédé et appareil pour compléter des données médicales, dispositif et support - Google Patents

Procédé et appareil pour entrainer un réseau antagoniste, procédé et appareil pour compléter des données médicales, dispositif et support Download PDF

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WO2021189960A1
WO2021189960A1 PCT/CN2020/135342 CN2020135342W WO2021189960A1 WO 2021189960 A1 WO2021189960 A1 WO 2021189960A1 CN 2020135342 W CN2020135342 W CN 2020135342W WO 2021189960 A1 WO2021189960 A1 WO 2021189960A1
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data
loss value
sample
network
generated
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PCT/CN2020/135342
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Chinese (zh)
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李彦轩
朱昭苇
孙行智
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平安科技（深圳）有限公司
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting

Definitions

  • This application relates to the field of artificial intelligence technology, and in particular to a method, device, equipment, and medium for combating network training and medical data supplementation.
  • the embodiments of the present application provide a method, device, equipment, and medium for combating network training and medical data supplementation to solve the problem of lack of data and low model accuracy.
  • a countermeasure network training method including:
  • the initial confrontation network including a generator model containing initial parameters and a trained induction network model;
  • the initial parameters of the iterative generator model are updated, until the total loss value reaches the preset convergence condition, the initial confrontation after the convergence
  • the network is recorded as a confrontational network.
  • a countermeasure network training device including:
  • a confrontation network acquisition module for acquiring an initial confrontation network, the initial confrontation network including a generator model containing initial parameters and a trained induction network model;
  • a data generation module configured to input preset random noise into the initial countermeasure network, and generate generated data corresponding to the random noise through the generator model;
  • a loss value determining module configured to determine the total loss value of the generator model through the induction network model according to the generated data
  • the convergence judgment module is used to update the initial parameters of the generator model when the total loss value does not reach the preset convergence condition, and converge until the total loss value reaches the preset convergence condition
  • the subsequent initial confrontation network is recorded as a confrontation network.
  • a method for supplementing medical data including:
  • the full medical data set contains multiple samples of medical data and a first small sample of medical data; the first small sample of medical data is associated with a small sample label;
  • the second sample size is much smaller than the first sample size
  • the confrontation network completed through training generates a second small sample of medical data that is equal to the sample difference and is associated with the small sample label; wherein, the confrontation network is obtained according to the above-mentioned confrontation network training method;
  • the induction network model is obtained by training according to the medical full data set;
  • a computer device includes a memory, a processor, and computer-readable instructions that are stored in the memory and can run on the processor, and the processor implements the following steps when the processor executes the computer-readable instructions:
  • the initial confrontation network including a generator model containing initial parameters and a trained induction network model;
  • the initial parameters of the iterative generator model are updated, until the total loss value reaches the preset convergence condition, the initial confrontation after the convergence
  • the network is recorded as a confrontational network.
  • a computer device includes a memory, a processor, and computer-readable instructions that are stored in the memory and can run on the processor, and the processor implements the following steps when the processor executes the computer-readable instructions:
  • the full medical data set contains multiple samples of medical data and a first small sample of medical data; the first small sample of medical data is associated with a small sample label;
  • the confrontation network completed through training generates a second small sample of medical data that is equal to the sample difference and is associated with the small sample label; wherein, the confrontation network is obtained according to the above-mentioned confrontation network training method;
  • the induction network model is obtained by training according to the medical full data set;
  • One or more readable storage media storing computer readable instructions, when the computer readable instructions are executed by one or more processors, the one or more processors execute the following steps:
  • the initial confrontation network including a generator model containing initial parameters and a trained induction network model;
  • the initial parameters of the iterative generator model are updated, until the total loss value reaches the preset convergence condition, the initial confrontation after the convergence
  • the network is recorded as a confrontational network.
  • One or more readable storage media storing computer readable instructions, when the computer readable instructions are executed by one or more processors, the one or more processors execute the following steps:
  • the full medical data set contains multiple samples of medical data and a first small sample of medical data; the first small sample of medical data is associated with a small sample label;
  • the confrontation network completed through training generates a second small sample of medical data that is equal to the sample difference and is associated with the small sample label; wherein, the confrontation network is obtained according to the above-mentioned confrontation network training method;
  • the induction network model is obtained by training according to the medical full data set;
  • the above-mentioned countermeasure network training and medical data supplement methods, devices, equipment and media are obtained by obtaining an initial countermeasure network, which includes a generator model containing initial parameters and a trained induction network model; preset random noise is input to The initial confrontation network generates generated data corresponding to the random noise through the generator model; determines the total loss value of the generator model through the induction network model according to the generated data; When the loss value does not reach the preset convergence condition, the initial parameters of the iterative generator model are updated, until the total loss value reaches the preset convergence condition, the initial confrontation network after convergence is recorded as a confrontation The internet.
  • This application improves the structure of the GAN network in the prior art and uses an induction network model to replace the discriminator model, so that the trained adversarial network can judge whether the generated data conforms to the distribution of the full data set and the distribution of the sub-data sets of each category.
  • This application The function of the confrontation network is expanded, and the accuracy of the data generated in the confrontation network is improved; and the trained confrontation network can be applied to the supplementation of small sample data in different scenarios, so that the completed model can be trained through the supplemented small sample data.
  • the accuracy rate is higher, which provides convenience for intelligent research in various scenarios.
  • FIG. 1 is a schematic diagram of an application environment of the confrontation network training method and the medical data supplement method in an embodiment of the present application;
  • Fig. 2 is a flowchart of a countermeasure network training method in an embodiment of the present application
  • FIG. 3 is a flowchart of step S30 in the countermeasure network training method in an embodiment of the present application.
  • FIG. 4 is a flowchart of step S301 in the countermeasure network training method in an embodiment of the present application.
  • FIG. 5 is a flowchart of step S302 in the countermeasure network training method in an embodiment of the present application
  • FIG. 6 is a flowchart of a method for supplementing medical data in an embodiment of the present application.
  • Fig. 7 is a functional block diagram of a countermeasure network training device in an embodiment of the present application.
  • FIG. 8 is a functional block diagram of the loss value determining module in the countermeasure network training device in an embodiment of the present application.
  • FIG. 9 is a functional block diagram of the first loss value determining unit in the adversarial network training device in an embodiment of the present application.
  • FIG. 10 is a functional block diagram of the second loss value determining unit in the adversarial network training device in an embodiment of the present application.
  • FIG. 11 is a functional block diagram of a medical data supplement device in an embodiment of the present application.
  • Fig. 12 is a schematic diagram of a computer device in an embodiment of the present application.
  • the confrontation network training method provided by the embodiment of the present application can be applied to the application environment shown in FIG. 1.
  • the confrontation network training method is applied in a confrontation network training system.
  • the confrontation network training system includes a client and a server as shown in FIG.
  • the client is also called the client, which refers to the program that corresponds to the server and provides local services to the client.
  • the client can be installed on, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices.
  • the server can be implemented as an independent server or a server cluster composed of multiple servers.
  • a method for training a confrontation network is provided.
  • the method is applied to the server in FIG. 1 as an example for description, including the following steps:
  • S10 Obtain an initial confrontation network, where the initial confrontation network includes a generator model containing initial parameters and a trained induction network model.
  • the initial confrontation network is improved based on the GAN (Generative Adversarial Networks) network in the prior art.
  • the initial confrontation network retains the generator containing the initial parameters in the original GAN network, and the discrimination in the original GAN network Replace the sensor with the trained induction network model.
  • the induction network model includes the following three modules: encoder module, induction module and correlation module. The induction network model is obtained through training on the full data set.
  • step S10 before step S10, that is, before obtaining the initial confrontation network, the method further includes:
  • S11 Obtain a full data set.
  • the full data set includes several sub-data sets corresponding to several categories; one sub-data set is associated with one sub-data label.
  • the full data set can be a data set in any scenario.
  • the full data set can be all application data; it can be a full data set in the medical field; the full data set includes several sub-data sets corresponding to several categories.
  • the full data set is application data
  • the corresponding sub-data set can be classified according to specific applications (such as NetEase Cloud Music, Tencent Video, etc.), or according to different types (such as music, video, and games). ) Applications are classified, and a sub-data set is associated with a sub-data label (for example, a music application corresponds to a music label).
  • S12 Input each of the sub-data sets into the inductive network model, and perform encoding conversion on each of the sub-data sets through the encoder module in the inductive network model to obtain the corresponding data of each sub-data set. Sub data vector.
  • the encoder module is used to convert the data in each sub-data set into a low-dimensional embedding vector, which facilitates the identification and calculation of the subsequent steps.
  • each sub-data set of the full data set into the induction network model, and encode and convert the data in each sub-data set through the encoder module in the induction network model, so that the data It is transformed into the corresponding low-dimensional embedding vector, that is, the sub-data vector corresponding to each data in each sub-data set.
  • the induction network model uses the principle of dynamic routing to convert the sub-data vector corresponding to each sub-data set into its corresponding representation.
  • all sub-data vectors under each category need to be expressed as unified features, that is, all sub-data vectors in each sub-data set are converted into corresponding category vectors.
  • the correlation module is a module that provides correlation calculation methods.
  • the correlation module in the induction network model is used to iteratively determine each category. After iterating over the category vectors of the same category, iteratively determine the correlation between the category vectors of different categories, and then determine the correlation function corresponding to each category vector.
  • the same-dimensional transformation refers to the transformation of various correlation functions into the same-dimensional relationship.
  • each correlation function is converted in the same dimension to determine each sub-data set and its corresponding sub-data set.
  • the preset relational expression standard can be that when the relational coefficient between the sub-data set and the sub-data label in the relational expression changes little or no longer changes, the relational expression is determined to be the final relational expression, and then in all the relations After the equations are determined, the training of the representation induction network model is completed.
  • the trained induction network model can learn the distribution of the full data set, after the new data is input to the induction network model, the new data can be classified into the category closest to its distribution, that is The induction network model can determine whether the new data conforms to the distribution of the full data set, and it can also determine whether the new data conforms to the distribution of any category of sub-data set.
  • S20 Input preset random noise to the initial countermeasure network, and generate generated data corresponding to the preset random noise through the generator model.
  • the preset random noise can be generated by a random algorithm. Further, after the random algorithm generates the preset random noise, the preset random noise is received through the generator model, and generated data corresponding to the preset random noise is generated.
  • S30 Determine the total loss value of the generator model through the induction network model according to the generated data.
  • step S30 includes the following steps:
  • S301 Output a first loss value between the generated data and the small sample data through the induction network model.
  • the small sample data refers to the data corresponding to the category with a small sample size in the full data set.
  • the full data set is an application data set.
  • this full data set there are less data for book category applications.
  • the data corresponding to the book category can be called small sample data.
  • the first loss value is obtained by logarithmic calculation based on the matching degree between the generated data and the small sample data.
  • step S301 includes the following steps:
  • S3011 Obtain a generated label corresponding to the generated data and a sample label corresponding to the small sample data.
  • the generated tag represents the category of the generated data.
  • the sample label represents the category of the small sample data.
  • S3012 Determine a first relational expression corresponding to the small sample data in the induction network model according to the small sample data and the sample label.
  • the induction network model when the induction network model is trained through the full data set, for the trained induction network model, it learns the distribution of the full data set and at the same time identifies the distribution of the sub-data set corresponding to each category, that is, The first relationship between the small sample data and the sample label has been determined in the induction network model.
  • S3013 Determine the first loss value according to the generated data, the generated label, and the first relational expression.
  • the relationship between the generated data obtained according to the preset random noise and the corresponding generated tag for the first time is quite different from the first relationship.
  • the network model outputs a first loss value corresponding to the generator model, and the first loss value is determined according to the generated data, the generated label, and the first relational expression.
  • S302 Output a second loss value between the generated data and the full data set through the induction network model.
  • the second loss value is obtained by logarithmic calculation according to the matching degree between the generated data and the full data set.
  • step S302 includes the following steps:
  • S3022 Determine a second relational expression corresponding to the full data set in the induction network model according to the full data set and the full data set label.
  • the induction network model is trained through the full data set, for the trained induction network model, it has learned the distribution of the full data set, so the full data set and the full data have been determined in the induction network model The second relationship between tags.
  • S3023 Determine the second loss value according to the generated data, the generated label, and the second relational expression.
  • the generator model in order to make the data generated by the generator model conform to the distribution of the sub-data set of the corresponding category, it also conforms to the distribution of the full data set, so that the generated data can be supplemented to the full data set without destroying the full data Therefore, it is necessary to determine the second loss value of the generator model according to the generated data, the generated label, and the second relational expression.
  • S303 Determine the total loss value of the generator model through the induction network model according to the first loss value and the second loss value.
  • the total loss value of the generator model can be determined by the following loss function:
  • LOSS G is the total loss value
  • log (similarity part ) is the first loss value
  • log(similarity all ) is the second loss value; ⁇ is the weight corresponding to the second loss value; ⁇ is the weight corresponding to the first loss value.
  • the similarity part is the degree of matching between the generated data and the small sample data, that is, the judgment of whether the generated data conforms to the distribution of the small sample data; the similarity all is the difference between the generated data and the full data set The degree of matching between the two, that is, the judgment of whether the generated data conforms to the distribution of the full data set.
  • the convergence condition can be the condition that the total loss value is less than the set threshold, that is, when the total loss value is less than the set threshold, stop training; the convergence condition can also be that the total loss value is calculated after 10,000 times The condition that it is small and will not decrease, that is, when the total loss value is small and does not decrease after 10,000 calculations, stop training and record the initial confrontation network after convergence as a confrontation network.
  • the initial parameters of the generator model are adjusted according to the total loss value output by the induction network model, so that the generator model outputs
  • the generated data can continue to move closer to the full data set distribution and small sample data distribution, so that the matching degree between the generated data and the small sample data, and the matching degree between the generated data and the full data set are getting higher and higher, until the generator When the total loss value of the model reaches the preset convergence condition, the initial confrontation network after convergence is recorded as the confrontation network.
  • the induction network model is used instead of the discriminator model, so that the trained adversarial network can determine whether the generated data conforms to the distribution of the full data set and the distribution of the sub-data sets of each category. It is judged that this application expands the function of the confrontation network and improves the accuracy of the data generated in the confrontation network; and the trained confrontation network can be applied to small sample data supplementation in different scenarios, so that the supplemented small sample data can be used for training
  • the completed model has a higher accuracy rate, which provides convenience for intelligent research in various scenarios, so as to promote the construction of smart cities.
  • the full data set and the confrontation network may be stored in the blockchain.
  • the Blockchain is an encrypted and chained transaction storage structure formed by blocks.
  • the header of each block can not only include the hash value of all transactions in the block, but also the hash value of all transactions in the previous block, so as to achieve tamper-proof transactions in the block based on the hash value And anti-counterfeiting; newly generated transactions are filled in the block and after the consensus of the nodes in the block chain network, they will be appended to the end of the block chain to form chain growth.
  • a method for supplementing medical data which includes the following steps:
  • S50 Receive a data supplement instruction including a full medical data set; the full medical data set includes multi-sample medical data and a first small-sample medical data; the first small-sample medical data is associated with a small-sample label.
  • the medical full data set is a collection containing all medical data in a specific scenario (such as a specific hospital or a specific department).
  • Multi-sample medical data refers to data corresponding to a category with a larger sample size in the full data set.
  • Small-sample medical data refers to the data corresponding to the categories with a small sample size in the full data set.
  • S60 Acquire a first sample size of the multi-sample medical data and a second sample size of the first small sample medical data; the second sample size is smaller than the first sample size.
  • the medical full data set contains a total of 100,000 sets of data.
  • the number of data corresponding to the multi-sample medical data is tens of thousands, and the number of data corresponding to the small-sample medical data may be hundreds, that is, multi-sample medical treatment.
  • Data One type of medical data may have 50,000 sets of data, while there are only a few hundred sets of data in a small sample of medical data.
  • medical models such as triage models cannot obtain enough feature information from categories with small sample sizes, resulting in failure to identify small-sample medical data categories. The data is correctly classified.
  • S70 Record the difference between the first sample quantity and the second sample quantity as a sample difference.
  • the confrontation network generated through training generates second small-sample medical data that is equal to the sample difference and is associated with the small-sample label; wherein, the confrontation network is based on the confrontation network training method in the foregoing embodiment Obtained;
  • the induction network model is obtained by training according to the medical full data set.
  • the confrontation network is obtained according to the training method of the confrontation network in the above embodiment, and the full data set in the confrontation network is the medical full data set, that is, the induction network model in the confrontation network is obtained by training based on the medical full data set. of.
  • the generator model in the confrontation network completed through training, according to the random noise signal obtained by the random algorithm, generates the second small sample of medical data with the number and the sample difference and is associated with the small sample label, and then generates The second small sample of medical data is supplemented to the medical full data set, so that the number of multi-sample medical data is balanced with the sum of the first small sample of medical data and the second small sample of medical data, and the second small sample of medical data is generated After being supplemented to the full medical data set, the distribution of the full medical data set will not be destroyed. Therefore, when training related models of medical scenarios such as triage model based on the supplemented full data set, the model can overcome samples in certain categories The problem of too little data, and the small sample medical data in the medical full data can also achieve a higher model classification accuracy rate.
  • a confrontation network training device is provided, and the confrontation network training device corresponds to the confrontation network training method in the foregoing embodiment in a one-to-one correspondence.
  • the confrontation network training device includes a confrontation network acquisition module 10, a data generation module 20, a loss value determination module 30 and a convergence judgment module 40.
  • the detailed description of each functional module is as follows:
  • the confrontation network acquisition module 10 is configured to acquire an initial confrontation network, the initial confrontation network including a generator model containing initial parameters and a trained induction network model;
  • the data generation module 20 is configured to input preset random noise into the initial countermeasure network, and generate generated data corresponding to the random noise through the generator model;
  • the loss value determination module 30 is configured to determine the total loss value of the generator model through the induction network model according to the generated data
  • the convergence judgment module 40 is configured to update the initial parameters of the generator model iteratively when the total loss value does not reach the preset convergence condition, until the total loss value reaches the preset convergence condition, The initial confrontation network after convergence is recorded as a confrontation network.
  • the confrontation network training device further includes the following modules:
  • the full data set acquisition module 11 is configured to acquire a full data set, the full data set includes several sub-data sets corresponding to classifications; one sub-data set is associated with one sub-data label;
  • the code conversion module 12 is configured to input each of the sub-data sets into the induction network model, and perform code conversion on each of the sub-data sets through the encoder module in the induction network model to obtain The sub-data vector corresponding to the sub-data set;
  • the vector conversion module 13 is configured to convert each of the sub-data vectors into a category vector corresponding to each sub-data vector through the induction module in the induction network model;
  • the correlation calculation module 14 is used to determine the correlation function corresponding to each of the category vectors through the correlation module in the induction network model;
  • the dimension conversion module 15 is configured to perform the same-dimensional conversion of each correlation function, and determine the relationship between each sub-data set and the corresponding sub-data label;
  • the standard determination module 16 is configured to indicate that the training of the induction network model is completed after each of the relational expressions reaches a preset relational expression standard.
  • the loss value determining module 30 includes the following units:
  • the first loss value determining unit 301 is configured to output the first loss value between the generated data and the small sample data through the induction network model;
  • the second loss value determining unit 302 is configured to output a second loss value between the generated data and the full data set through the induction network model;
  • the total loss value determining unit 303 is configured to determine the total loss value of the generator model through the induction network model according to the first loss value and the second loss value.
  • the first loss value determining unit 301 includes the following subunits:
  • the first label obtaining subunit 3011 is configured to obtain a generated label corresponding to the generated data and a sample label corresponding to the small sample data.
  • the first relational expression determining subunit 3012 is configured to determine a first relational expression corresponding to the small sample data in the induction network model according to the small sample data and the sample label.
  • the first loss value determining subunit 3013 is configured to determine the first loss value according to the generated data, the generated label, and the first relational expression.
  • the second loss value determining unit 302 includes the following subunits:
  • the second label obtaining subunit 3021 is configured to obtain the full data label corresponding to the full data set
  • the second relational expression determining subunit 3022 is configured to determine a second relational expression corresponding to the full data set in the induction network model according to the full data set and the full data set label;
  • the second loss value determining subunit 3023 is configured to determine the second loss value according to the generated data, the generated label, and the second relational expression.
  • the total loss value determining unit 303 is further configured to determine the total loss value of the generator model by using the following loss function:
  • LOSS G is the total loss value
  • log (similarity part ) is the first loss value
  • log(similarity all ) is the second loss value; ⁇ is the weight corresponding to the second loss value; ⁇ is the weight corresponding to the first loss value.
  • the various modules in the above-mentioned confrontation network training device can be implemented in whole or in part by software, hardware and a combination thereof.
  • the above-mentioned modules may be embedded in the form of hardware or independent of the processor in the computer equipment, or may be stored in the memory of the computer equipment in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.
  • a medical data supplement device is provided, and the medical data supplement device corresponds to the medical data supplement method in the above-mentioned embodiment in a one-to-one correspondence.
  • the medical data supplement device includes a supplement instruction receiving module 50, a sample quantity acquisition module 60, a sample difference recording module 70, a data generation module 80 and a data supplement module 90.
  • the detailed description of each functional module is as follows:
  • the supplementary instruction receiving module 50 is configured to receive a data supplementary instruction containing a full medical data set; the full medical data set contains multi-sample medical data and a first small-sample medical data; the first small-sample medical data and a small-sample label Associated
  • the sample quantity acquisition module 60 is configured to acquire the first sample quantity of the multi-sample medical data and the second sample quantity of the first small-sample medical data; the second sample quantity is smaller than the first sample quantity ;
  • the sample difference recording module 70 is configured to record the difference between the first sample quantity and the second sample quantity as a sample difference
  • the data generation module 80 is configured to generate the second small sample medical data whose quantity is equal to the sample difference and is associated with the small sample label through the countermeasure network completed through training; wherein, the countermeasure network is according to the above-mentioned embodiment Obtained by a confrontation network training method; the induction network model is obtained by training according to the medical full data set;
  • the data supplement module 90 is configured to supplement the generated second small sample medical data to the medical full data set.
  • a computer device is provided.
  • the computer device may be a server, and its internal structure diagram may be as shown in FIG. 12.
  • the computer equipment includes a processor, a memory, a network interface, and a database connected through a system bus.
  • the processor of the computer device is used to provide calculation and control capabilities.
  • the memory of the computer device includes a readable storage medium and an internal memory.
  • the readable storage medium stores an operating system, computer readable instructions, and a database.
  • the internal memory provides an environment for the operation of the operating system and computer readable instructions in the readable storage medium.
  • the database of the computer device is used to store the data used in the countermeasure network training method or the medical data supplement method in the foregoing embodiment.
  • the network interface of the computer device is used to communicate with an external terminal through a network connection.
  • the computer-readable instruction is executed by the processor to implement a method for training against a network, or the computer-readable instruction is executed by the processor to implement a method for supplementing medical data.
  • the readable storage medium provided in this embodiment includes a non-volatile readable storage medium and a volatile readable storage medium.
  • a computer device including a memory, a processor, and computer readable instructions stored in the memory and capable of running on the processor, and the processor implements the following steps when the processor executes the computer readable instructions:
  • the initial confrontation network including a generator model containing initial parameters and a trained induction network model;
  • the initial parameters of the iterative generator model are updated, until the total loss value reaches the preset convergence condition, the initial confrontation after the convergence
  • the network is recorded as a confrontational network.
  • a computer device including a memory, a processor, and computer readable instructions stored in the memory and capable of running on the processor, and the processor implements the following steps when the processor executes the computer readable instructions:
  • the full medical data set contains multiple samples of medical data and a first small sample of medical data; the first small sample of medical data is associated with a small sample label;
  • the confrontation network completed through training generates a second small sample of medical data that is equal to the sample difference and is associated with the small sample label; wherein, the confrontation network is obtained according to the above-mentioned confrontation network training method;
  • the induction network model is obtained by training according to the medical full data set;
  • one or more readable storage media storing computer readable instructions are provided.
  • the readable storage media provided in this embodiment include non-volatile readable storage media and volatile readable storage. Medium; the readable storage medium stores computer readable instructions, and when the computer readable instructions are executed by one or more processors, the one or more processors implement the following steps:
  • the initial confrontation network including a generator model containing initial parameters and a trained induction network model;
  • the initial parameters of the iterative generator model are updated, until the total loss value reaches the preset convergence condition, the initial confrontation after the convergence
  • the network is recorded as a confrontational network.
  • one or more readable storage media storing computer readable instructions are provided.
  • the readable storage media provided in this embodiment include non-volatile readable storage media and volatile readable storage. Medium; the readable storage medium stores computer readable instructions, and when the computer readable instructions are executed by one or more processors, the one or more processors implement the following steps:
  • the full medical data set contains multiple samples of medical data and a first small sample of medical data; the first small sample of medical data is associated with a small sample label;
  • the confrontation network completed through training generates a second small sample of medical data that is equal to the sample difference and is associated with the small sample label; wherein, the confrontation network is obtained according to the above-mentioned confrontation network training method;
  • the induction network model is obtained by training according to the medical full data set;
  • Non-volatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.
  • Volatile memory may include random access memory (RAM) or external cache memory.
  • RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Channel (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

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Abstract

La présente invention concerne le domaine technique de l'intelligence artificielle et est appliquée au domaine du traitement médical intelligent. L'invention concerne un procédé et un appareil pour entraîner un réseau antagoniste, un procédé et un appareil pour compléter des données médicales, ainsi qu'un dispositif et un support d'informations. Le procédé d'entraînement d'un réseau antagoniste comprend les étapes consistant à: acquérir un réseau antagoniste initial, le réseau antagoniste initial comprenant un modèle de générateur qui comprend un paramètre initial, et un modèle de réseau de détection entraîné; à introduire un bruit aléatoire prédéfini dans le réseau antagoniste initial, et à générer des données générées correspondant au bruit aléatoire au moyen du modèle de générateur; déterminer, en fonction des données générées, une valeur de perte totale du modèle de générateur au moyen du modèle de réseau de détection; et lorsque la valeur de perte totale n'atteint pas une condition de convergence prédéfinie, mettre à jour et itérer le paramètre initial du modèle de générateur jusqu'à ce que la valeur de perte totale atteigne la condition de convergence prédéfinie, et enregistrer le réseau antagoniste initial après convergence en tant que réseau antagoniste. Selon la présente invention, en améliorant un GAN, La fonction d'un réseau antagoniste obtenu au moyen d'un apprentissage est étendue, et la précision de génération de données du réseau antagoniste est améliorée.
PCT/CN2020/135342 2020-10-22 2020-12-10 Procédé et appareil pour entrainer un réseau antagoniste, procédé et appareil pour compléter des données médicales, dispositif et support WO2021189960A1 (fr)

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CN117933250A (zh) * 2024-03-22 2024-04-26 南京泛美利机器人科技有限公司 一种基于改进生成对抗网络的新菜谱生成方法

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