WO2021169101A1 - Procédé et appareil de génération de modèle de reconnaissance d'image médicale, dispositif informatique et support - Google Patents

Procédé et appareil de génération de modèle de reconnaissance d'image médicale, dispositif informatique et support Download PDF

Info

Publication number
WO2021169101A1
WO2021169101A1 PCT/CN2020/097964 CN2020097964W WO2021169101A1 WO 2021169101 A1 WO2021169101 A1 WO 2021169101A1 CN 2020097964 W CN2020097964 W CN 2020097964W WO 2021169101 A1 WO2021169101 A1 WO 2021169101A1
Authority
WO
WIPO (PCT)
Prior art keywords
image
medical
information
annotation
file
Prior art date
Application number
PCT/CN2020/097964
Other languages
English (en)
Chinese (zh)
Inventor
丁思雨
丁文静
Original Assignee
平安国际智慧城市科技股份有限公司
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 平安国际智慧城市科技股份有限公司 filed Critical 平安国际智慧城市科技股份有限公司
Publication of WO2021169101A1 publication Critical patent/WO2021169101A1/fr

Links

Images

Classifications

    • 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
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/5866—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using information manually generated, e.g. tags, keywords, comments, manually generated location and time information
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning

Definitions

  • This application relates to a method, device, computer equipment and medium for generating a medical image recognition model.
  • a method, device, computer device, and medium for generating a medical image recognition model are provided.
  • a method for generating a medical image recognition model including:
  • the image annotation file is input into a machine learning model to learn the feature relationship between the image features in the image annotation file and the image annotation information through the machine learning model, and a medical image recognition model is generated according to the feature relationship.
  • a medical image recognition model generation device including:
  • Data acquisition module for acquiring medical data
  • the feature extraction module is used to extract medical images in the medical data, and perform feature extraction on the medical images to obtain image features;
  • An annotation generation module configured to extract medical documents in the medical data, analyze the medical documents to generate annotation information, and search for image annotation information corresponding to the medical image in the annotation information;
  • An image file generating module configured to establish a mapping relationship according to the image characteristics and the image annotation information, and generate an image annotation file according to the mapping relationship;
  • the model generation module is configured to input the image annotation file into a machine learning model to learn the feature relationship between the image feature in the image annotation file and the image annotation information through the machine learning model, and according to the feature relationship Generate medical image recognition model.
  • a computer device including a memory and one or more processors, the memory stores computer readable instructions, and when the computer readable instructions are executed by the processor, the one or more processors execute The following steps:
  • the image annotation file is input into a machine learning model to learn the feature relationship between the image features in the image annotation file and the image annotation information through the machine learning model, and a medical image recognition model is generated according to the feature relationship.
  • One or more computer-readable storage media storing computer-readable instructions.
  • the one or more processors perform the following steps:
  • the image annotation file is input into a machine learning model to learn the feature relationship between the image features in the image annotation file and the image annotation information through the machine learning model, and a medical image recognition model is generated according to the feature relationship.
  • Fig. 1 is an application scenario diagram of a method for generating a medical image recognition model according to one or more embodiments.
  • Fig. 2 is a schematic flowchart of a method for generating a medical image recognition model according to one or more embodiments.
  • FIG. 3 is a schematic flowchart of a method for generating and using a medical image recognition model according to one or more embodiments.
  • Fig. 4 is a schematic flowchart of a method for generating a medical image recognition model in another embodiment.
  • Fig. 5 is a structural block diagram of a device for generating a medical image recognition model according to one or more embodiments.
  • Figure 6 is a block diagram of a computer device according to one or more embodiments.
  • the medical image recognition model generation method provided in this application can be applied to the application environment as shown in FIG. 1.
  • the user terminal 102 communicates with the server 104 through the network.
  • the server 104 obtains medical data; the server 104 extracts medical images in the medical data, and extracts features from the medical images to obtain image features; the server 104 extracts medical files in the medical data, analyzes the medical files to generate annotation information, and the server 104 searches for the annotation information
  • the image annotation information corresponding to the medical image in the image; the server 104 establishes a mapping relationship according to the image characteristics and the image annotation information, and generates an image annotation file according to the mapping relationship; inputs the image annotation file into the machine learning model to learn the image annotation file through the machine learning model Based on the feature relationship between the image feature and the image annotation information, a medical image recognition model is generated according to the feature relationship.
  • the medical image recognition model can also be pushed to the user terminal 102.
  • the user terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices.
  • the server 104 may be implemented by an independent server or a server cluster composed of multiple servers. When the server 104 is an independent server, multiple databases can be deployed in the server 104, and each database can store specific medical files; when the server 104 is a server cluster composed of multiple servers, the database deployed in each server Can store specific medical file data tables.
  • FIG. 2 a schematic flow chart of a method for generating a medical image recognition model is provided.
  • the method is applied to the server 104 in FIG. 1 as an example for description.
  • the The method can also be applied to the terminal, and the method includes the following steps:
  • Step 210 Obtain medical data.
  • Medical data includes at least medical images and medical documents generated during medical procedures. Specifically, the user terminal uploads medical data with the help of the client, and the server obtains the medical data uploaded by the user terminal to perform data analysis on the medical data.
  • Step 220 Extract medical images from the medical data, and perform feature extraction on the medical images to obtain image features.
  • Medical images can be human body structure diagrams collected by medical equipment. Medical images not only include the medical image itself, but may also have attribute information associated with the medical image. For example, the attribute information can be the label information, diagnosis description information, and annotations corresponding to the medical image. Auxiliary information of medical images such as result information. And medical images can also have multiple storage formats.
  • the server may use an image feature extraction algorithm to extract image features corresponding to the medical image.
  • an image feature extraction algorithm For example, use the Histogram of Oriented Gradient (HOG) feature extraction algorithm, Local Binary Pattern (LBP) feature extraction algorithm, or Haar feature extraction algorithm to extract image features corresponding to medical images, such as image features It can be "raised”, “dark tone” and so on.
  • HOG Histogram of Oriented Gradient
  • LBP Local Binary Pattern
  • Haar feature extraction algorithm to extract image features corresponding to medical images, such as image features It can be "raised”, “dark tone” and so on.
  • the server preprocesses the acquired medical image, such as performing enhancement processing or filtering processing on the medical image, so as to improve the accuracy of the feature extraction of the medical image by the server.
  • Step 230 Extract medical files in the medical data, analyze the medical files to generate annotation information, and search for image annotation information corresponding to the medical image in the annotation information.
  • Medical documents can be textual information generated during medical treatment, etc., and medical documents can have multiple formats, such as medical paper medical records or medical electronic medical record documents, etc., more specifically, medical electronic medical record documents can be excel, xml And other file formats.
  • the server extracts medical documents from the medical data, analyzes the medical documents to generate annotation information, and the annotation information may be diagnostic information given by medical professionals.
  • the medical file may contain labeling information for the medical image, and the labeling information may be the diagnostic information for the medical image given by the medical professional.
  • the server may use keyword extraction technology to extract annotation information from medical documents, and when the acquired medical document is a medical paper medical record, the server also includes electronic processing of the paper medical record to obtain an electronic medical record document, Then the required annotation information is parsed from the electronic medical record document. Parsing refers to the process of extracting the required information from the medical file.
  • the annotation information can be the cause of the disease, diagnosis suggestions, or diagnosis results.
  • the server can extract the annotation information of the medical image in the medical text according to the keyword matching technology, for example, the annotation information corresponding to the diagnosis result of the medical image may be "lumps", "shadows", and so on.
  • Step 240 Establish a mapping relationship according to the image characteristics and the image annotation information, and generate an image annotation file according to the mapping relationship.
  • an image annotation file can be: shadow-pulmonary cyst, indicating that when the medical image feature extracted by the server is "shadow”, the corresponding image annotation information should be "pulmonary cyst” at this time, so that the annotation information can be automatically obtained from the medical image Function.
  • the server can also perform data processing on the acquired image annotation files, such as data cleaning, data management, data verification, etc., to make the acquired data more standard.
  • Step 250 Input the image annotation file into the machine learning model to learn the feature relationship between the image features in the image annotation file and the image annotation information through the machine learning model, and generate a medical image recognition model based on the feature relationship.
  • the server uses the sample set to train the machine learning model to obtain the association relationship between the image features and the image annotation information, and obtain the medical image recognition model according to the association relationship. Since the medical image recognition model stores the correspondence between image features and image annotation information, the medical image recognition model can be used to realize automatic acquisition of medical image features or image annotation information.
  • the server extracts medical images and medical texts from medical data, extracts image features corresponding to medical images and annotation information in medical texts, realizes automatic extraction of medical information from medical data, and improves the processing of medical data. Efficiency and accuracy.
  • an image annotation file is established, and the image annotation file is used as a sample set to train the machine learning model to obtain a medical image recognition model.
  • the medical image recognition model can then be used to automatically extract image annotation information, which improves The recognition accuracy and efficiency of medical data.
  • establishing a mapping relationship based on image features and image annotation information, and generating an image annotation file based on the mapping relationship includes: obtaining an image identifier corresponding to the medical image; searching for an image analysis method corresponding to the image identifier; The attribute information associated with the medical image is analyzed to generate image label information; the mapping relationship is established according to the image characteristics, the image annotation information, and the image label information, and the image annotation file is generated according to the mapping relationship.
  • the image identifier is used to uniquely identify the category of the medical image, and the category of the medical image can be the storage format of the image.
  • Different medical images can be stored in different medical image formats.
  • the information contained in different formats of medical images is different, and the usage scenarios are different. In traditional technology, they are read and viewed in different formats through corresponding client tools. Medical imaging requires users to download multiple tools and know the instructions for use of the corresponding tools, which brings great inconvenience to users and also makes the efficiency of viewing medical images inefficient.
  • the server extracts the medical image from the medical data, obtains the image identifier corresponding to the medical image, and calls the image analysis method associated with the image identifier to automatically analyze the attribute information associated with the medical image according to the image analysis method to generate image tag information.
  • the storage format can be DICOM, ndpi, tiff, jpg, bmp, png, etc.
  • C++-based DCMTK, Java-based dcm4che, and python-based pydicom can be used for analysis.
  • Another example is the use of dcm4che to analyze the tag information of DICOM files, and the use of opencv to analyze and process medical images such as pathology and fundus.
  • the image analysis method is used to extract and analyze the attribute information corresponding to the medical image to obtain the required image tag information.
  • the attribute information associated with a medical image can be encoded information, and the server parses and converts the encoded information according to the image analysis method found to obtain image tag information.
  • the parsed image tag information is: Tag. Tag.PatientName--patient name, Tag.PatientAge--patient age, Tag.PatientSex--patient gender, Tag.StudyID--examination ID, Tag.StudyDate--examination date, etc.
  • performing feature extraction on medical images to obtain image features includes: extracting annotation information in the medical image; obtaining location coordinates corresponding to the annotation information; extracting regional medical images corresponding to the location coordinates from the medical images; using Image feature extraction algorithm extracts image features of regional medical images.
  • the annotation information in the medical image may be information for identifying areas that need to be focused on in the medical image.
  • the annotation information may be parsed based on files such as xml and json generated by an internal annotation platform that has been desensitized.
  • the location coordinates can be the coordinates of the labeled information in the medical image, and the server can obtain the regional medical image corresponding to the area that needs to be focused on through the coordinates.
  • the server may use HOG feature extraction algorithm, LBP feature extraction algorithm, Haar feature extraction algorithm, etc. to automatically extract image features corresponding to regional medical images.
  • the extracted image features can be "bulge” or “dark tone”.
  • the server can extract the labeling information of the regional medical image in the medical text according to the keyword matching technology, for example, the labeling information can be "lumps", "shadows", and so on.
  • the regional medical image in the medical image is automatically extracted according to the coordinates in the medical image, and the key areas in the medical image and the regional features of the key areas are automatically extracted, so that the operation steps of the regional medical image can be realized. Improve the efficiency of medical image data processing.
  • after parsing the medical file to generate the annotation information it further includes: dividing the annotation information into unit annotation information according to preset rules; matching the unit annotation information with the information in the annotation information database, and matching The information with the highest degree is extracted as label information.
  • the labeling information generated by the server parsing the medical documents can be professionals, such as doctors’ diagnosis suggestions for patients. Since different doctors may have different expressions for the same disease, the labeling information obtained at this time may exist. Inconsistent textual representations of the same matter.
  • the server can use Chinese natural language processing word segmentation methods, such as HanLP for Chinese word segmentation, and segment and adjust the obtained labeling information according to certain rules to obtain multiple unit labeling information, and then label the unit
  • Chinese natural language processing word segmentation methods such as HanLP for Chinese word segmentation
  • segment and adjust the obtained labeling information according to certain rules to obtain multiple unit labeling information, and then label the unit
  • the information is matched with words in an annotation information database such as a dictionary, and the information with the largest matching degree in the dictionary is extracted as annotation information. Since the information stored in the dictionary is standardized information, the use of dictionary matching technology can realize the standardized processing of labeling information.
  • Chinese word segmentation methods include word segmentation methods based on string matching, word segmentation methods based on understanding, and word segmentation methods based on statistics.
  • Chinese word segmentation is performed based on HanLP to generate labeling information, which realizes the standardization of labeling information and facilitates subsequent data retrieval.
  • the method further includes: receiving the query request sent by the terminal, extracting the query identifier carried in the query request; extracting the image identifier corresponding to the image annotation file; calculating the query identifier and each image The matching degree of the logo; the image annotation file associated with the image logo with the largest matching degree is pushed to the terminal.
  • the image annotation file after the image annotation file is established according to each image feature and corresponding image annotation information, it also includes associating each image annotation file with the corresponding image identifier to generate an image annotation file library, that is, the image annotation file library stores different images Mark the corresponding image annotation file, such as "lung-lung image annotation file, stomach-stomach image annotation file, etc.” are stored.
  • the user terminal wants to find medical image diagnosis suggestions from the image annotation file library
  • the user terminal sends a query request to the server, and the server matches the corresponding image annotation file from the image annotation file library according to the obtained query request, and annotates the image
  • the file is pushed to the user terminal, and then the user can perform operations based on the queried image annotation file. For example, users can match their own medical images with the medical images in the acquired image annotation files, and then query the annotation information of the medical images in the image annotation file library to obtain diagnosis suggestions for personal pathological conditions.
  • the user terminal may send a query request to the server, for example, it may be an image query request.
  • the server extracts the query identifier carried in the query request, for example, extracts the image query identifier carried in the image query request. Then the server matches the obtained query identifier with the image identifier corresponding to the image annotation file, and pushes the image annotation file associated with the successfully matched image identifier to the user terminal, so as to realize the function that the user can obtain the corresponding image annotation file through the query request .
  • the user wants to obtain the image annotation file corresponding to a certain type of disease, he can obtain the corresponding file only according to the query request. Specifically, the user can use the obtained image annotation file as a reference file, and can refer to the medical image of his concern. Compare with the medical images in the acquired reference image annotation files, and then obtain the reference image features and reference image annotation information of the medical images that they are concerned about, and help users obtain professional medical image suggestions in time.
  • the server obtains the user's query request, and then can push the corresponding image annotation file to the user, which realizes the automatic acquisition of the image annotation file and improves the efficiency of file acquisition.
  • a flow diagram of a method for generating and using a medical image recognition model including:
  • Step 310 Obtain medical data.
  • Step 320 Extract medical images from the medical data, and perform feature extraction on the medical images to obtain image features.
  • Step 330 Extract medical files in the medical data, analyze the medical files to generate annotation information, and search for image annotation information corresponding to the medical image in the annotation information.
  • Step 340 Establish a mapping relationship according to the image characteristics and the image annotation information, and generate an image annotation file according to the mapping relationship.
  • Step 350 Input the image annotation file into the machine learning model to learn the feature relationship between the image features in the image annotation file and the image annotation information through the machine learning model, and generate a medical image recognition model based on the feature relationship.
  • step 310 to step 350 For specific embodiments of step 310 to step 350, reference may be made to the description of step 210 to step 250 in the foregoing, which is not repeated here.
  • the method further includes:
  • Step 360 Receive the medical image sent by the terminal.
  • the user terminal may send medical images to the server, and there is no restriction on the format of the medical images sent by the user terminal.
  • the server receives the medical images sent by the user terminal.
  • Step 370 Input the medical image into the medical image recognition model to extract image features corresponding to the medical image through the medical image recognition model, and obtain image annotation information based on the medical image features.
  • the server recognizes the acquired medical images according to the trained medical image recognition model, and then obtains the image annotation information corresponding to the medical image, and realizes the medical image Automatic recognition.
  • Step 380 Push the image annotation information to the terminal.
  • the server pushes the image annotation information recognized according to the medical image recognition model to the user terminal.
  • the trained medical image recognition model can be used to recognize medical images in any format, realize automatic recognition and automatic labeling of medical images, and use the medical image recognition model obtained by automatic training of big data.
  • the recognition result is more accurate and the recognition efficiency is higher.
  • a schematic diagram of the process of generating a medical image recognition model in another embodiment including:
  • Step 400 Obtain files in DICOM, ndpi, tiff, jpg, bmp, png and other formats.
  • the server can obtain medical files in multiple formats, and the server can obtain files uploaded by the user terminal.
  • Step 410 Obtain a diagnosis file, an annotation file, and an electronic medical record.
  • the server can obtain the diagnosis file, the annotation file, the electronic medical record, etc. in the medical file.
  • the server can obtain the files uploaded by the user terminal.
  • Step 420 upload the client.
  • the medical files obtained in step 400 and step 410 are uploaded to the client.
  • the server can call the corresponding analysis algorithm for analysis based on the obtained medical files in multiple formats, and obtain the medical image in step 430 and the metadata information in step 440.
  • the metadata information can be label information, including tag information and diagnosis. Information and other label information, etc.
  • Step 450 Obtain a medical tag library.
  • the medical tag library may be an image annotation file.
  • Step 460 the medical image management platform.
  • the server uploads the data acquired in step 430 and step 450 to the medical image management platform.
  • the medical image management platform may be an image annotation file library, which stores multiple types of image annotation files. Then, the medical image management platform can be used to perform the medical image retrieval in step 470, the medical image export in step 480, and the intelligent labeling function in step 490.
  • a medical image recognition model generation device including: a data acquisition module 510, a feature extraction module 520, an annotation generation module 530, an image file generation module 540, and a model generation module 550, of which:
  • the data acquisition module 510 is used to acquire medical data.
  • the feature extraction module 520 is configured to extract medical images in the medical data, and perform feature extraction on the medical images to obtain image features.
  • the annotation generating module 530 is configured to extract medical documents in the medical data, analyze the medical documents to generate annotation information, and search for image annotation information corresponding to the medical image in the annotation information.
  • the image file generating module 540 is configured to establish a mapping relationship according to the image characteristics and the image annotation information, and generate an image annotation file according to the mapping relationship.
  • the model generation module 550 is configured to input the image annotation file into a machine learning model to learn the feature relationship between the image feature in the image annotation file and the image annotation information through the machine learning model, and according to the feature Relationship generation medical image recognition model.
  • the feature extraction module 520 includes:
  • the image annotation information extraction unit is used to extract the annotation information in the medical image.
  • the coordinate acquisition unit is used to acquire the position coordinates corresponding to the label information.
  • the regional image extraction unit is used to extract regional medical images corresponding to the position coordinates from the medical images.
  • the regional feature extraction unit is used to extract image features of the regional medical image by using an image feature extraction algorithm.
  • the device further includes:
  • the segmentation module is configured to segment the label information into unit label information according to preset rules.
  • the information extraction module is configured to extract and match the unit label information with the information in the label information database, and extract the information with the greatest degree of matching as label information.
  • the image file generating module 540 includes:
  • the identification acquiring unit is used to acquire the image identification corresponding to the medical image.
  • the method search unit is used to search for the image analysis method corresponding to the image identifier.
  • the information generating unit is configured to analyze the attribute information associated with the medical image by using the image analysis method to generate image tag information.
  • the file generating unit is configured to establish a mapping relationship according to the image characteristics, the image annotation information, and the image tag information, and generate an image annotation file according to the mapping relationship.
  • the device further includes:
  • the query identifier extraction module is used to receive the query request sent by the terminal, and extract the query identifier carried in the query request.
  • the identification extraction module is used to extract the image identification corresponding to the image annotation file.
  • the calculation module is used to calculate the matching degree between the query identifier and each of the image identifiers.
  • the first pushing module is configured to push the image annotation file associated with the image identifier with the greatest matching degree to the terminal.
  • the device further includes:
  • the receiving module is used to receive medical images sent by the terminal.
  • the annotation information recognition module is used to input the medical image into the medical image recognition model to extract the image feature corresponding to the medical image through the medical image recognition model, and obtain the image according to the medical image feature Annotate information.
  • the second push module is used to push the image annotation information to the terminal.
  • Each module in the above-mentioned medical image recognition model generating 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 computer device is provided.
  • the computer device may be a server, and its internal structure diagram may be as shown in FIG. 6.
  • 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 non-volatile or volatile storage media and internal memory.
  • the non-volatile or volatile 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 non-volatile or volatile storage medium.
  • the computer equipment database is used for medical data processing related data.
  • the network interface of the computer device is used to communicate with an external terminal through a network connection.
  • FIG. 6 is only a block diagram of part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied.
  • the specific computer device may Including more or fewer parts than shown in the figure, or combining some parts, or having a different arrangement of parts.
  • a computer device includes a memory and one or more processors.
  • the memory stores computer-readable instructions.
  • the one or more processors perform the following steps: acquiring medical data; extracting For the medical images in the medical data, feature extraction is performed on the medical images to obtain image features; the medical files in the medical data are extracted, the medical files are parsed to generate annotation information, and the annotation information is searched for and the The image annotation information corresponding to the medical image; establish a mapping relationship according to the image characteristics and the image annotation information, and generate an image annotation file according to the mapping relationship; and input the image annotation file into a machine learning model to pass machine learning The model learns the feature relationship between the image feature and the image tag information in the image tag file, and generates a medical image recognition model according to the feature relationship.
  • the processor when the processor executes the computer-readable instructions to implement the step of extracting features from the medical image to obtain image features, it is also used to: extract the annotation information in the medical image; and obtain the annotation information Corresponding location coordinates; extracting regional medical images corresponding to the location coordinates from the medical images; and extracting image features of the regional medical images using an image feature extraction algorithm.
  • the processor when the processor executes the computer-readable instructions, after the processor executes the parsing of the medical file to generate annotation information, it is further used to: divide the annotation information into unit annotation information according to a preset rule; And matching the unit labeling information with the information in the labeling information database, and extracting the information with the greatest degree of matching as labeling information.
  • the step of establishing a mapping relationship based on the image characteristics and the image annotation information, and generating an image annotation file based on the mapping relationship is further used for: Obtain the image identifier corresponding to the medical image; find the image analysis method corresponding to the image identifier; use the image analysis method to analyze the attribute information associated with the medical image to generate image tag information; and according to the image feature Establishing a mapping relationship between the image annotation information and the image tag information, and generating an image annotation file according to the mapping relationship.
  • the processor when the processor executes the computer-readable instruction, after the image annotation file is generated according to the mapping relationship, the processor is further used to: receive the query request sent by the terminal, and extract the query identifier carried in the query request Extracting the image identification corresponding to the image annotation file; calculating the matching degree between the query identification and each of the image identification; and pushing the image annotation file associated with the image identification with the largest matching degree to the terminal.
  • the processor when the processor executes the computer-readable instructions to realize the generation of the medical image recognition model, it is further used to: receive the medical image sent by the terminal; input the medical image into the medical image recognition model to Extracting the image feature corresponding to the medical image through the medical image recognition model, obtaining the image annotation information according to the medical image feature; and pushing the image annotation information to the terminal.
  • One or more computer-readable storage media storing computer-readable instructions.
  • the one or more processors perform the following steps: acquiring medical data; extracting the medical The medical image in the data is extracted from the medical image to obtain the image feature; the medical file in the medical data is extracted, the medical file is parsed to generate annotation information, and the annotation information is searched for in the annotation information and the medical image Corresponding image annotation information; establish a mapping relationship according to the image features and the image annotation information, and generate an image annotation file according to the mapping relationship; and input the image annotation file into a machine learning model to learn all the information through the machine learning model According to the feature relationship between the image feature and the image tag information in the image tag file, a medical image recognition model is generated according to the feature relationship.
  • the computer-readable storage medium may be non-volatile or volatile.
  • the computer-readable instruction when executed by the processor to implement the step of extracting features from the medical image to obtain image features, it is also used to: extract the annotation information in the medical image; and obtain the annotation information Corresponding location coordinates; extracting regional medical images corresponding to the location coordinates from the medical images; and extracting image features of the regional medical images using an image feature extraction algorithm.
  • the computer-readable instruction when executed by the processor, after realizing the parsing of the medical file to generate annotation information, it is further used to: divide the annotation information into unit annotation information according to a preset rule; And matching the unit labeling information with the information in the labeling information database, and extracting the information with the greatest degree of matching as labeling information.
  • the step of establishing a mapping relationship based on the image characteristics and the image annotation information, and generating an image annotation file based on the mapping relationship is further used to: Obtain the image identifier corresponding to the medical image; find the image analysis method corresponding to the image identifier; use the image analysis method to analyze the attribute information associated with the medical image to generate image tag information; and according to the image feature Establishing a mapping relationship between the image annotation information and the image tag information, and generating an image annotation file according to the mapping relationship.
  • the computer-readable instruction when executed by the processor, after the image annotation file is generated according to the mapping relationship, it is further used to: receive a query request sent by the terminal, and extract the query identifier carried in the query request Extracting the image identification corresponding to the image annotation file; calculating the matching degree between the query identification and each of the image identification; and pushing the image annotation file associated with the image identification with the largest matching degree to the terminal.
  • the computer-readable instruction when executed by the processor to realize the generation of the medical image recognition model, it is further used to: receive the medical image sent by the terminal; input the medical image into the medical image recognition model to Extracting the image feature corresponding to the medical image through the medical image recognition model, obtaining the image annotation information according to the medical image feature; and pushing the image annotation information to the terminal.
  • 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.
  • SRAM static RAM
  • DRAM dynamic RAM
  • SDRAM synchronous DRAM
  • DDRSDRAM double data rate SDRAM
  • ESDRAM enhanced SDRAM
  • SLDRAM synchronous chain Channel
  • memory bus Radbus direct RAM
  • RDRAM direct memory bus dynamic RAM
  • RDRAM memory bus dynamic RAM

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Software Systems (AREA)
  • Medical Informatics (AREA)
  • Health & Medical Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)
  • Databases & Information Systems (AREA)
  • Evolutionary Computation (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Radiology & Medical Imaging (AREA)
  • Epidemiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Public Health (AREA)
  • Medical Treatment And Welfare Office Work (AREA)
  • Library & Information Science (AREA)

Abstract

L'invention concerne un procédé de génération d'un modèle de reconnaissance d'image médicale, qui se rapporte au domaine technique de l'intelligence artificielle. Le procédé comprend les étapes consistant : à acquérir des données médicales ; à extraire une image médicale dans les données médicales, et à réaliser une extraction de caractéristiques sur l'image médicale pour obtenir des caractéristiques d'image ; à extraire un fichier médical dans les données médicales, à analyser le fichier médical pour générer des informations d'annotation, et à chercher dans les informations d'annotation des informations d'annotation d'image correspondant à l'image médicale ; à établir une relation de mappage en fonction des caractéristiques d'image et des informations d'annotation d'image, et à générer un fichier d'annotation d'image selon la relation de mappage ; et à entrer le fichier d'annotation d'image dans un modèle d'apprentissage automatique de façon à apprendre la relation des caractéristiques entre les caractéristiques d'image dans le fichier d'annotation d'image et les informations d'annotation d'image au moyen du modèle d'apprentissage automatique, et à générer un modèle de reconnaissance d'image médicale en fonction de la relation des caractéristiques.
PCT/CN2020/097964 2020-02-28 2020-06-24 Procédé et appareil de génération de modèle de reconnaissance d'image médicale, dispositif informatique et support WO2021169101A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202010128465.0 2020-02-28
CN202010128465.0A CN111462865A (zh) 2020-02-28 2020-02-28 医疗影像识别模型生成方法、装置、计算机设备和介质

Publications (1)

Publication Number Publication Date
WO2021169101A1 true WO2021169101A1 (fr) 2021-09-02

Family

ID=71684198

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/097964 WO2021169101A1 (fr) 2020-02-28 2020-06-24 Procédé et appareil de génération de modèle de reconnaissance d'image médicale, dispositif informatique et support

Country Status (2)

Country Link
CN (1) CN111462865A (fr)
WO (1) WO2021169101A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114067964A (zh) * 2021-11-22 2022-02-18 武汉联影医疗科技有限公司 医疗影像数据的处理方法、装置、计算机设备和存储介质

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111816284B (zh) * 2020-09-04 2020-12-25 平安国际智慧城市科技股份有限公司 医疗测试数据的批量生成方法、装置、设备及存储介质
CN113192607B (zh) * 2021-04-13 2024-03-26 复旦大学附属中山医院 标注处理方法、装置、计算机设备和存储介质
CN113744845A (zh) * 2021-09-17 2021-12-03 平安好医投资管理有限公司 基于人工智能的医学影像处理方法、装置、设备及介质
CN114095757A (zh) * 2021-11-17 2022-02-25 南通市肿瘤医院 基于云端的医院放射科自学习影像传输系统

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110022615A1 (en) * 2009-07-21 2011-01-27 National Taiwan University Digital data processing method for personalized information retrieval and computer readable storage medium and information retrieval system thereof
CN110503151A (zh) * 2019-08-26 2019-11-26 北京推想科技有限公司 一种影像的处理方法和系统
CN110737714A (zh) * 2019-10-18 2020-01-31 西安电子科技大学 基于计算机视觉的电竞数据可视化系统及方法
CN110796180A (zh) * 2019-10-12 2020-02-14 吉林大学 一种基于人工智能的模型训练系统与方法

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109063740A (zh) * 2018-07-05 2018-12-21 高镜尧 超声影像关键目标的检测模型构建及检测方法、装置
CN110598782B (zh) * 2019-09-06 2020-07-07 上海杏脉信息科技有限公司 一种训练用于医学影像的分类网络的方法与装置

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110022615A1 (en) * 2009-07-21 2011-01-27 National Taiwan University Digital data processing method for personalized information retrieval and computer readable storage medium and information retrieval system thereof
CN110503151A (zh) * 2019-08-26 2019-11-26 北京推想科技有限公司 一种影像的处理方法和系统
CN110796180A (zh) * 2019-10-12 2020-02-14 吉林大学 一种基于人工智能的模型训练系统与方法
CN110737714A (zh) * 2019-10-18 2020-01-31 西安电子科技大学 基于计算机视觉的电竞数据可视化系统及方法

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114067964A (zh) * 2021-11-22 2022-02-18 武汉联影医疗科技有限公司 医疗影像数据的处理方法、装置、计算机设备和存储介质

Also Published As

Publication number Publication date
CN111462865A (zh) 2020-07-28

Similar Documents

Publication Publication Date Title
WO2021169101A1 (fr) Procédé et appareil de génération de modèle de reconnaissance d'image médicale, dispositif informatique et support
WO2021068321A1 (fr) Procédé et appareil de poussée d'informations basés sur l'interaction homme-machine et dispositif informatique
WO2021184567A1 (fr) Procédé et appareil d'interrogation de dossier de santé électronique, dispositif informatique et support de stockage
CN108899064A (zh) 电子病历生成方法、装置、计算机设备和存储介质
CN112015917A (zh) 基于知识图谱的数据处理方法、装置及计算机设备
WO2020177365A1 (fr) Procédé et appareil de traitement de données d'assurance sociale fondés sur une exploration de données, et dispositif informatique
CN112800766A (zh) 基于主动学习的中文医疗实体识别标注方法及系统
CN110580942A (zh) 一种新型体检报告生成方法、装置、介质及终端设备
CN113111162A (zh) 科室推荐方法、装置、电子设备及存储介质
CN112530550A (zh) 影像报告生成方法、装置、计算机设备和存储介质
CN114358001A (zh) 诊断结果的标准化方法及其相关装置、设备和存储介质
CN113435200A (zh) 实体识别模型训练、电子病历处理方法、系统及设备
CN113903422A (zh) 医疗影像诊断报告实体提取方法、装置及设备
CN109065015B (zh) 一种数据采集方法、装置、设备及可读存储介质
CN113111660A (zh) 数据处理方法、装置、设备和存储介质
CN116469505A (zh) 数据处理方法、装置、计算机设备及可读存储介质
CN115859984B (zh) 医疗命名实体识别模型训练方法、装置、设备及介质
CN112037904A (zh) 在线诊疗数据处理方法、装置、计算机设备和存储介质
Nagarajan et al. Content-based medical image annotation and retrieval using perceptual hashing algorithm
CN116719840A (zh) 一种基于病历后结构化处理的医疗信息推送方法
CN115662562A (zh) 病历诊疗数据管理方法、装置、设备及存储介质
CN111724873B (zh) 一种数据处理方法及装置
Li et al. Extrinsic factors affecting the accuracy of biomedical NER
CN113343680B (en) Structured information extraction method based on multi-type medical record text
Shivamurthy Procedures Design and Development of Framework for Content Based Image Retrieval

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: 20921773

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 16/01/2023)

122 Ep: pct application non-entry in european phase

Ref document number: 20921773

Country of ref document: EP

Kind code of ref document: A1