CN111739617A - Medical image artificial intelligence quality control marking method, device, equipment and storage medium - Google Patents
Medical image artificial intelligence quality control marking method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN111739617A CN111739617A CN202010544138.3A CN202010544138A CN111739617A CN 111739617 A CN111739617 A CN 111739617A CN 202010544138 A CN202010544138 A CN 202010544138A CN 111739617 A CN111739617 A CN 111739617A
- Authority
- CN
- China
- Prior art keywords
- labeling
- quality control
- artificial intelligence
- personnel
- medical image
- Prior art date
- Legal status (The legal status 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 status listed.)
- Granted
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
- G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
-
- 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
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
-
- 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
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- Biomedical Technology (AREA)
- Public Health (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Medical Informatics (AREA)
- Primary Health Care (AREA)
- Epidemiology (AREA)
- Computational Linguistics (AREA)
- Business, Economics & Management (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Molecular Biology (AREA)
- General Business, Economics & Management (AREA)
- Evolutionary Computation (AREA)
- Computing Systems (AREA)
- Biophysics (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Pathology (AREA)
- Databases & Information Systems (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention relates to the technical field of medical image labeling, in particular to a medical image artificial intelligence quality control labeling method, a device, equipment and a storage medium; the quality control labeling method comprises the following steps: carrying out image annotation on the medical image by a common annotation worker; and the auditing experts carry out quality control on the labeling results of the ordinary labeling personnel. The medical image artificial intelligence quality control labeling method, the device, the equipment and the storage medium disclosed by the invention have the advantages that the experience of experts is better applied, and the quality of image labeling is ensured; meanwhile, the artificial intelligence of image labeling is realized, and the working intensity of doctors is reduced.
Description
Technical Field
The invention relates to the technical field of medical image labeling, in particular to a medical image artificial intelligence quality control labeling method, device, equipment and storage medium.
Background
Symptom monitoring, also called syndrome monitoring, refers to a monitoring method for continuously and systematically collecting and analyzing data of occurrence frequency of clinical syndromes of specific diseases, and timely discovering abnormal aggregation of diseases in time and space distribution so as to perform early exploration, early warning and quick response to disease outbreak. Disease monitoring is the most basic function of a disease prevention and control center, the traditional monitoring work is generally to report the disease condition after diagnosis in a hospital, and the early detection and early warning of epidemic situations are difficult to realize based on the disease monitoring based on the diagnosis; moreover, the existing monitoring work is complex in operation and can be used only by having a certain medical background, so that the monitoring work cannot achieve the expected purpose. For medical images, labeling is usually performed manually, and the quality of image labeling often depends on the working experience of labeling personnel, so that labeling results are uneven, the labeling quality cannot be well controlled, and the subsequent diagnosis results of the images are obtained.
Therefore, in order to solve the above problems, it is urgently needed to invent a new medical image artificial intelligence quality control labeling method, device, equipment and storage medium.
Disclosure of Invention
The invention aims to: a medical image artificial intelligent quality control labeling method, device, equipment and storage medium are provided.
The invention provides the following scheme:
a medical image artificial intelligence quality control labeling method comprises the following steps:
s1, setting a marking data layer for common marking personnel, and collecting and storing image marking data of the medical image on line by the common marking personnel;
s2, setting a quality control data layer of the auditing experts for collecting and storing the online quality control evaluation of the auditing experts on the labeling results of the ordinary labeling personnel;
s3, setting a quality control evaluation artificial intelligence model, and performing image annotation on ordinary annotation personnel by using the quality control evaluation artificial intelligence model to perform quality control, wherein the quality control evaluation artificial intelligence model comprises the following steps:
constructing a quality control evaluation artificial intelligence model; constructing a quality control evaluation convolutional neural network through a quality control evaluation artificial intelligence model;
processing the labeling data of the ordinary labeling personnel to obtain a labeling training set of the ordinary labeling personnel;
processing the quality control data of the audit expert to obtain an audit expert quality control labeling result set;
training a convolutional neural network of the quality control evaluation artificial intelligence model by utilizing a common labeling personnel labeling training set and an auditing expert quality control labeling result set to obtain a trained quality control evaluation artificial intelligence model;
s4, performing quality control on the labeling result of the ordinary labeling personnel by using a quality control evaluation artificial intelligence model, specifically:
if the labeling result of the quality control evaluation artificial intelligent model is consistent with the labeling result of the ordinary labeling personnel, the quality control is qualified;
if the labeling result of the quality control evaluation artificial intelligence model is inconsistent with the labeling result of the ordinary labeling personnel, sending an auditing expert to perform further quality control on the labeling result of the ordinary labeling personnel;
and S5, incorporating the labeling result of the ordinary labeling personnel and the quality control labeling result of the auditing experts into a training set for learning.
Preferably, in step S4, if the annotation result of the quality control evaluation artificial intelligence model is inconsistent with the annotation result of the ordinary annotator, feeding the annotation result of the artificial intelligence model back to the ordinary annotator, and if the annotation result of the artificial intelligence model by the ordinary annotator is inconsistent, sending the annotation result to an auditor for further quality control of the annotation result of the ordinary annotator;
the method comprises the following steps that a common marking person carries out image marking on the medical image, and specifically comprises the following steps:
the ordinary marking personnel browse, draw and measure the medical image, complete the detection, segmentation and attribute information selection of the focus, and classify and standardize the marking result.
The method comprises the following steps that an auditor performs quality control on the labeling result of a common labeling person, and specifically comprises the following steps:
and the auditing expert checks, reviews, supplements and modifies the labeling result of the ordinary labeling personnel.
The quality control labeling mode of the auditing experts comprises a linear quality control mode and a parallel quality control mode:
the linear quality control mode refers to that the labeling result of each ordinary labeling person is subjected to the quality control step of the quality control evaluation artificial intelligence model and the quality control step of the auditing expert and is used in the initial training stage of the model.
The parallel quality control mode refers to that the labeling result of each ordinary labeling person is subjected to the quality control step of the quality control evaluation artificial intelligence model, and only when the labeling result of the ordinary labeling person is inconsistent with the labeling result of the quality control evaluation artificial intelligence model, the labeling result is sent to an auditing expert for quality control, so that the annotation result is used in the middle and later use stages of the model.
Further comprising: further training and perfecting the artificial intelligent model of the medical image according to the expert quality control labeling result;
further, a medical image artificial intelligence quality control labeling device of the medical image artificial intelligence quality control labeling method is also provided, which comprises:
the marking module is used for marking the medical image by common marking personnel;
and the quality control module is used for controlling the quality of the labeling result of the ordinary labeling personnel by the auditing expert.
Further comprising:
the artificial intelligence module is used for training the artificial intelligence model of the medical image according to the expert quality control labeling result; and image labeling is carried out by utilizing the medical image artificial intelligence model.
The marking module, the quality control module and the artificial intelligence module are electrically connected in sequence.
An electronic device comprising a memory and a processor; the memory is used for storing a computer program; the processor executes the computer program in the memory to realize the medical image artificial intelligence quality control labeling method.
A computer readable storage medium stores a computer program, and the computer program is used for realizing the medical image artificial intelligence quality control labeling method when being executed by a processor.
The invention has the following beneficial effects:
the invention discloses a medical image artificial intelligence quality control labeling method, a device, equipment and a storage medium, wherein the quality control labeling method comprises the following steps: carrying out image annotation on the medical image by a common annotation worker; the auditing expert performs quality control on the labeling result of the ordinary labeling personnel; the experience of experts is better applied, and the quality of image labeling is ensured; meanwhile, the artificial intelligence of image labeling is realized, and the working intensity of doctors is reduced.
Drawings
Fig. 1 is a flow chart of the medical image artificial intelligence quality control labeling method of the present invention.
FIG. 2 is a flow chart of the neural network machine translation training method based on word classes according to the present invention.
FIG. 3 is a block diagram of the neural network machine translation system based on word classes according to the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Referring to fig. 1, a medical image artificial intelligence quality control labeling method includes the following steps:
s1, setting a marking data layer for common marking personnel, and collecting and storing image marking data of the medical image on line by the common marking personnel;
s2, setting a quality control data layer of the auditing experts for collecting and storing the online quality control evaluation of the auditing experts on the labeling results of the ordinary labeling personnel;
s3, setting a quality control evaluation artificial intelligence model, and performing image annotation on ordinary annotation personnel by using the quality control evaluation artificial intelligence model to perform quality control, wherein the quality control evaluation artificial intelligence model comprises the following steps:
constructing a quality control evaluation artificial intelligence model; constructing a quality control evaluation convolutional neural network through a quality control evaluation artificial intelligence model;
processing the labeling data of the ordinary labeling personnel to obtain a labeling training set of the ordinary labeling personnel;
processing the quality control data of the audit expert to obtain an audit expert quality control labeling result set;
training a convolutional neural network of the quality control evaluation artificial intelligence model by utilizing a common labeling personnel labeling training set and an auditing expert quality control labeling result set to obtain a trained quality control evaluation artificial intelligence model;
s4, performing quality control on the labeling result of the ordinary labeling personnel by using a quality control evaluation artificial intelligence model, specifically:
if the labeling result of the quality control evaluation artificial intelligent model is consistent with the labeling result of the ordinary labeling personnel, the quality control is qualified;
if the labeling result of the quality control evaluation artificial intelligence model is inconsistent with the labeling result of the ordinary labeling personnel, sending an auditing expert to perform further quality control on the labeling result of the ordinary labeling personnel;
and S5, incorporating the labeling result of the ordinary labeling personnel and the quality control labeling result of the auditing experts into a training set for learning.
The method comprises the following steps that a common marking person carries out image marking on the medical image, and specifically comprises the following steps:
the ordinary marking personnel browse, draw and measure the medical image to complete the detection, segmentation and attribute information selection of the focus.
The method comprises the following steps that an auditor performs quality control on the labeling result of a common labeling person, and specifically comprises the following steps:
and the auditing expert checks, reviews and modifies the labeling result of the ordinary labeling personnel.
The quality control labeling mode of the auditing experts comprises a linear quality control mode and a parallel quality control mode.
Further comprising:
training the artificial intelligent model of the medical image according to the expert quality control labeling result;
and carrying out image annotation by the medical image artificial intelligence model.
Referring to fig. 2, an artificial intelligence quality control labeling device for medical images of the artificial intelligence quality control labeling method for medical images includes:
the marking module is used for marking the medical image by common marking personnel;
and the quality control module is used for controlling the quality of the labeling result of the ordinary labeling personnel by the auditing expert.
Further comprising:
the artificial intelligence module is used for training the artificial intelligence model of the medical image according to the expert quality control labeling result; and image labeling is carried out by utilizing the medical image artificial intelligence model.
The marking module, the quality control module and the artificial intelligence module are electrically connected in sequence.
Referring to fig. 3, an electronic device includes a memory 1 and a processor 2; the memory is used for storing a computer program; the processor executes the computer program in the memory to realize the medical image artificial intelligence quality control labeling method.
Further, a computer readable storage medium is provided, which stores a computer program, and the computer program is used for implementing the artificial intelligence quality control labeling method for medical images when being executed by a processor.
The medical image artificial intelligence quality control labeling method, device, equipment and storage medium in the embodiment, the quality control labeling method comprises the following steps: carrying out image annotation on the medical image by a common annotation worker; the auditing expert performs quality control on the labeling result of the ordinary labeling personnel; the experience of experts is better applied, and the quality of image labeling is ensured; meanwhile, the artificial intelligence of image labeling is realized, and the working intensity of doctors is reduced.
In the artificial intelligence quality control labeling device for medical images in this embodiment, the image labeling module includes:
(1) detecting a doctor homepage: and realizing login, exit, checking uncompleted tasks and checking a completed task list.
(2) Labeling the workbench:
A. and (3) task loading: and automatically loading a marking service template which accords with the task.
B. Labeling tool sets: including tools related to annotation, measurement, and basic image manipulation tools.
C. Information viewing and supplementing: the information review is used to review the medical history and the information supplement is used to fill in the negative of the lesion, the positive and save the submission.
D. And (3) storage: and saving the labeling result.
E. Submitting: and submitting the labeling result.
The expert quality control module comprises:
(1) expert quality control doctor homepage: and realizing login, exit, checking uncompleted tasks and checking a completed task list.
(2) Expert quality control tool table:
A. and (3) task loading: and automatically loading an arbitration business template which accords with the task.
B. Labeling tool sets: including tools related to annotation, measurement, and basic image manipulation tools.
C. Information viewing and supplementing: looking up the medical history; viewing a focus marking list according to layers; view a detailed list of individual lesions; information supplementation is used to fill out the negative, positive and save submissions of the lesion.
D. And (4) auditing opinions: the quality control expert can select the annotation or re-annotation of the annotation doctor and save the annotation as the audit opinion.
E. And (3) storage: and saving the labeling result.
F. Submitting: and submitting the labeling result.
The medical image artificial intelligence quality control labeling device in this embodiment further includes: a background management module comprising:
(1) administrator homepage: and (6) logging in and exiting.
(2) Background personnel management and authority management:
A. adding, deleting, modifying and checking by managers: create, modify, delete, and grant rights to users.
B. And (3) administrator authority configuration: the authority can be created according to all modules in the background, so that the manager can see different functions after logging in.
(3) Doctor management: and creating doctors according to the information of the names, the hospitals, the belonged categories and the like of the doctors, and supporting modification, inquiry, deletion and recovery.
(4) Hospital or institutional administration: and establishing the hospital or the institution according to information such as the name of the hospital or the institution, and supporting modification, inquiry, deletion and recovery.
(5) The disease species configuration establishes disease species according to parts, supports single selection, multiple selection and gap filling, supports modification, inquiry, deletion and deletion recovery.
(6) Parameter management: some common parameter management is placed on the module, for example: and information management such as position, item type, mark type and the like supports modification, inquiry, deletion and deletion recovery.
(7) And (3) flow management: because all disease types or all medical image labels do not have a uniform flow, the flow is created according to different requirements, and modification, inquiry, deletion and deletion recovery are supported.
(8) Task management: and according to the selected data, filling task information, selecting disease types, selecting processes, selecting doctors and other information to create tasks, and supporting modification, inquiry, deletion and recovery.
(9) Managing medical records: the functional medical image data management function uploads data to store data according to the information of the data filling data. Query, delete, and delete recovery are supported.
(10) And (4) operation recording: and checking the operation records of doctor labeling and quality control.
The medical image artificial intelligence quality control labeling device in the embodiment further comprises an artificial intelligence module, wherein the artificial intelligence module is used for training the medical image artificial intelligence model according to the expert quality control labeling result; image marking is carried out by utilizing a medical image artificial intelligence model; the automation of image labeling is realized, and the labor intensity of doctors is reduced.
For simplicity of explanation, the method embodiments are described as a series of acts or combinations, but those skilled in the art will appreciate that the embodiments are not limited by the order of acts described, as some steps may occur in other orders or concurrently with other steps in accordance with the embodiments of the invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A medical image artificial intelligence quality control labeling method is characterized by comprising the following steps:
s1, setting a marking data layer for common marking personnel, and collecting and storing image marking data of the medical image on line by the common marking personnel;
s2, setting a quality control data layer of the auditing experts for collecting and storing the online quality control evaluation of the auditing experts on the labeling results of the ordinary labeling personnel;
s3, setting a quality control evaluation artificial intelligence model, and performing image annotation on ordinary annotation personnel by using the quality control evaluation artificial intelligence model to perform quality control, wherein the quality control evaluation artificial intelligence model comprises the following steps:
constructing a quality control evaluation artificial intelligence model; constructing a quality control evaluation convolutional neural network through a quality control evaluation artificial intelligence model;
processing the labeling data of the ordinary labeling personnel to obtain a labeling training set of the ordinary labeling personnel;
processing the quality control data of the audit expert to obtain an audit expert quality control labeling result set;
training a convolutional neural network of the quality control evaluation artificial intelligence model by utilizing a common labeling personnel labeling training set and an auditing expert quality control labeling result set to obtain a trained quality control evaluation artificial intelligence model;
s4, performing quality control on the labeling result of the ordinary labeling personnel by using a quality control evaluation artificial intelligence model, specifically:
if the labeling result of the quality control evaluation artificial intelligent model is consistent with the labeling result of the ordinary labeling personnel, the quality control is qualified;
if the labeling result of the quality control evaluation artificial intelligence model is inconsistent with the labeling result of the ordinary labeling personnel, sending an auditing expert to perform further quality control on the labeling result of the ordinary labeling personnel;
and S5, incorporating the labeling result of the ordinary labeling personnel and the quality control labeling result of the auditing experts into a training set for learning.
2. The medical image artificial intelligence quality control labeling method according to claim 1, wherein a general labeling person performs image labeling on the medical image, specifically comprising:
the ordinary marking personnel browse, draw and measure the medical image to complete the detection, segmentation and attribute information selection of the focus.
3. The medical image artificial intelligence quality control labeling method according to claim 2, wherein the step of performing quality control on the labeling result of a general labeling person by an audit expert comprises the following steps:
and the auditing expert checks, reviews, supplements and modifies the labeling result of the ordinary labeling personnel.
4. The medical image artificial intelligence quality control labeling method according to claim 1, wherein in step S4, if the labeling result of the quality control evaluation artificial intelligence model is inconsistent with the labeling result of a general labeling person:
firstly, the labeling result of the artificial intelligence model is fed back to ordinary labeling personnel, and if the ordinary labeling personnel have inconsistent comments on the labeling result of the artificial intelligence model, the comments are sent to an auditing expert to further control the quality of the labeling result of the ordinary labeling personnel.
5. The medical image artificial intelligence quality control labeling method according to claim 3, wherein the quality control mode comprises a linear quality control mode and a parallel quality control mode.
6. A medical image artificial intelligence quality control labeling device for realizing the medical image artificial intelligence quality control labeling method according to claim 1, comprising:
the marking module is used for marking the medical image by common marking personnel;
and the quality control module is used for controlling the quality of the labeling result of the ordinary labeling personnel by the auditing expert.
7. The medical image artificial intelligence quality control labeling device of claim 6, further comprising:
the artificial intelligence module is used for training the artificial intelligence model of the medical image according to the expert quality control labeling result; and image labeling is carried out by utilizing the medical image artificial intelligence model.
8. The medical image artificial intelligence quality control labeling device of claim 7, wherein the labeling module, the quality control module and the artificial intelligence module are electrically connected in sequence.
9. An electronic device, characterized in that: comprising a memory and a processor; the memory is used for storing a computer program; the processor executes the computer program in the memory to realize the artificial intelligence quality control labeling method for medical images according to any one of claims 1 to 5.
10. A computer-readable storage medium characterized by: a computer program is stored, which when executed by a processor, is used for implementing the artificial intelligence quality control labeling method for medical images according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010544138.3A CN111739617B (en) | 2020-06-15 | 2020-06-15 | Medical image artificial intelligence quality control labeling method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010544138.3A CN111739617B (en) | 2020-06-15 | 2020-06-15 | Medical image artificial intelligence quality control labeling method, device, equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111739617A true CN111739617A (en) | 2020-10-02 |
| CN111739617B CN111739617B (en) | 2023-07-11 |
Family
ID=72649360
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010544138.3A Active CN111739617B (en) | 2020-06-15 | 2020-06-15 | Medical image artificial intelligence quality control labeling method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111739617B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112669939A (en) * | 2020-12-24 | 2021-04-16 | 中电通商数字技术(上海)有限公司 | Medical image inspection part standardization method and system |
| CN113313359A (en) * | 2021-04-30 | 2021-08-27 | 北京医准智能科技有限公司 | Evaluation method and device for image labeling diagnosis quality |
| CN113380378A (en) * | 2021-05-25 | 2021-09-10 | 复旦大学附属中山医院 | Online collaborative medical image labeling method and device and storage medium |
| CN114927199A (en) * | 2022-02-28 | 2022-08-19 | 北京一脉阳光医学信息技术有限公司 | Medical image report and image quality control management system, method and storage medium |
Citations (28)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170143312A1 (en) * | 2014-09-03 | 2017-05-25 | Contextvision Ab | Methods and systems for automatic control of subjective image quality in imaging of objects |
| US9760690B1 (en) * | 2016-03-10 | 2017-09-12 | Siemens Healthcare Gmbh | Content-based medical image rendering based on machine learning |
| CN108630303A (en) * | 2018-04-28 | 2018-10-09 | 江苏医像信息技术有限公司 | Cloud labeling system |
| CN109272483A (en) * | 2018-08-01 | 2019-01-25 | 安翰光电技术（武汉）有限公司 | Capsule endoscope diagosis and the system and control method of quality control |
| CN109446370A (en) * | 2018-10-26 | 2019-03-08 | 广州金域医学检验中心有限公司 | Pathology mask method and device, the computer readable storage medium of medical image |
| WO2019047949A1 (en) * | 2017-09-08 | 2019-03-14 | 众安信息技术服务有限公司 | Image quality evaluation method and image quality evaluation system |
| WO2019052063A1 (en) * | 2017-09-15 | 2019-03-21 | 深圳市前海安测信息技术有限公司 | Medical image classification processing system and method based on artificial intelligence |
| US20190125298A1 (en) * | 2016-04-21 | 2019-05-02 | The University Of British Columbia | Echocardiographic image analysis |
| US20190164279A1 (en) * | 2017-11-28 | 2019-05-30 | Siemens Healthcare Gmbh | Method and device for the automated evaluation of at least one image data record recorded with a medical image recording device, computer program and electronically readable data carrier |
| CN110010217A (en) * | 2019-04-11 | 2019-07-12 | 中国医学科学院医学信息研究所 | A kind of mask method and device of electronic health record |
| CN110033859A (en) * | 2018-01-12 | 2019-07-19 | 西门子医疗有限公司 | Assess method, system, program and the storage medium of the medical findings of patient |
| WO2019153039A1 (en) * | 2018-02-06 | 2019-08-15 | Alerte Echo IQ Pty Ltd | Systems and methods for ai-assisted echocardiography |
| CN209266030U (en) * | 2019-03-18 | 2019-08-16 | 成都金盘电子科大多媒体技术有限公司 | Medical image Quality Control gateway all-in-one machine |
| CN110140178A (en) * | 2016-11-23 | 2019-08-16 | 皇家飞利浦有限公司 | The closed-loop system collected and fed back for knowing the picture quality of context |
| CN110264449A (en) * | 2019-06-14 | 2019-09-20 | 辽宁万象联合医疗科技有限公司 | The method of quality control of the deep learning method of artificial intelligence and the medical radioactive image chest radiography based on artificial intelligence |
| CN110276749A (en) * | 2019-06-14 | 2019-09-24 | 辽宁万象联合医疗科技有限公司 | Children penetrate the quality control artificial intelligence system and its quality control method of piece and diagnosis |
| CN110379492A (en) * | 2019-07-24 | 2019-10-25 | 复旦大学附属中山医院青浦分院 | A kind of completely new AI+PACS system and its audit report construction method |
| CN110428415A (en) * | 2019-08-05 | 2019-11-08 | 上海联影医疗科技有限公司 | Medical image quality evaluation method, device, equipment and storage medium |
| CN110459302A (en) * | 2019-08-05 | 2019-11-15 | 苏州米特希赛尔人工智能有限公司 | Ultrasonic medical image AI assists labeling system |
| CN110568483A (en) * | 2019-07-22 | 2019-12-13 | 中国石油化工股份有限公司 | Automatic evaluation method for seismic linear noise suppression effect based on convolutional neural network |
| CN110781633A (en) * | 2019-10-30 | 2020-02-11 | 广东博智林机器人有限公司 | Image-text design quality detection method, device and system based on deep learning model |
| WO2020039075A1 (en) * | 2018-08-24 | 2020-02-27 | Siemens Aktiengesellschaft | Code quality assessment method and apparatus, system, and storage medium |
| CN111079586A (en) * | 2019-12-03 | 2020-04-28 | 西安电子科技大学 | Automatic driving target detection system and method based on deep learning and binocular shooting |
| WO2020082732A1 (en) * | 2018-10-26 | 2020-04-30 | 平安科技（深圳）有限公司 | Automatic picture classification method, device, and computer readable storage medium |
| CN111095261A (en) * | 2017-04-27 | 2020-05-01 | 视网膜病答案有限公司 | Automatic analysis system and method for fundus images |
| CN111128349A (en) * | 2019-11-14 | 2020-05-08 | 清华大学 | GAN-based medical image focus detection marking data enhancement method and device |
| US20200160511A1 (en) * | 2018-11-21 | 2020-05-21 | Enlitic, Inc. | Medical scan artifact detection system |
| US20200160510A1 (en) * | 2018-11-20 | 2020-05-21 | International Business Machines Corporation | Automated Patient Complexity Classification for Artificial Intelligence Tools |
-
2020
- 2020-06-15 CN CN202010544138.3A patent/CN111739617B/en active Active
Patent Citations (28)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170143312A1 (en) * | 2014-09-03 | 2017-05-25 | Contextvision Ab | Methods and systems for automatic control of subjective image quality in imaging of objects |
| US9760690B1 (en) * | 2016-03-10 | 2017-09-12 | Siemens Healthcare Gmbh | Content-based medical image rendering based on machine learning |
| US20190125298A1 (en) * | 2016-04-21 | 2019-05-02 | The University Of British Columbia | Echocardiographic image analysis |
| CN110140178A (en) * | 2016-11-23 | 2019-08-16 | 皇家飞利浦有限公司 | The closed-loop system collected and fed back for knowing the picture quality of context |
| CN111095261A (en) * | 2017-04-27 | 2020-05-01 | 视网膜病答案有限公司 | Automatic analysis system and method for fundus images |
| WO2019047949A1 (en) * | 2017-09-08 | 2019-03-14 | 众安信息技术服务有限公司 | Image quality evaluation method and image quality evaluation system |
| WO2019052063A1 (en) * | 2017-09-15 | 2019-03-21 | 深圳市前海安测信息技术有限公司 | Medical image classification processing system and method based on artificial intelligence |
| US20190164279A1 (en) * | 2017-11-28 | 2019-05-30 | Siemens Healthcare Gmbh | Method and device for the automated evaluation of at least one image data record recorded with a medical image recording device, computer program and electronically readable data carrier |
| CN110033859A (en) * | 2018-01-12 | 2019-07-19 | 西门子医疗有限公司 | Assess method, system, program and the storage medium of the medical findings of patient |
| WO2019153039A1 (en) * | 2018-02-06 | 2019-08-15 | Alerte Echo IQ Pty Ltd | Systems and methods for ai-assisted echocardiography |
| CN108630303A (en) * | 2018-04-28 | 2018-10-09 | 江苏医像信息技术有限公司 | Cloud labeling system |
| CN109272483A (en) * | 2018-08-01 | 2019-01-25 | 安翰光电技术（武汉）有限公司 | Capsule endoscope diagosis and the system and control method of quality control |
| WO2020039075A1 (en) * | 2018-08-24 | 2020-02-27 | Siemens Aktiengesellschaft | Code quality assessment method and apparatus, system, and storage medium |
| WO2020082732A1 (en) * | 2018-10-26 | 2020-04-30 | 平安科技（深圳）有限公司 | Automatic picture classification method, device, and computer readable storage medium |
| CN109446370A (en) * | 2018-10-26 | 2019-03-08 | 广州金域医学检验中心有限公司 | Pathology mask method and device, the computer readable storage medium of medical image |
| US20200160510A1 (en) * | 2018-11-20 | 2020-05-21 | International Business Machines Corporation | Automated Patient Complexity Classification for Artificial Intelligence Tools |
| US20200160511A1 (en) * | 2018-11-21 | 2020-05-21 | Enlitic, Inc. | Medical scan artifact detection system |
| CN209266030U (en) * | 2019-03-18 | 2019-08-16 | 成都金盘电子科大多媒体技术有限公司 | Medical image Quality Control gateway all-in-one machine |
| CN110010217A (en) * | 2019-04-11 | 2019-07-12 | 中国医学科学院医学信息研究所 | A kind of mask method and device of electronic health record |
| CN110264449A (en) * | 2019-06-14 | 2019-09-20 | 辽宁万象联合医疗科技有限公司 | The method of quality control of the deep learning method of artificial intelligence and the medical radioactive image chest radiography based on artificial intelligence |
| CN110276749A (en) * | 2019-06-14 | 2019-09-24 | 辽宁万象联合医疗科技有限公司 | Children penetrate the quality control artificial intelligence system and its quality control method of piece and diagnosis |
| CN110568483A (en) * | 2019-07-22 | 2019-12-13 | 中国石油化工股份有限公司 | Automatic evaluation method for seismic linear noise suppression effect based on convolutional neural network |
| CN110379492A (en) * | 2019-07-24 | 2019-10-25 | 复旦大学附属中山医院青浦分院 | A kind of completely new AI+PACS system and its audit report construction method |
| CN110459302A (en) * | 2019-08-05 | 2019-11-15 | 苏州米特希赛尔人工智能有限公司 | Ultrasonic medical image AI assists labeling system |
| CN110428415A (en) * | 2019-08-05 | 2019-11-08 | 上海联影医疗科技有限公司 | Medical image quality evaluation method, device, equipment and storage medium |
| CN110781633A (en) * | 2019-10-30 | 2020-02-11 | 广东博智林机器人有限公司 | Image-text design quality detection method, device and system based on deep learning model |
| CN111128349A (en) * | 2019-11-14 | 2020-05-08 | 清华大学 | GAN-based medical image focus detection marking data enhancement method and device |
| CN111079586A (en) * | 2019-12-03 | 2020-04-28 | 西安电子科技大学 | Automatic driving target detection system and method based on deep learning and binocular shooting |
Non-Patent Citations (4)
| Title |
|---|
| MILLER,ET AL: "machine intelligence in cardiovascular medicine", 《CARDIOLOGY IN REVIEW》, vol. 28, no. 2, pages 53 - 64 * |
| 吉浩;曾凡荣;赵亚妮;谢颖夫;: "人工智能在医疗影像诊断领域的研究进展", 大医生, no. 08, pages 135 * |
| 王祥等: "基于三维卷积神经网络肺结节深度学习算法模型临床效能初步评估", 《放射学实践》, vol. 34, no. 9, pages 942 - 946 * |
| 谢俊祥等: "欧盟医疗人工智能相关战略形成路径及启示", 《中国医疗器械信息》, vol. 26, no. 3, pages 28 - 31 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112669939A (en) * | 2020-12-24 | 2021-04-16 | 中电通商数字技术(上海)有限公司 | Medical image inspection part standardization method and system |
| CN113313359A (en) * | 2021-04-30 | 2021-08-27 | 北京医准智能科技有限公司 | Evaluation method and device for image labeling diagnosis quality |
| CN113380378A (en) * | 2021-05-25 | 2021-09-10 | 复旦大学附属中山医院 | Online collaborative medical image labeling method and device and storage medium |
| CN114927199A (en) * | 2022-02-28 | 2022-08-19 | 北京一脉阳光医学信息技术有限公司 | Medical image report and image quality control management system, method and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111739617B (en) | 2023-07-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111739617B (en) | Medical image artificial intelligence quality control labeling method, device, equipment and storage medium | |
| US10965559B1 (en) | Automatic creation of related event groups for an IT service monitoring system | |
| US11044179B1 (en) | Service monitoring interface controlling by-service mode operation | |
| US11200130B2 (en) | Automatic entity control in a machine data driven service monitoring system | |
| US10417108B2 (en) | Portable control modules in a machine data driven service monitoring system | |
| US20210117985A1 (en) | Analytics engine for detecting medical fraud, waste, and abuse | |
| US9967351B2 (en) | Automated service discovery in I.T. environments | |
| US10193775B2 (en) | Automatic event group action interface | |
| US10209956B2 (en) | Automatic event group actions | |
| US10198155B2 (en) | Interface for automated service discovery in I.T. environments | |
| US20070260492A1 (en) | Master patient index | |
| US20160342572A1 (en) | Computer-Implemented System And Method For Identifying And Visualizing Relevant Data | |
| CN106991015B (en) | Medical information system integrated monitoring method based on message semantic annotation | |
| US11648621B2 (en) | Systems and methods to design part weld processes using media libraries | |
| US7353230B2 (en) | Dynamic distributed customer issue analysis | |
| WO2013151962A1 (en) | Medical device quality control management software | |
| Zhang et al. | Understanding and detecting defects in healthcare administration data: Toward higher data quality to better support healthcare operations and decisions | |
| CN113742227B (en) | Control method, device, equipment and medium for software testing process | |
| CN114270313A (en) | Computer-implemented method, computer program and physical computing environment | |
| US11755559B1 (en) | Automatic entity control in a machine data driven service monitoring system | |
| CN111696012A (en) | Medical image remote teaching method, device, equipment and storage medium | |
| US20080222179A1 (en) | Apparatus and method for sharing and reuse of structured knowledge artifacts | |
| US20220358102A1 (en) | Systems and methods of analyzing user-entered or machine-generated values in data for determining defective entries | |
| US11514068B1 (en) | Data validation system | |
| CN112259202A (en) | Rehabilitation diagnosis and treatment system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |