CN111739617B - Medical image artificial intelligence quality control labeling method, device, equipment and storage medium - Google Patents
Medical image artificial intelligence quality control labeling method, device, equipment and storage medium Download PDFInfo
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- 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
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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- G—PHYSICS
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- 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
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- G—PHYSICS
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- 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
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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: the common labeling personnel labels the medical images; and the auditing specialist controls the quality of the labeling result of the common labeling personnel. The medical image artificial intelligence quality control labeling method, the device, the equipment and the storage medium disclosed by the invention enable the experience of an expert to be better applied, and ensure the quality of image labeling; meanwhile, the artificial intelligence of image annotation is realized, and the working strength 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, a device, equipment and a storage medium.
Background
Symptom monitoring, also called syndrome monitoring, refers to a monitoring method for detecting and early warning and quick response of outbreaks of diseases by continuously and systematically collecting and analyzing data of occurrence frequency of clinical syndrome of specific diseases and timely finding abnormal aggregation of the diseases in time and space distribution. The disease monitoring is the most basic function of a disease prevention control center, the disease condition is reported after diagnosis by a hospital in the traditional sense, and the disease monitoring based on diagnosis is difficult to realize early detection and early warning of epidemic situation; moreover, the existing monitoring work is complex in operation and needs to have a certain medical background to use, so that the monitoring work cannot achieve the expected purpose. For medical images, the marking is usually performed manually, the quality of the image marking often depends on the working experience of marking personnel, so that marking results are uneven, the quality of the marking cannot be well controlled, and the subsequent diagnosis results of the images are obtained.
Therefore, in order to solve the above-mentioned problems, it is necessary to invent a new method, device, equipment and storage medium for labeling the artificial intelligence quality control of medical images.
Disclosure of Invention
The invention aims at: provided are a medical image artificial intelligence quality control labeling method, a device, equipment and a storage medium.
The invention provides the following scheme:
an artificial intelligence quality control labeling method for medical images comprises the following steps:
s1, setting a common labeling personnel labeling data layer for collecting and storing image labeling data of a medical image on line by a common labeling personnel;
s2, setting an audit expert quality control data layer for collecting and storing the quality control evaluation of the annotation result of the common annotator on line by the audit expert;
s3, setting a quality control evaluation artificial intelligent model, and performing image annotation on common annotators by using the quality control evaluation artificial intelligent model to perform quality control, wherein the method comprises the following steps:
constructing a quality control evaluation artificial intelligent model; constructing a quality control evaluation convolutional neural network through a quality control evaluation artificial intelligent model;
processing the labeling data of the common labeling personnel to obtain a labeling training set of the common labeling personnel;
processing the quality control data of the auditing specialist to obtain a quality control labeling result set of the auditing specialist;
training a convolutional neural network of the quality control evaluation artificial intelligent model by using a common labeling personnel labeling training set and an auditing expert quality control labeling result set to obtain a trained quality control evaluation artificial intelligent model;
s4, performing quality control on the labeling result of the common labeling personnel by using a quality control evaluation artificial intelligent model, wherein the quality control method specifically comprises the following steps:
if the labeling result of the quality control evaluation artificial intelligent model is consistent with the labeling result of the common labeling personnel, the quality control is qualified;
if the labeling result of the quality control evaluation artificial intelligent model is inconsistent with the labeling result of the common labeling personnel, sending an auditing expert to further control the quality of the labeling result of the common labeling personnel;
and S5, incorporating the labeling result of the common labeling personnel and the quality control labeling result of the auditing expert into a training set for learning.
Preferably, in the step S4, if the labeling result of the quality control evaluation artificial intelligent model is inconsistent with the labeling result of the common labeling personnel, the labeling result of the artificial intelligent model is fed back to the common labeling personnel, and if the labeling result of the common labeling personnel on the artificial intelligent model is inconsistent, the labeling result is sent to the auditing expert for further quality control on the labeling result of the common labeling personnel;
the step of image marking of the medical image by common marking personnel comprises the following specific steps:
the common labeling personnel browse, delineate and measure the medical images to finish detection, segmentation and attribute information selection of the focus, and classify and standardize labeling results.
The auditing expert performs quality control on the labeling result of the common labeling personnel, specifically:
and the auditing specialist checks, reviews, supplements and modifies the labeling results of the common labeling personnel.
The auditing expert labeling quality control modes comprise a line quality control mode and a parallel quality control mode:
the line quality control mode refers to a quality control step and an audit expert quality control step of performing quality control evaluation on the labeling result of each common labeling person, and is used for an initial training stage of the model.
The parallel quality control mode refers to a quality control step that the labeling result of each common labeling person passes through the quality control evaluation artificial intelligent model, and only when the labeling result of the common labeling person is inconsistent with the labeling result of the quality control evaluation artificial intelligent model, an auditing expert is sent to perform quality control for the use stage of the middle and later stages of the model.
Further comprises: further training and perfecting the medical image artificial intelligent model according to expert quality control labeling results;
further, a medical image artificial intelligence quality control labeling device of the medical image artificial intelligence quality control labeling method is provided, comprising:
the marking module is used for marking the medical images by common marking personnel;
and the quality control module is used for controlling the quality of the labeling result of the common labeling personnel by the auditing expert.
Further comprises:
the artificial intelligent module is used for training the medical image artificial intelligent model according to expert quality control labeling results; and image labeling is performed by using the medical image artificial intelligence model.
The labeling module, the quality control module and the artificial intelligence module are electrically connected in sequence.
An electronic device includes 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 storing a computer program which, when executed by a processor, is configured to implement the medical image artificial intelligence quality control labeling method.
The invention has the beneficial effects that:
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: the common labeling personnel labels the medical images; the auditing expert controls the quality of the labeling result of the common labeling personnel; the experience of the expert is better applied, and the quality of image marking is ensured; meanwhile, the artificial intelligence of image annotation is realized, and the working strength of doctors is reduced.
Drawings
FIG. 1 is a block flow diagram of the medical image artificial intelligence quality control labeling method of the invention.
FIG. 2 is a block flow diagram of a word class based neural network machine translation training method of the present invention.
FIG. 3 is a block diagram of a word class based neural network machine translation system of 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 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 unless defined otherwise. 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, an artificial intelligence quality control labeling method for medical images comprises the following steps:
s1, setting a common labeling personnel labeling data layer for collecting and storing image labeling data of a medical image on line by a common labeling personnel;
s2, setting an audit expert quality control data layer for collecting and storing the quality control evaluation of the annotation result of the common annotator on line by the audit expert;
s3, setting a quality control evaluation artificial intelligent model, and performing image annotation on common annotators by using the quality control evaluation artificial intelligent model to perform quality control, wherein the method comprises the following steps:
constructing a quality control evaluation artificial intelligent model; constructing a quality control evaluation convolutional neural network through a quality control evaluation artificial intelligent model;
processing the labeling data of the common labeling personnel to obtain a labeling training set of the common labeling personnel;
processing the quality control data of the auditing specialist to obtain a quality control labeling result set of the auditing specialist;
training a convolutional neural network of the quality control evaluation artificial intelligent model by using a common labeling personnel labeling training set and an auditing expert quality control labeling result set to obtain a trained quality control evaluation artificial intelligent model;
s4, performing quality control on the labeling result of the common labeling personnel by using a quality control evaluation artificial intelligent model, wherein the quality control method specifically comprises the following steps:
if the labeling result of the quality control evaluation artificial intelligent model is consistent with the labeling result of the common labeling personnel, the quality control is qualified;
if the labeling result of the quality control evaluation artificial intelligent model is inconsistent with the labeling result of the common labeling personnel, sending an auditing expert to further control the quality of the labeling result of the common labeling personnel;
and S5, incorporating the labeling result of the common labeling personnel and the quality control labeling result of the auditing expert into a training set for learning.
The step of image marking of the medical image by common marking personnel comprises the following specific steps:
the common labeling personnel browse, delineate and measure the medical images to finish detection, segmentation and attribute information selection of the focus.
The auditing expert performs quality control on the labeling result of the common labeling personnel, specifically:
and the auditing expert checks, reviews and modifies the labeling results of the common labeling personnel.
The auditing expert labeling quality control modes comprise a line quality control mode and a parallel quality control mode.
Further comprises:
training the medical image artificial intelligent model according to expert quality control labeling results;
and (5) image marking is carried out by the medical image artificial intelligent model.
Referring to fig. 2, a medical image artificial intelligence quality control labeling device of a medical image artificial intelligence quality control labeling method includes:
the marking module is used for marking the medical images by common marking personnel;
and the quality control module is used for controlling the quality of the labeling result of the common labeling personnel by the auditing expert.
Further comprises:
the artificial intelligent module is used for training the medical image artificial intelligent model according to expert quality control labeling results; and image labeling is performed by using the medical image artificial intelligence model.
The labeling 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, in which a computer program is stored, and the computer program is used for implementing the medical image artificial intelligence quality control labeling method when being executed by a processor.
The medical image artificial intelligence quality control labeling method, device, equipment and storage medium in the embodiment, and the quality control labeling method comprises the following steps: the common labeling personnel labels the medical images; the auditing expert controls the quality of the labeling result of the common labeling personnel; the experience of the expert is better applied, and the quality of image marking is ensured; meanwhile, the artificial intelligence of image annotation is realized, and the working strength of doctors is reduced.
Medical image artificial intelligence matter control marking device in this embodiment, the image marking module includes:
(1) Detecting doctor homepage: logging in, logging out, viewing incomplete tasks, and viewing a completed task list.
(2) Marking a workbench:
A. task loading: and automatically loading the labeling business template conforming to the task.
B. Labeling a tool set: including tools related to labeling, measurement, and basic image manipulation tools.
C. Information viewing and supplementing: information review is used to review medical history, and information supplementation is used to fill out the negative, positive and save submissions of lesions.
D. And (3) preserving: and (5) storing the labeling result.
E. Submitting: submitting the labeling result.
The expert quality control module comprises:
(1) Expert quality control doctor homepage: logging in, logging out, viewing incomplete tasks, and viewing a completed task list.
(2) Expert quality control tool table:
A. task loading: and automatically loading an arbitration service template conforming to the task.
B. Labeling a tool set: including tools related to labeling, measurement, and basic image manipulation tools.
C. Information viewing and supplementing: checking medical history; checking a focus labeling list layer by layer; looking at a detailed list of individual lesions; information supplementation was negative, positive and save submissions used to fill out lesions.
D. Auditing opinion: the quality control expert can select the label of the labeling doctor or remark and store the label as an audit opinion.
E. And (3) preserving: and (5) storing the labeling result.
F. Submitting: submitting the labeling result.
Medical image artificial intelligence matter accuse mark device in this embodiment still includes: a background management module, comprising:
(1) An administrator homepage: logging in and exiting.
(2) Background personnel management and authority management:
A. manager adds, deletes and checks: create, modify, delete and give the user rights.
B. And (3) authority configuration of management personnel: rights can be created according to all modules in the background, so that different functions can be seen after the manager logs in.
(3) Doctor management: and creating doctors according to the names, the hospitals, the categories and other information of the doctors, supporting modification, inquiry, deletion and deletion recovery.
(4) Hospital or institution management: and creating a hospital or an organization according to the information such as the name of the hospital or the organization, supporting modification, inquiry, deletion and deletion recovery.
(5) Disease configuration creates disease according to the position, supports single selection, multiple selection and filling, supports modification, inquiry, deletion and deletion recovery.
(6) Parameter management: management of some common parameters is placed at the module, for example: information management such as parts, item types, label types and the like supports modification, inquiry, deletion and deletion recovery.
(7) And (3) flow management: because the labels of all disease types or all medical images do not have a uniform flow, the flow is created according to different requirements, and modification, query, deletion and deletion recovery are supported.
(8) And (3) task management: according to the selection data, filling in task information, selecting disease types, selecting a flow, selecting doctor and other information to create tasks, supporting modification, inquiry, deletion and deletion recovery.
(9) And (3) medical records management: the medical image data management function uploads data to store the data according to the information of the data filling data. Query, delete and delete recovery are supported.
(10) Operation record: and checking operation records of doctor marks 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 expert quality control labeling results; image marking is carried out by utilizing the medical image artificial intelligent model; the automation of image marking is realized, and the labor intensity of doctors is reduced.
For the purposes of simplicity of explanation, the methodologies are shown and described as a series of acts, it is to be understood and appreciated by one of ordinary skill in the art that the methodologies are not limited by the order of acts, as some acts may, in accordance with the methodologies, take place in other order or concurrently. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred embodiments, and that the acts are not necessarily required by the embodiments of the invention.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the 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 scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. The medical image artificial intelligence quality control labeling method is characterized by comprising the following steps of:
s1, setting a common labeling personnel labeling data layer for collecting and storing image labeling data of a medical image on line by a common labeling personnel;
s2, setting an audit expert quality control data layer for collecting and storing the quality control evaluation of the annotation result of the common annotator on line by the audit expert;
s3, setting a quality control evaluation artificial intelligent model, and performing image annotation on common annotators by using the quality control evaluation artificial intelligent model to perform quality control, wherein the method comprises the following steps: constructing a quality control evaluation artificial intelligent model; constructing a quality control evaluation convolutional neural network through a quality control evaluation artificial intelligent model; processing the labeling data of the common labeling personnel to obtain a labeling training set of the common labeling personnel; processing the quality control data of the auditing specialist to obtain a quality control labeling result set of the auditing specialist; training a convolutional neural network of the quality control evaluation artificial intelligent model by using a common labeling personnel labeling training set and an auditing expert quality control labeling result set to obtain a trained quality control evaluation artificial intelligent model;
s4, performing quality control on the labeling result of the common labeling personnel by using a quality control evaluation artificial intelligent model, wherein the quality control method specifically comprises the following steps: if the labeling result of the quality control evaluation artificial intelligent model is consistent with the labeling result of the common labeling personnel, the quality control is qualified; if the labeling result of the quality control evaluation artificial intelligent model is inconsistent with the labeling result of the common labeling personnel, sending an auditing expert to further control the quality of the labeling result of the common labeling personnel;
and S5, incorporating the labeling result of the common labeling personnel and the quality control labeling result of the auditing expert into a training set for learning.
2. The medical image artificial intelligence quality control labeling method according to claim 1, wherein the step of labeling the medical image by a common labeling person comprises the following steps: the common labeling personnel browse, delineate and measure the medical images to finish 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 the auditing specialist controlling the quality of the labeling result of the common labeling person is specifically as follows: and the auditing specialist checks, reviews, supplements and modifies the labeling results of the common labeling personnel.
4. The medical image artificial intelligence quality control labeling method according to claim 1, wherein in step S4, labeling results of the quality control evaluation artificial intelligence model are inconsistent with labeling results of common labeling personnel: firstly, the labeling result of the artificial intelligent model is fed back to common labeling personnel, and if the labeling result of the artificial intelligent model is inconsistent by the common labeling personnel, complaints are sent to auditing specialists to further control the quality of the labeling result of the common labeling personnel.
5. The medical image artificial intelligence quality control labeling method according to claim 3, wherein the quality control modes include a line quality control mode and a parallel quality control mode.
6. A medical image artificial intelligence quality control labeling device for implementing the medical image artificial intelligence quality control labeling method according to claim 1, comprising: the marking module is used for marking the medical images by common marking personnel; the quality control module is used for enabling an auditing expert to control quality of the labeling result of the common labeling personnel;
further comprises: the artificial intelligent module is used for training the medical image artificial intelligent model according to expert quality control labeling results; and image labeling is performed by using the medical image artificial intelligence model.
7. The medical image artificial intelligence quality control labeling device of claim 6, wherein the labeling module, the quality control module and the artificial intelligence module are electrically connected in sequence.
8. 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 implement the medical image artificial intelligence quality control labeling method of any of claims 1-5.
9. A computer-readable storage medium, characterized by: a computer program is stored which, when executed by a processor, is adapted to carry out the medical image artificial intelligence quality control labeling method according to any of claims 1-5.
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