WO2021051497A1 - Procédé et appareil de détermination de tuberculose pulmonaire, dispositif informatique et support de stockage - Google Patents

Procédé et appareil de détermination de tuberculose pulmonaire, dispositif informatique et support de stockage Download PDF

Info

Publication number
WO2021051497A1
WO2021051497A1 PCT/CN2019/115946 CN2019115946W WO2021051497A1 WO 2021051497 A1 WO2021051497 A1 WO 2021051497A1 CN 2019115946 W CN2019115946 W CN 2019115946W WO 2021051497 A1 WO2021051497 A1 WO 2021051497A1
Authority
WO
WIPO (PCT)
Prior art keywords
training
network
resnet
target image
image sample
Prior art date
Application number
PCT/CN2019/115946
Other languages
English (en)
Chinese (zh)
Inventor
任嘉祥
王健宗
Original Assignee
平安科技（深圳）有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 平安科技（深圳）有限公司 filed Critical 平安科技（深圳）有限公司
Publication of WO2021051497A1 publication Critical patent/WO2021051497A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • 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
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • This application relates to the field of artificial intelligence technology, and in particular to a method, device, computer equipment and storage medium for determining tuberculosis.
  • Tuberculosis is a disease that affects many people and requires accurate diagnosis before it can be treated.
  • hospitals usually have X-ray machines, but some related staff lack radiology expertise to accurately evaluate images, resulting in poor diagnosis; some related staff can manually check X-rays, but the task is time-consuming and screening costs Larger.
  • the embodiments of the present application provide a tuberculosis determination method, device, computer equipment, and storage medium to solve the current problem that the rapid determination of tuberculosis cannot be achieved under the premise of ensuring a high accuracy rate.
  • an embodiment of the present application provides a method for determining tuberculosis, including:
  • the target image to be classified is input into the tuberculosis classification model to obtain a prediction probability.
  • the prediction probability is the probability that the tuberculosis is predicted to be positive. When the prediction probability is greater than a preset threshold, it is determined to be the same as the target to be classified.
  • the training sample set includes a target image sample for model training and a target image sample for model testing, the target image sample for model training and a target image sample for model testing There is no same target image sample between them;
  • an apparatus for determining tuberculosis including:
  • the first acquisition module is used to acquire a chest X-ray image to be classified
  • the second acquisition module is configured to convert the chest X-ray image to be classified into a target image to be classified according to preset image processing steps, wherein the resolution and dimension of the target image to be classified are the same as the training tuberculosis classification model The target image samples are the same;
  • the judging module is used to input the target image to be classified into the tuberculosis classification model to obtain the predicted probability.
  • the predicted probability is the probability that the predicted tuberculosis is positive.
  • the Tuberculosis is present in the chest X-ray image to be classified corresponding to the target image to be classified, wherein the tuberculosis classification model is obtained through a construction module, an initialization module, a training module, an update module, and a third acquisition module:
  • the construction module is used to construct a training sample set, wherein the training sample set includes a target image sample used for model training and a target image sample used for model testing, and the target image sample used for model training is used for model training. There is no identical target image sample among the tested target image samples;
  • the initialization module is used to use the ResNet-50 network as a deep neural network for training, and use the weights obtained by pre-training as the initial weights of the ResNet-50 network;
  • a training module configured to input the target image samples used for model training into the ResNet-50 network for training, and the output result is the predicted probability of pulmonary tuberculosis positive;
  • An update module configured to update the weight of the ResNet-50 network according to the predicted probability, and stop training until the updated change amount is less than a first preset threshold to obtain the model to be tested;
  • the third acquisition module is configured to use the target image sample for model testing to test the model to be tested, and when the accuracy of the test result output by the model to be tested is greater than the preset accuracy, the The model is used as the pulmonary tuberculosis classification model.
  • a computer device in a third aspect, 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 foregoing when the computer-readable instructions are executed. The steps of the tuberculosis determination method.
  • an embodiment of the present application provides a computer non-volatile readable storage medium, including: computer readable instructions, which when executed by a processor, implement the steps of the above method for determining tuberculosis.
  • the tuberculosis classification model is used to realize the tuberculosis determination of the chest X-ray image to be classified.
  • the tuberculosis classification model uses the ResNet-50 network as the deep neural network for training, so that the trained tuberculosis classification model has strong feature extraction capabilities and high classification accuracy; in addition, the training tuberculosis classification model also uses migration learning Method, the weights obtained by pre-training are used as the initial weights of the ResNet-50 network, which can speed up model training and improve the accuracy of model classification.
  • the embodiment of the application uses the tuberculosis classification model trained for tuberculosis determination.
  • the tuberculosis determination can be achieved according to the predicted probability output by the tuberculosis classification model. , Under the premise of ensuring high accuracy, the rapid determination of tuberculosis can be realized.
  • Fig. 1 is a flowchart of a method for determining tuberculosis in an embodiment of the present application
  • Figure 2 is a schematic diagram of a tuberculosis determination device in an embodiment of the present application
  • Fig. 3 is a schematic diagram of a computer device in an embodiment of the present application.
  • first, second, third, etc. may be used in the embodiments of the present application to describe the preset range, etc., these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from each other.
  • the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.
  • the word “if” as used herein can be interpreted as “when” or “when” or “in response to determination” or “in response to detection”.
  • the phrase “if determined” or “if detected (statement or event)” can be interpreted as “when determined” or “in response to determination” or “when detected (statement or event) )” or “in response to detection (statement or event)”.
  • Fig. 1 shows a flow chart of the method for determining tuberculosis in this embodiment.
  • the tuberculosis determination method can be applied to the tuberculosis determination system, and the tuberculosis determination system can be used to determine the tuberculosis of the chest X-ray image.
  • the pulmonary tuberculosis determination system can be specifically applied to a computer device, where the computer device is a device that can perform human-computer interaction with a user, including but not limited to devices such as computers, smart phones, and tablets.
  • the method for determining tuberculosis includes the following steps:
  • the chest X-ray image that the user wants to determine for tuberculosis is the chest X-ray image to be classified.
  • S2 Convert the chest X-ray image to be classified into the target image to be classified according to the preset image processing steps, where the resolution and dimension of the target image to be classified are the same as the target image sample for training the tuberculosis classification model.
  • the directly obtained chest X-ray image to be classified needs to be converted into the target image to be classified before it can be input into the tuberculosis determination model for determination, so that its resolution and dimension are the same as the target image for training the tuberculosis classification model.
  • the samples are the same, thereby improving the accuracy of tuberculosis determination.
  • the resolution and dimension of the target image samples used for training the tuberculosis classification model are different from those of chest X-ray images.
  • the ability to extract features of the tuberculosis determination model can be improved, thereby improving the accuracy of tuberculosis determination. rate.
  • the preset image processing is the processing of the image samples of the chest X-ray film when constructing the training sample set in the following steps S11-S15.
  • S3 Input the target image to be classified into the tuberculosis classification model to obtain the predicted probability.
  • the predicted probability is the probability that the predicted tuberculosis is positive.
  • the predicted probability is greater than the preset threshold, determine the chest X-ray to be classified corresponding to the target image to be classified
  • the film image has tuberculosis.
  • the chest X-ray image to be classified can be classified according to whether there is tuberculosis, and the determination of tuberculosis is also completed during the classification.
  • the preset threshold can be specifically set to 0.5, and the accuracy of tuberculosis determination under the preset threshold is relatively high.
  • the model training steps adopted by the tuberculosis classification model include:
  • S10 Construct a training sample set, where the training sample set includes a target image sample used for model training and a target image sample used for model testing, between the target image sample used for model training and the target image sample used for model testing There are no identical target image samples.
  • the absence of the same target image sample between the target image sample used for model training and the target image sample used for model testing can improve the generalization ability of the tuberculosis classification model, and can deal with more tuberculosis judgments in different scenarios.
  • the step of constructing the training sample set specifically includes:
  • S12 Process the image sample into an image sample with a preset resolution, where, for an image sample with a resolution higher than the preset resolution, the down-sampling method is used to down-sample the resolution of the image sample to the preset resolution. For image samples whose resolution is lower than the preset resolution, use bilinear interpolation to upsample the resolution of the image samples to the preset resolution;
  • the preset resolution may be 512*512.
  • the tuberculosis classification model trained at this resolution has a faster calculation speed and a higher classification accuracy.
  • the resolution of the directly acquired image samples of the chest X-ray film may be too high or too low.
  • the image samples may be processed to a resolution that is beneficial for model training to ensure the accuracy of the model.
  • the normalized expression is specifically (x-127.5)/127.5.
  • the image sample is being copied, and the target image sample obtained by expanding the dimension of the image sample will be expressed as 512*512*N, where N is the number of times of copying. Copying the image sample and expanding the dimension of the image sample can increase the number of input samples, help the model to fully train, and improve the accuracy of tuberculosis determination.
  • S15 Use the target image samples to construct a training sample set, where the ratio of the target image samples used for model training and the target image samples used for model testing in the training sample set is 5:1.
  • the model training and model configuration can be completed well under this ratio, which is a better ratio.
  • steps S11-S15 a specific implementation manner for constructing a training sample set is provided, which can effectively process the image samples of the original chest X-ray film, so that the classification effect of the tuberculosis classification model is more accurate.
  • the ResNet-50 network contains a total of 49 convolutional layers, a standardized layer and a fully connected layer.
  • the classification effect of the ResNet-50 network is better.
  • the ResNet-50 network is used as the original model for training the tuberculosis classification model, and the migration learning method is adopted, and the weights obtained by pre-training are used as the initial weights of the ResNet-50 network.
  • the weight value obtained by the pre-training may specifically be the initial weight value used by the developer when dealing with other projects. Among them, the content of the project or the principle of function realization are more relevant to the tuberculosis classification, the better.
  • the speed of model training can be accelerated, and the accuracy of model classification can be improved. rate.
  • the input dimension of the ResNet-50 network can be specifically set to 512x512x3.
  • the target image samples used for model training are input into the ResNet-50 network for training, and a 256x256x64 feature map is obtained after a 7x7x64 convolutional layer and a 3x3 maximum pooling layer with a step size of 2; Then after 4 groups of residual modules, the output changes to 128x128x256, 64x64x512, 32x32x1024, 16x16x2048 feature maps (wherein, for feature maps with different dimensions, first use a 1x1 convolutional layer to adjust the dimensions of the input features to match the desired The dimension of the feature map is added, and then the elements of the corresponding position are added), and finally through the standardized layer and the fully connected layer of dimension 1, the output result is the predicted probability of pulmonary tuberculosis positive.
  • step of updating the weight of the ResNet-50 network according to the predicted probability specifically includes:
  • S42 Use a backpropagation algorithm to return the loss value generated during the training process to the ResNet-50 network, and update the weight of the ResNet-50 network according to the loss value returned during each training.
  • steps S41-S42 a specific implementation manner for updating the weights of the ResNet-50 network according to the predicted probability is provided, and the network parameters can be updated under supervised learning.
  • S50 Use the target image sample for model testing to test the model to be tested, and when the accuracy rate of the test result output by the model to be tested is greater than the preset accuracy rate, the model to be tested is used as a tuberculosis classification model.
  • the ResNet-50 network includes a convolutional layer, a standardization layer, and a fully connected layer.
  • the tuberculosis classification model first performs a preset number of passes on the convolutional layer in the ResNet-50 network when updating the weights of the ResNet-50 network. Update, after the preset number of training passes, freeze the weights of the convolutional layer in the ResNet-50 network, and use a learning rate of 0.001 to train the standardized layer and the fully connected layer in the ResNet-50 network for 1000 times. The weights of the standardized layer and the fully connected layer are updated, where freezing means that the weights of the convolutional layer in the ResNet-50 network are not updated.
  • the weights of the convolutional layer contain key features for distinguishing the target image samples, but the features reflected in the pre-training weights come from the sample training of other items and cannot be used to distinguish tuberculosis samples.
  • transfer learning is used to first train the convolutional layer to reflect the characteristics of judging tuberculosis disease; then freeze the convolutional layer, train the standardized layer and the fully connected layer, which can further improve the feature extraction ability of the model, and then improve the model The accuracy rate.
  • the tuberculosis classification model updates the weights of the ResNet-50 network
  • it first updates the convolutional layer in the ResNet-50 network within a preset number of passes.
  • the ResNet-50 network includes a convolutional layer, and the preset number of passes is specific It can be 3000 times.
  • the training process is to train the ResNet-50 network 3000 times with a learning rate of 0.0001, where each pass includes 10 target image samples for training.
  • the tuberculosis classification model is used to realize the tuberculosis determination of the chest X-ray image to be classified.
  • the tuberculosis classification model uses the ResNet-50 network as the deep neural network for training, so that the trained tuberculosis classification model has strong feature extraction capabilities and high classification accuracy; in addition, the training tuberculosis classification model also uses migration learning Method, the weights obtained by pre-training are used as the initial weights of the ResNet-50 network, which can speed up model training and improve the accuracy of model classification.
  • the embodiment of the application uses the tuberculosis classification model trained for tuberculosis determination.
  • the tuberculosis determination can be achieved according to the predicted probability output by the tuberculosis classification model. , Under the premise of ensuring high accuracy, the rapid determination of tuberculosis can be realized.
  • the embodiments of the present application further provide device embodiments that implement the steps and methods in the foregoing method embodiments.
  • Fig. 2 shows a principle block diagram of a tuberculosis determination device corresponding to the tuberculosis determination method in the embodiment one-to-one.
  • the tuberculosis determination device includes a first acquisition module 10, a second acquisition module 20, a determination module 30, a construction module 40, an initialization module 50, a training module 60, an update module 70 and a third acquisition module 80.
  • the implementation functions of the first acquisition module 10, the second acquisition module 20, the determination module 30, the construction module 40, the initialization module 50, the training module 60, the update module 70, and the third acquisition module 80 correspond to the tuberculosis determination method in the embodiment
  • the steps of are one-to-one correspondence, in order to avoid repetition, this embodiment will not describe them one by one.
  • the first acquisition module 10 is used to acquire a chest X-ray image to be classified.
  • the second acquisition module 20 is used to convert the chest X-ray image to be classified into a target image to be classified according to the preset image processing steps, wherein the resolution and dimension of the target image to be classified are the same as the target image for training the tuberculosis classification model The samples are the same.
  • the determination module 30 is used to input the target image to be classified into the tuberculosis classification model to obtain the predicted probability.
  • the predicted probability is the probability that the tuberculosis is predicted to be positive. When the predicted probability is greater than a preset threshold, determine the target image to be classified.
  • the classification of chest X-ray images has tuberculosis, and the tuberculosis classification model is obtained through the construction module, the initialization module, the training module, the update module, and the third acquisition module:
  • the construction module 40 is used to construct a training sample set, where the training sample set includes a target image sample used for model training and a target image sample used for model testing, a target image sample used for model training and a target used for model testing The same target image sample does not exist between the image samples.
  • the initialization module 50 is used to use the ResNet-50 network as a training deep neural network, and use the weights obtained by pre-training as the initial weights of the ResNet-50 network.
  • the training module 60 is used to input the target image samples used for model training into the ResNet-50 network for training, and the output result is the predicted probability of pulmonary tuberculosis positive.
  • the update module 70 is configured to update the weight of the ResNet-50 network according to the predicted probability, and stop training until the updated change amount is less than the first preset threshold to obtain the model to be tested.
  • the third acquisition module 80 is used to test the model to be tested using the target image sample for model testing, and when the accuracy of the test result output by the model to be tested is greater than the preset accuracy, the model to be tested is used as a tuberculosis classification model.
  • the building module 40 is specifically used for:
  • the image samples are processed into image samples with a preset resolution.
  • the down-sampling method is used to down-sample the resolution of the image samples to the preset resolution.
  • image samples lower than the preset resolution use bilinear interpolation to upsample the resolution of the image samples to the preset resolution;
  • the target image samples are used to construct a training sample set, where the ratio of the target image samples used for model training and the target image samples used for model testing in the training sample set is 5:1.
  • the ResNet-50 network includes a convolutional layer.
  • the training process is to use a learning rate of 0.0001 to train 3000 times the ResNet-50 network, where each training pass includes 10 A sample of the target image used for training.
  • the ResNet-50 network includes a convolutional layer, a standardization layer, and a fully connected layer.
  • the tuberculosis classification model first performs a preset number of passes to the convolutional layer in the ResNet-50 network when updating the weights of the ResNet-50 network After updating, the preset number of passes is trained, the weights of the convolutional layer in the ResNet-50 network are frozen, and the learning rate of 0.001 is used to train the standardized layer and the fully connected layer in the ResNet-50 network 1000 times.
  • the weights of the standardized layer and the fully connected layer are updated, where freezing means that the weights of the convolutional layer in the ResNet-50 network are not updated.
  • the update module 70 is specifically used for:
  • the cross entropy loss function is used to calculate the loss value generated during the training process, where the cross entropy loss function is expressed as: Represents the label of the target image sample used for training, and y represents the predicted probability;
  • the backpropagation algorithm is used to return the loss value generated during the training process to the ResNet-50 network, and the weight of the ResNet-50 network is updated according to the loss value returned during each training.
  • the tuberculosis classification model is used to realize the tuberculosis determination of the chest X-ray image to be classified.
  • the tuberculosis classification model uses the ResNet-50 network as the deep neural network for training, so that the trained tuberculosis classification model has strong feature extraction capabilities and high classification accuracy; in addition, the training tuberculosis classification model also uses migration learning Method, the weights obtained by pre-training are used as the initial weights of the ResNet-50 network, which can speed up model training and improve the accuracy of model classification.
  • the embodiment of the application uses the tuberculosis classification model trained for tuberculosis determination.
  • the tuberculosis determination can be achieved according to the predicted probability output by the tuberculosis classification model. , Under the premise of ensuring high accuracy, the rapid determination of tuberculosis can be realized.
  • This embodiment provides a computer non-volatile readable storage medium, the computer non-volatile readable storage medium stores computer readable instructions, and when the computer readable instructions are executed by a processor, the method for determining tuberculosis in the embodiment is implemented To avoid repetition, I won’t repeat them here.
  • the computer-readable instructions realize the functions of the various modules/units in the tuberculosis determination device in the embodiment when being executed by the processor. In order to avoid repetition, details are not repeated here.
  • Fig. 3 is a schematic diagram of a computer device provided by an embodiment of the present application.
  • the computer device 90 of this embodiment includes: a processor 91, a memory 92, and computer-readable instructions 93 stored in the memory 92 and running on the processor 91, and the computer-readable instructions 93 are processed.
  • the method for determining tuberculosis in the embodiment is implemented when the device 91 is executed. In order to avoid repetition, it will not be repeated here.
  • the computer-readable instruction 93 is executed by the processor 91, the function of each model/unit in the tuberculosis determination device in the embodiment is realized. In order to avoid repetition, it will not be repeated here.
  • the computer device 90 may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server.
  • the computer device 90 may include, but is not limited to, a processor 91 and a memory 92.
  • FIG. 3 is only an example of the computer device 90, and does not constitute a limitation on the computer device 90. It may include more or less components than those shown in the figure, or a combination of certain components, or different components.
  • computer equipment may also include input and output devices, network access devices, buses, and so on.
  • the so-called processor 91 may be a central processing unit (Central Processing Unit, CPU), other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc.
  • the general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like.
  • the memory 92 may be an internal storage unit of the computer device 90, such as a hard disk or a memory of the computer device 90.
  • the memory 92 may also be an external storage device of the computer device 90, such as a plug-in hard disk equipped on the computer device 90, a smart media card (SMC), a secure digital (SD) card, and a flash memory card (Flash). Card) and so on.
  • the memory 92 may also include both an internal storage unit of the computer device 90 and an external storage device.
  • the memory 92 is used to store computer readable instructions and other programs and data required by the computer equipment.
  • the memory 92 can also be used to temporarily store data that has been output or will be output.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Biomedical Technology (AREA)
  • Evolutionary Computation (AREA)
  • General Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Computational Linguistics (AREA)
  • Evolutionary Biology (AREA)
  • Molecular Biology (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Biophysics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Computing Systems (AREA)
  • Public Health (AREA)
  • Medical Informatics (AREA)
  • Pathology (AREA)
  • Databases & Information Systems (AREA)
  • Epidemiology (AREA)
  • Primary Health Care (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)
  • Apparatus For Radiation Diagnosis (AREA)

Abstract

La présente invention concerne un procédé et un appareil de détermination de tuberculose pulmonaire, un dispositif informatique et un support de stockage qui se rapportent au domaine technique de l'intelligence artificielle. Le procédé de détermination de tuberculose pulmonaire comprend les étapes consistant à : acquérir une image radiographique de poitrine à classifier ; convertir l'image radiographique de poitrine en une image cible à classifier sur la base d'étapes de traitement d'image pré-configurées, la résolution et la dimension de l'image cible étant identiques à celles d'un échantillon d'image cible destiné à l'entraînement d'un modèle de classification de tuberculose pulmonaire ; entrer l'image cible dans le modèle de classification de tuberculose pulmonaire afin d'acquérir une probabilité prédite, la probabilité prédite étant la probabilité acquise au moyen de la prédiction selon laquelle un test de tuberculose pulmonaire est positif et, lorsque la probabilité prédite est supérieure à un seuil pré-configuré, déterminer qu'une tuberculose pulmonaire existe dans l'image radiographique de poitrine correspondant à l'image cible. Le procédé de détermination de tuberculose pulmonaire assure une précision élevée et permet une détermination rapide de la présence d'une tuberculose pulmonaire.
PCT/CN2019/115946 2019-09-16 2019-11-06 Procédé et appareil de détermination de tuberculose pulmonaire, dispositif informatique et support de stockage WO2021051497A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201910869773.6 2019-09-16
CN201910869773.6A CN110738235B (zh) 2019-09-16 2019-09-16 肺结核判定方法、装置、计算机设备及存储介质

Publications (1)

Publication Number Publication Date
WO2021051497A1 true WO2021051497A1 (fr) 2021-03-25

Family

ID=69267962

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2019/115946 WO2021051497A1 (fr) 2019-09-16 2019-11-06 Procédé et appareil de détermination de tuberculose pulmonaire, dispositif informatique et support de stockage

Country Status (2)

Country Link
CN (1) CN110738235B (fr)
WO (1) WO2021051497A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113378483A (zh) * 2021-07-12 2021-09-10 广东电网有限责任公司 一种电网数据的预警方法、装置、设备及存储介质
CN113657530A (zh) * 2021-08-24 2021-11-16 凌云光技术股份有限公司 一种采用检测及分类模型对多目标大图像进行处理的方法及装置

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114065826A (zh) * 2020-07-28 2022-02-18 紫东信息科技(苏州)有限公司 图像分类模型的构建方法、分类方法、装置及电子设备
CN111932564B (zh) * 2020-09-24 2021-03-02 平安科技（深圳）有限公司 图片识别方法、装置、电子设备及计算机可读存储介质
CN113076993B (zh) * 2021-03-31 2023-04-18 零氪智慧医疗科技(天津)有限公司 用于胸部x光片识别的信息处理方法、模型训练方法
CN113780444B (zh) * 2021-09-16 2023-07-25 平安科技（深圳）有限公司 基于渐进式学习的舌苔图像分类模型的训练方法
CN117095241B (zh) * 2023-10-17 2024-01-12 四川大学 一种耐药性肺结核类别的筛查方法、系统、设备及介质

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190073568A1 (en) * 2017-09-06 2019-03-07 Kla-Tencor Corporation Unified neural network for defect detection and classification
CN109635842A (zh) * 2018-11-14 2019-04-16 平安科技（深圳）有限公司 一种图像分类方法、装置及计算机可读存储介质
CN109685765A (zh) * 2018-11-21 2019-04-26 浙江大学山东工业技术研究院 一种基于卷积神经网络的x光片肺炎结果预测装置
CN110111313A (zh) * 2019-04-22 2019-08-09 腾讯科技（深圳）有限公司 基于深度学习的医学图像检测方法及相关设备
CN110188795A (zh) * 2019-04-24 2019-08-30 华为技术有限公司 图像分类方法、数据处理方法和装置

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107492099B (zh) * 2017-08-28 2021-08-20 京东方科技集团股份有限公司 医学图像分析方法、医学图像分析系统以及存储介质
CN108022647B (zh) * 2017-11-30 2022-01-25 东北大学 基于ResNet-Inception模型的肺结节良恶性预测方法
CN109191436A (zh) * 2018-08-15 2019-01-11 复旦大学 基于视觉显著性谱残差方法的低剂量ct肺结节检测算法
CN109345507B (zh) * 2018-08-24 2021-07-13 河海大学 一种基于迁移学习的大坝图像裂缝检测方法
CN110163260B (zh) * 2019-04-26 2024-05-28 平安科技（深圳）有限公司 基于残差网络的图像识别方法、装置、设备及存储介质

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190073568A1 (en) * 2017-09-06 2019-03-07 Kla-Tencor Corporation Unified neural network for defect detection and classification
CN109635842A (zh) * 2018-11-14 2019-04-16 平安科技（深圳）有限公司 一种图像分类方法、装置及计算机可读存储介质
CN109685765A (zh) * 2018-11-21 2019-04-26 浙江大学山东工业技术研究院 一种基于卷积神经网络的x光片肺炎结果预测装置
CN110111313A (zh) * 2019-04-22 2019-08-09 腾讯科技（深圳）有限公司 基于深度学习的医学图像检测方法及相关设备
CN110188795A (zh) * 2019-04-24 2019-08-30 华为技术有限公司 图像分类方法、数据处理方法和装置

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113378483A (zh) * 2021-07-12 2021-09-10 广东电网有限责任公司 一种电网数据的预警方法、装置、设备及存储介质
CN113657530A (zh) * 2021-08-24 2021-11-16 凌云光技术股份有限公司 一种采用检测及分类模型对多目标大图像进行处理的方法及装置
CN113657530B (zh) * 2021-08-24 2024-04-05 凌云光技术股份有限公司 一种采用检测及分类模型对多目标大图像进行处理的方法及装置

Also Published As

Publication number Publication date
CN110738235B (zh) 2023-05-30
CN110738235A (zh) 2020-01-31

Similar Documents

Publication Publication Date Title
WO2021051497A1 (fr) Procédé et appareil de détermination de tuberculose pulmonaire, dispositif informatique et support de stockage
CN112037912B (zh) 基于医疗知识图谱的分诊模型训练方法、装置及设备
WO2020155518A1 (fr) Procédé et dispositif de détection d'objet, dispositif informatique et support d'informations
WO2016124103A1 (fr) Procédé et dispositif de détection d'image
WO2021179498A1 (fr) Procédé de détection de cible, procédé d'entraînement d'un modèle pour celui-ci, appareil et dispositif électronique
CN110866471A (zh) 人脸图像质量评价方法及装置、计算机可读介质、通信终端
TWI532014B (zh) 圖像相似度判斷系統及方法
WO2022213465A1 (fr) Procédé et appareil de reconnaissance d'image à base de réseau neuronal, dispositif électronique et support
GB2571825A (en) Semantic class localization digital environment
AU2017272288A1 (en) Method, system, electronic device, and medium for classifying license plates based on deep learning
WO2019200702A1 (fr) Procédé et appareil d'apprentissage de système de détramage, procédé et appareil de détramage, et support
US20220092407A1 (en) Transfer learning with machine learning systems
US11568212B2 (en) Techniques for understanding how trained neural networks operate
WO2021217937A1 (fr) Procédé et dispositif d'apprentissage de modèle de posture, procédé et dispositif de reconnaissance de posture
US10438088B2 (en) Visual-saliency driven scene description
CN112329762A (zh) 图像处理方法、模型训练方法、装置、计算机设备和介质
WO2024001806A1 (fr) Procédé d'évaluation de données basé sur un apprentissage fédéré et dispositif associé
CN111192678A (zh) 病理显微图像诊断、模型训练方法、装置、设备及介质
US11188795B1 (en) Domain adaptation using probability distribution distance
WO2021042544A1 (fr) Procédé et appareil de vérification faciale basés sur un modèle de retrait de maillage, et dispositif informatique et support d'informations
US9928408B2 (en) Signal processing
TWI803243B (zh) 圖像擴增方法、電腦設備及儲存介質
CN116415020A (zh) 一种图像检索的方法、装置、电子设备及存储介质
TWI742312B (zh) 機器學習系統、機器學習方法及其非暫態電腦可讀媒體
CN112651467A (zh) 卷积神经网络的训练方法和系统以及预测方法和系统

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 19945840

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 19945840

Country of ref document: EP

Kind code of ref document: A1