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WO2019047366A1 - Système et procédé de reconnaissance d'image basée sur l'intelligence artificielle - Google Patents

Système et procédé de reconnaissance d'image basée sur l'intelligence artificielle Download PDF

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Publication number
WO2019047366A1
WO2019047366A1 PCT/CN2017/110477 CN2017110477W WO2019047366A1 WO 2019047366 A1 WO2019047366 A1 WO 2019047366A1 CN 2017110477 W CN2017110477 W CN 2017110477W WO 2019047366 A1 WO2019047366 A1 WO 2019047366A1
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Prior art keywords
image
image recognition
instruction
recognition result
recognition
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Ceased
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PCT/CN2017/110477
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English (en)
Chinese (zh)
Inventor
姚育东
钱唯
郑斌
马贺
齐守良
赵明芳
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Shenzhen Qianhai AnyCheck Information Technology Co Ltd
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Shenzhen Qianhai AnyCheck Information Technology Co Ltd
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Publication of WO2019047366A1 publication Critical patent/WO2019047366A1/fr
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06F16/51—Indexing; Data structures therefor; Storage structures

Definitions

  • the present invention relates to the field of medical image processing and recognition technologies, and in particular, to an image recognition system and method based on artificial intelligence.
  • the main object of the present invention is to provide an image recognition system and method based on artificial intelligence, which can obtain a screening image recognition result by simply inputting a simple image recognition instruction, simplifying the operation of screening image recognition, and improving The efficiency and accuracy of image recognition.
  • the present invention provides an image recognition system based on artificial intelligence, which is applied to a medical terminal device, and the medical terminal device is connected to an image collection terminal and a medical information platform through a communication network, and the manual is based on artificial
  • the intelligent image recognition system includes: an image acquisition module, configured to acquire an image signal of the object to be detected from the image capturing terminal, process the image signal of the object to be detected into a screening image, and receive an image recognition instruction from an input unit of the medical terminal device. ;
  • an image matching module for matching and screening image similarity in an image database of a medical information platform Image data within a preset range
  • the image recognition module is configured to match, according to the image recognition instruction, a text corresponding to the image recognition instruction in the instruction recognition library of the medical information platform as the image recognition result.
  • the image capturing terminal includes an infrared generator, an infrared receiver, an analog to digital converter, and a communication port, wherein: the infrared generator is configured to generate infrared light and inspect the infrared light to the body of the object to be detected.
  • the infrared receiver is configured to collect an infrared light signal transmitted through a body part of the body to be detected into an analog electrical signal containing body tissue structure information of the body to be detected;
  • the analog to digital converter is used to The analog electrical signal analog to the body tissue structure information of the body to be detected is converted into an image signal in the form of a digital signal;
  • the communication port is configured to transmit an image signal of the person to be detected to the medical terminal device.
  • the image recognition instruction is a first identification mark, a second identification mark or a third identification mark, wherein:
  • the image recognition module matches the corresponding text in the instruction recognition library according to the first identification mark as a normal image recognition result
  • the image recognition module matches the corresponding character in the instruction recognition library as a reference image recognition result according to the second identification mark;
  • the image recognition module matches the corresponding character in the command recognition library according to the third identification mark as an abnormal image recognition result.
  • the artificial intelligence-based image recognition system further includes a result indication module, configured to identify a normal image recognition result, an abnormal image recognition result, or a reference image recognition result in the image recognition report.
  • a result indication module configured to identify a normal image recognition result, an abnormal image recognition result, or a reference image recognition result in the image recognition report.
  • the image recognition module is further configured to receive the image recognition result input by the doctor and add the image recognition result to the image recognition report. And adding the input image recognition result to the instruction recognition library.
  • the present invention also provides an image recognition method based on artificial intelligence, which is applied to a medical terminal device, and the medical terminal device is connected to the image capturing terminal and the medical information platform through a communication network, and the method includes the following steps: from the image capturing terminal Obtaining an image signal of the person to be tested;
  • processing the image signal of the person to be detected as a screening image [0015] processing the image signal of the person to be detected as a screening image; [0016] receiving an image recognition instruction from an input unit of the medical terminal device;
  • the step of acquiring an image signal of the object to be detected from the image capturing terminal comprises: generating infrared light by using an infrared generator of the image capturing terminal and seeing the infrared light to a body part of the body to be detected;
  • the infrared receiver of the image capturing terminal collects the infrared light signal transmitted through the body part of the body to be detected into an analog electrical signal containing the tissue structure information of the body part of the body to be detected;
  • the analog to digital converter using the image capturing terminal will contain
  • the analog electrical signal analogy of the body part organ structure information of the subject to be detected is converted into an image signal in the form of a digital signal; the communication port of the image capturing terminal is used to transmit the image signal of the subject to be detected to the medical terminal device.
  • the image recognition instruction is a first identification mark, a second identification mark or a third identification mark, and the matching the image corresponding to the image recognition instruction in the instruction recognition library of the medical information platform according to the image recognition instruction
  • the steps as the result of image recognition include:
  • the image recognition instruction is the third identification mark ⁇
  • the corresponding character is matched in the instruction recognition library according to the third identification mark as an abnormal image recognition result.
  • the artificial intelligence-based image recognition method further comprises the following steps: identifying a normal image recognition result, an abnormal image recognition result or a reference image recognition result in the image recognition report
  • the artificial intelligence-based image recognition method further comprises the steps of: receiving an image recognition result input by the doctor when the image recognition result corresponding to the image recognition instruction is not matched in the instruction recognition library And added to the image recognition report, and the input image recognition result is added to the instruction recognition library.
  • the artificial intelligence-based image recognition system and method of the present invention can be used in a preset instruction recognition library according to a doctor's input image recognition instruction during a process of screening image recognition by a doctor.
  • the image recognition result corresponding to the image recognition instruction is matched, and the image data with the similarity of the image similarity in the preset image is matched in the preset image database, and the image recognition report is generated according to the image recognition result and the image data. Since the doctor only needs to input a simple image recognition command, the recognition result of the screening image can be obtained without the doctor inputting the cumbersome examination conclusion, thereby simplifying the operation of the image recognition of the body part organ and improving the efficiency of image recognition of the body part organ. And accuracy, assist doctors to improve the efficiency and accuracy of body part disease detection and screening, and improve the social efficiency of body partal organ screening.
  • FIG. 1 is a schematic diagram of an application environment of a preferred embodiment of an artificial intelligence-based image recognition system according to the present invention
  • FIG. 2 is a flow chart of a preferred embodiment of an artificial intelligence based image recognition method according to the present invention.
  • FIG. 1 is a schematic diagram of an application environment of a preferred embodiment of an artificial intelligence-based image recognition system according to the present invention.
  • the artificial intelligence-based image recognition system 10 is installed and operated in the medical terminal device 1.
  • the medical terminal device 1 establishes a communication connection with the medical information platform 2 and the image capturing terminal 4 via the communication network 3.
  • the medical terminal device 1 is disposed at a body part organ examination center Or a doctor's workstation computer or server of a large hospital, such as a computing device with data processing and communication functions.
  • the medical information platform 2 can be a server in a medical information system platform, and the medical information platform 2 includes an instruction identification library 21 and an image database 22.
  • the preset instruction recognition library 21 pre-collects medical terms, radiology terms, and common image recognition result templates commonly used for body part organ health screening, and in general, the template corresponds to Identify the results for normal images.
  • the image database 22 stores various normal or abnormal image data that are commonly used for reference. After receiving the screening image, the image data in the screening image and the image database can be similarly matched.
  • the communication network 3 may be an internet network including a local area network, a wide area network, or a wireless transmission network including GSM, GPRS, and CDMA.
  • the body part organ image collecting terminal 4 is disposed in a medical examination institution such as a community medical workstation, and establishes a network communication connection with the medical terminal device i.
  • the body part organ image collecting terminal 4 includes an infrared generator 41, an infrared receiver 42, an analog to digital converter 43, and a communication port 44.
  • the infrared generator 41 generates infrared light and sees the infrared light onto a body part organ of the body to be detected; the infrared receiver 42 collects infrared light signals transmitted through the body part of the body to be detected and processes the body part organ structure The analog electrical signal of the information; the infrared light generated by the infrared generator 41 is fluorinated to the body part of the body to be detected, and the infrared light signal received by the infrared receiver 42 carries the infrared transmitted light of the body tissue structure information of the body.
  • the analog-to-digital converter 43 analog-to-digital converts the analog electrical signal containing the body tissue structure information of the body to be detected collected by the infrared receiver 42 into an image signal in the form of a digital signal; the communication port 44 is used for the person to be detected The information and the digital signal containing the body part organ image information of the subject to be detected are transmitted to the cloud server 1 through the communication network 3.
  • the communication port 44 can be a wireless communication interface with remote wireless communication functions, such as a communication interface supporting GSM, GPRS, CDMA.
  • the medical terminal device 1 includes, but is not limited to, an artificial intelligence based image recognition system 10, an input unit 11, a storage unit 12, a processing unit 13, a communication unit 14, and a display unit 15 .
  • the input unit 11, the storage unit 12, the processing unit 13, the communication unit 14, and the display unit 15 are all connected to the processing unit 13 through a data bus, and can perform information interaction with the artificial intelligence-based image recognition system 10 through the processing unit 13.
  • the input unit 11 can be a hardware device such as a keyboard, a touch screen or a mouse.
  • the storage unit 12 is a read only memory unit ROM, an electrically erasable storage unit EEPROM or Flash memory unit FLASH and other memory.
  • the processing unit 13 is a central processing unit (CPU), a microprocessor, a microcontroller (MCU), a data processing chip, or an information processing unit having a data processing function.
  • the communication unit 14 can be a wireless communication interface with remote wireless communication functions, such as a communication interface supporting GSM, GPRS, CDMA.
  • the display unit 15 is a display for displaying a body part organ inspection report of the person to be examined.
  • the artificial intelligence-based image recognition system 10 includes, but is not limited to, an image acquisition module 101, an image matching module 102, an image recognition module 103, and a result indication module 104.
  • the module referred to in the present invention refers to a series of computer program instruction segments that can be executed by the processing unit 13 of the medical terminal device 1 and that can perform a fixed function, which are stored in the storage unit 12 of the medical terminal device 1. .
  • the image acquisition module 101 is configured to acquire an image signal of the object to be detected from the image capturing terminal 4.
  • the infrared generator 41 generates infrared light and sees the infrared light on a body part of the body to be detected;
  • the infrared receiver 42 collects an infrared light signal passing through a body part of the body to be detected. And processing the analog electrical signal as the body tissue structure information of the body;
  • the analog-to-digital converter 43 converts the analog electrical signal collected by the infrared receiver 42 and contains the information of the body tissue structure of the body to be detected into a digital signal.
  • the image signal of the form is sent to the image acquisition module 101 by the communication port 44.
  • the image acquisition module 101 is further configured to process the image signal of the object to be detected as a screening image. Specifically, the image acquisition module 101 records the image data of the image to be detected by the digital image processing software in the form of a digital file, and then generates a screening image of the object to be detected according to the image data.
  • the principle of detecting the local body organ of the infrared body is: infrared light illuminates the local organ part of the human body, and the local organ tissue of the human body exhibits different absorption characteristics through the infrared spectrum passing through the body, so the infrared light transmitted through the lesion part
  • the intensity of the infrared signal is different between the light signal and the tissue of the normal body.
  • the acquired gray image of the infrared image, the tissue structure, the external dimensions, especially the optical properties of the body part and body tissues can detect the body. The location and size of the lesion in the local organ site.
  • the image acquisition module 101 is further configured to receive an image recognition instruction from the input unit 11.
  • the doctor inputs from the input unit 11 to check the health of the body part of the body.
  • Image recognition instructions are further configured to be received from the input unit 11.
  • the image recognition module 103 is configured to match the text corresponding to the image recognition instruction in the instruction recognition library 21 of the medical information platform 2 as the image recognition result according to the image recognition instruction.
  • the image recognition instruction may be a first identification mark, a second identification mark or a third identification mark.
  • the image recognition instruction includes a preset first identification mark, and the first identification mark is used as an indicator for determining whether the image recognition result is normal, that is, when the image recognition instruction includes the first identification mark ⁇ ,
  • the image recognition module 103 determines that the image recognition result of the screening image is normal, and the content of the first identification mark is generally selected as a word different from related medical terms and radiological terms.
  • the image recognition instruction when the image recognition instruction includes "click + body part organ", it indicates that the image recognition result of the screening image is normal, and therefore, the text corresponding to the normal health of the body part of the body can be matched in the instruction recognition library 21 as a normal
  • the image recognition result for example, matches "the body part of the organ is not abnormal" as a normal image recognition result.
  • the image recognition instruction includes a preset second identification mark, and the second identification mark is used as an indicator for determining whether the image recognition result is a reference image recognition result, that is, when the image recognition instruction includes the second identification mark.
  • the image recognition module 103 needs to match the image recognition result corresponding to the screening image, and the image recognition module 103 matches the image corresponding to the screening image in the instruction recognition library 21 as the reference image recognition result according to the second identification flag.
  • the content of the second identification mark may be set as a "reference” or the like which can clearly determine the image recognition result whose reference image recognition result is a reference. For example, when the image recognition command is "reference", the character corresponding to the conclusion of the image recognition report is matched in the command recognition library, and the character is used as the reference image recognition result.
  • the image recognition module 103 may determine that the image recognition result is not a normal image recognition result, and match the corresponding text in the instruction recognition library according to the third identification mark.
  • the matching text corresponding to the third identification mark has nothing to do with the image recognition result of the screening image, it indicates that the received image recognition instruction is invalid; on the contrary, it indicates that the image recognition result corresponding to the image recognition instruction is an abnormal image recognition result.
  • the recognized image recognition command is "a number of soft tissue density lesions visible on both sides of the breast, and the morphology conforms to the body part organs"
  • the corresponding text is matched in the command recognition library, and the text is used as an abnormal image recognition result.
  • the image recognition module 103 if the image recognition module 103 does not match the image recognition result corresponding to the image recognition instruction in the instruction recognition library 21, it indicates that the corresponding file is not stored in the instruction identification library 21, Thereafter, the doctor can input the corresponding image recognition result into the image recognition report through the input unit 11, and The image recognition module 103 adds the input image recognition result to the instruction recognition library 21, so as to match the corresponding image recognition result from the instruction recognition library 21 after receiving the same or similar image recognition instruction next time.
  • the image matching module 102 is configured to match the image data in the preset image database 22 with the image similarity within the preset range, and add the matched image data to the corresponding image recognition report. in.
  • the similar image data in the image database 22 is acquired as the image data corresponding to the screening image, and the preset range may be customized according to the relevant parts of the breast, for example, set to be greater than 80%.
  • the image matching module 102 matches the image data corresponding to the screening image, the matched image data is added to the corresponding image recognition report.
  • the image recognition report not only includes the image recognition result of the screening image, but also the image data of the screening image for reference by the doctor and the examinee.
  • the result indication module 104 is configured to identify a normal image recognition result, an abnormal image recognition result, or a reference image recognition result in the image recognition report, and display the image recognition report on the display unit 15 in a preset display manner.
  • Normal image recognition results, abnormal image recognition results, or reference image recognition results are different identification on the normal image recognition result and the abnormal image recognition result according to whether the image recognition result is normal or abnormal, and the reference result can be identified as being distinguishable from the normal or abnormal image recognition result.
  • Other identifiers such as marking different image recognition results as different fonts or different colors, so as to display in a preset display manner after display, for example, the text content of the abnormal image recognition result is in bold form Display or highlight.
  • the text content corresponding to the recognition template may be identified as blue after matching the recognition template indicating that the image recognition result is normal. If the image recognition result in the image recognition report is an abnormal image recognition result, the text content may be identified as a red font after being matched with the text corresponding to the abnormal image recognition result, and displayed. The text content of the red font is displayed in bold or highlighted to remind the examinee to pay attention.
  • the normal image recognition result or the abnormal image recognition result in the image recognition report is differently identified, and the normal image recognition result or the abnormal image recognition result is displayed in a preset display manner, so that The image recognition report is more clear and easy for doctors and subjects to read.
  • the present invention also provides an image recognition method based on artificial intelligence, which is applied to a medical terminal device.
  • FIG. 2 is a flow chart of a preferred embodiment of the image recognition method based on artificial intelligence according to the present invention.
  • the image recognition method based on artificial intelligence includes the following steps: [0043] Step S21, the image acquisition module 101 acquires an image signal of the object to be detected from the image acquisition terminal 4, and is to be detected.
  • the image signal processing is to screen the image; in the embodiment, the infrared generator 41 generates infrared light and sees the infrared light on the body part of the body to be detected; the infrared receiver 42 collects the body through the person to be tested
  • the infrared light signal of the local organ is processed as an analog electrical signal of body tissue structure information of the body; the analog-to-digital converter 43 converts the analog electrical signal of the local organ structure information of the body of the subject to be detected by the infrared receiver 42 Processing is an image signal in the form of a digital signal; the communication port 44 transmits the image signal of the person to be detected to the image acquisition module 101.
  • the image acquisition module 101 records the image data of the image to be detected by the digital image processing software in the form of a digital file, and then generates a screening image of the object to be detected according to the image data.
  • Step S22 The image matching module 102 matches the image data in the preset image database with the image similarity within the preset range, and adds the matched image data to the corresponding image recognition report.
  • the image database 22 stores various commonly used normal or abnormal image data for reference.
  • the image matching module 102 can perform screening on the image and image database.
  • the image data is similarly matched.
  • the similar image data in the image database is acquired as the image data corresponding to the screening image, and the preset range may be customized according to the breast related portion, for example, set to be greater than 80%.
  • the image matching module 102 After matching the image data corresponding to the screening image, the image matching module 102 adds the image data according to the image data to the corresponding image recognition report.
  • the image recognition report includes not only the image recognition result of the screening image but also the image data of the screening image for reference by the doctor and the examinee.
  • Step S23 the image acquisition module 101 receives an image recognition instruction from the input unit 11 of the medical terminal device.
  • the doctor inputs an image recognition command for checking the health condition of the body part from the input unit 11.
  • Step S24 the image recognition module 103 matches the character corresponding to the image recognition instruction in the command recognition library 21 as the image recognition result according to the image recognition instruction.
  • the image recognition instruction may be a first identification mark, a second identification mark or a third identification mark; the image recognition result may be a normal image recognition result, a reference image recognition result, or an abnormal image. Identify the results.
  • the image recognition instruction includes a preset first identification mark, and the first identification mark is used as an indicator for determining whether the image recognition result is normal, that is, the first identification mark is included in the image recognition instruction.
  • the image recognition module 103 determines that the image recognition result of the screening image is normal, and the content of the first identification mark is generally selected as a word different from related medical terms and radiological terms. For example, when the image recognition instruction includes "click + body part organ", it indicates that the image recognition result of the screening image is normal, and thus, the instruction recognition library 21 can match the text corresponding to the health of the body part organ, for example, matching. "There is no abnormality in the local organs of the body” as a result of normal image recognition.
  • the image recognition instruction includes a preset second identification mark, and the second identification mark is used as an indicator for determining whether the image recognition result is a reference image recognition result, that is, when the image recognition instruction includes the second identification mark.
  • the image recognition module 103 needs to match the image recognition result corresponding to the screening image, and the image recognition module 103 matches the image corresponding to the screening image in the instruction recognition library 21 as the reference image recognition result according to the second identification flag.
  • the content of the second identification mark can be set to a word such as "reference” which can clearly determine the image recognition result whose indication is the reference image recognition result. For example, when the image recognition command is "reference", the character corresponding to the conclusion of the image recognition report is matched in the command recognition library 21, and the character is used as a reference image recognition result.
  • the image recognition module 103 may determine that the image recognition result is not a normal image recognition result, and match the corresponding text in the instruction recognition library 21 according to the third identification mark.
  • the matching text corresponding to the third identification mark has nothing to do with the image recognition result of the screening image, it indicates that the received image recognition instruction is invalid; on the contrary, it indicates that the image recognition result corresponding to the image recognition instruction is abnormal.
  • Image recognition results For example, if the recognized image recognition command is "a number of soft tissue densities visible on both sides of the breast, and the morphology is consistent with the body part organs", the corresponding recognition text is matched in the instruction recognition library 21, and the text is used as an abnormal image recognition result.
  • the image recognition module 103 if the image recognition module 103 does not match the image recognition result corresponding to the image recognition instruction in the instruction recognition library 21, it indicates that the corresponding file is not stored in the instruction recognition library 21, and The doctor can input the corresponding image recognition result into the image recognition report through the input unit 11, and the image recognition module 103 adds the input image recognition result to the instruction recognition library 21, so as to receive the same or similar image recognition next time.
  • the command ⁇ , the corresponding image recognition result is matched from the command recognition library 21.
  • Step S25 the result indication module 104 identifies the normal image recognition result, the abnormal image recognition result or the reference image recognition result in the image recognition report, and displays the preset display mode on the display unit 15 A normal image recognition result, an abnormal image recognition result, or a reference image recognition result in the image recognition report is displayed.
  • the result indication module 104 performs different identification on the normal image recognition result and the abnormal image recognition result according to whether the image recognition result is normal or abnormal, and the reference result can be identified as being distinguishable from the normal or abnormal image recognition result.
  • Other identifiers such as marking different image recognition results as different fonts or different colors, so that after display, the display unit 15 displays the display in a preset manner, for example, the text content of the abnormal image recognition result is Displayed in bold or highlighted.
  • the text content identifier corresponding to the recognition template may be matched after matching the recognition template representing that the image recognition result is normal. It is a blue font and is displayed normally; if the image recognition result in the image recognition report is an abnormal image recognition result, the text content may be identified as a red font after matching the text corresponding to the abnormal image recognition result, and In the display, the text content of the red font is displayed in bold or highlighted to remind the examinee to pay attention.
  • the normal image recognition result or the abnormal image recognition result in the image recognition report is differently identified, and the normal image recognition result or the abnormal image recognition result is displayed in a preset display manner, so that The image recognition report is more clear and easy for doctors and examiners to read.
  • the body part organ screening intelligent identification system and method of the present invention can match and image in a preset instruction recognition library according to a doctor inputting a simple image recognition instruction during a process of screening image recognition by a doctor. Identifying the image recognition result corresponding to the instruction, matching the image data in the preset image database with the image similarity within the preset range, and generating the image recognition report according to the image recognition result and the image data, since the doctor only needs to input the simple
  • the image recognition command can obtain the recognition result of the screening image without the doctor inputting the cumbersome examination conclusion, thereby simplifying the operation of the image recognition of the body part organ, improving the efficiency and accuracy of the image recognition of the body part organ, thereby assisting Doctors improve the efficiency and accuracy of detection and screening of body parts and diseases, and improve the social efficiency of body partal organ screening.
  • the artificial intelligence-based image recognition system and method of the present invention can match and match the image recognition instruction input by the doctor in the preset instruction recognition library during the process of screening image recognition by the doctor.
  • the image recognition result corresponding to the image recognition instruction matches the image data in the preset image database with the image similarity within the preset range, and generates the image recognition report according to the image recognition result and the image data. Since the doctor only needs to input a simple image recognition command, the recognition result of the screening image can be obtained without the doctor inputting the cumbersome examination conclusion, thereby simplifying the operation of the image recognition of the body part organ and improving the efficiency of image recognition of the body part organ. And accuracy, assist doctors to improve the efficiency and accuracy of body part disease detection and screening, and improve the social efficiency of body partal organ screening.

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Abstract

La présente invention concerne un système et un procédé de reconnaissance d'image basée sur l'intelligence artificielle, qui sont appliqués à des dispositifs de terminal médical, le procédé comprenant les étapes suivantes : acquérir, à partir d'un terminal d'acquisition d'image, un signal d'image d'une personne à détecter; traiter le signal d'image de la personne à détecter en tant qu'image de criblage; dans une base de données d'images d'une plateforme d'informations médicales, mettre en correspondance des données d'image pour lesquelles le degré de similarité avec l'image de criblage se trouve dans une plage prédéfinie; recevoir, à partir d'une unité d'entrée du dispositif de terminal médical, une instruction de reconnaissance d'image; dans une bibliothèque de reconnaissance d'instruction de la plateforme d'informations médicales, mettre en correspondance un texte correspondant à l'instruction de reconnaissance d'image en tant que résultat de reconnaissance d'image en fonction de l'instruction de reconnaissance d'image. Par la mise en œuvre de la présente invention, un médecin a uniquement besoin d'entrer une instruction de reconnaissance d'image simple pour obtenir un résultat de reconnaissance d'une image de criblage, ce qui simplifie ainsi l'opération de reconnaissance d'image de criblage et augmente l'efficacité et la précision de la reconnaissance d'image.
PCT/CN2017/110477 2017-09-11 2017-11-10 Système et procédé de reconnaissance d'image basée sur l'intelligence artificielle Ceased WO2019047366A1 (fr)

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CN109754867A (zh) * 2018-12-18 2019-05-14 杭州深睿博联科技有限公司 用于医疗影像信息的处理方法及装置、服务器、系统
CN109935294A (zh) * 2019-02-19 2019-06-25 广州视源电子科技股份有限公司 一种文本报告输出方法、装置、存储介质及终端
CN111724893B (zh) * 2019-03-20 2024-04-09 宏碁股份有限公司 医疗影像辨识装置及医疗影像辨识方法
CN110459306A (zh) * 2019-08-16 2019-11-15 杭州依图医疗技术有限公司 医学影像显示方法和显示设备

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