CN114822779A - Symptom information image display method and device, storage medium and computer equipment - Google Patents
Symptom information image display method and device, storage medium and computer equipment Download PDFInfo
- Publication number
- CN114822779A CN114822779A CN202210216806.9A CN202210216806A CN114822779A CN 114822779 A CN114822779 A CN 114822779A CN 202210216806 A CN202210216806 A CN 202210216806A CN 114822779 A CN114822779 A CN 114822779A
- Authority
- CN
- China
- Prior art keywords
- symptom
- image
- symptom information
- information
- information 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.)
- Pending
Links
- 208000024891 symptom Diseases 0.000 title claims abstract description 374
- 238000000034 method Methods 0.000 title claims abstract description 55
- 238000003860 storage Methods 0.000 title claims abstract description 15
- 238000012163 sequencing technique Methods 0.000 claims description 17
- 238000004590 computer program Methods 0.000 claims description 7
- 238000005457 optimization Methods 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 7
- 238000000605 extraction Methods 0.000 claims description 6
- 230000004044 response Effects 0.000 claims description 2
- 238000012549 training Methods 0.000 description 9
- 238000004364 calculation method Methods 0.000 description 7
- 238000003745 diagnosis Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 6
- 238000009826 distribution Methods 0.000 description 5
- 210000004185 liver Anatomy 0.000 description 5
- 238000013528 artificial neural network Methods 0.000 description 4
- 239000000284 extract Substances 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 238000002591 computed tomography Methods 0.000 description 3
- 201000010099 disease Diseases 0.000 description 3
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 3
- 201000007270 liver cancer Diseases 0.000 description 3
- 208000014018 liver neoplasm Diseases 0.000 description 3
- 241000283690 Bos taurus Species 0.000 description 2
- 238000013527 convolutional neural network Methods 0.000 description 2
- 238000013135 deep learning Methods 0.000 description 2
- 210000004072 lung Anatomy 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000005728 strengthening Methods 0.000 description 2
- 206010060891 General symptom Diseases 0.000 description 1
- 206010027476 Metastases Diseases 0.000 description 1
- 206010028980 Neoplasm Diseases 0.000 description 1
- 206010035664 Pneumonia Diseases 0.000 description 1
- 230000003321 amplification Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000002457 bidirectional effect Effects 0.000 description 1
- 210000004204 blood vessel Anatomy 0.000 description 1
- 210000000988 bone and bone Anatomy 0.000 description 1
- 201000011510 cancer Diseases 0.000 description 1
- 239000002775 capsule Substances 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000001815 facial effect Effects 0.000 description 1
- 239000005337 ground glass Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000012528 membrane Substances 0.000 description 1
- 230000009401 metastasis Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
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
- G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
Landscapes
- Health & Medical Sciences (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
- Engineering & Computer Science (AREA)
- Epidemiology (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Primary Health Care (AREA)
- Public Health (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The embodiment of the application discloses a method, a device, a storage medium and computer equipment for displaying symptom information images, wherein the method comprises the following steps: acquiring a medical image, and extracting a symptom information image from the medical image; determining the importance priority of each symptom information image, and grouping the symptom information images according to the importance priority to obtain a plurality of groups of symptom information image groups; and sorting the symptom information images in each symptom information image group to obtain the symptom information image group with sorted symptom information images. The extracted symptom information images are grouped according to the priority of importance, and the symptom information images in each group of symptom information image group are sorted, so that the symptom information images with high importance and the front serial numbers are preferentially displayed to the doctor, the efficiency of the doctor in analyzing the symptoms is improved, and the error probability is reduced.
Description
Technical Field
The application relates to the field of computers, in particular to a method and a device for displaying symptom information images, a computer readable storage medium and computer equipment.
Background
In the prior art, a large number of medical images are usually obtained after a medical scan is performed on a human body. When a doctor diagnoses the state of an illness of a patient, the doctor needs to observe a medical image of the patient so as to judge the real state of the illness of the patient. When a doctor observes a medical image, the doctor usually needs to analyze a plurality of characteristics or signs from the image to determine the condition of the patient.
In the process of research and practice of the prior art, the inventor of the present application finds that in the prior art, doctors look at a large number of signs in a plurality of images, the signs are easy to leak, or the signs are interleaved by a large amount of information, and the extraction of the signs corresponding to the diagnosis logic is very energetic, so that errors are easy to occur.
Disclosure of Invention
The embodiment of the application provides a method and a device for displaying a symptom information image, which can improve the efficiency of a doctor in analyzing the symptom and reduce the error probability.
In order to solve the above technical problem, an embodiment of the present application provides the following technical solutions:
a method for displaying symptom information images comprises the following steps:
acquiring a medical image, and extracting a symptom information image from the medical image;
determining the importance priority of each symptom information image, and grouping the symptom information images according to the importance priority to obtain a plurality of groups of symptom information image groups;
and sorting the symptom information images in each symptom information image group to obtain the symptom information image group with sorted symptom information images.
A sign information image display device, comprising:
the extraction module is used for acquiring a medical image and extracting a symptom information image from the medical image;
the grouping module is used for determining the importance priority of each symptom information image and grouping the symptom information images according to the importance priority to obtain a plurality of groups of symptom information image groups;
and the sorting module is used for sorting the symptom information images in each symptom information image group to obtain the symptom information image group after the symptom information images are sorted.
In some embodiments, the ranking module comprises:
the first sequencing submodule is used for sequencing the symptom information images in each symptom information image group by adopting a trained symptom information image sequencing model; or
And the second sorting submodule is used for sorting the symptom information images in each symptom information image group according to a preset display sequence.
In some embodiments, the information type of the symptom information image is an image type, and the apparatus further includes:
a first determining module, configured to determine a sequence number of the symptom information images in the symptom information image group;
the second determining module is used for determining the symptom information image with the sequencing serial number before the designated serial number as the symptom information image to be processed;
and the optimization module is used for performing image optimization processing on the to-be-processed symptom information image to obtain an optimized symptom information image.
In some embodiments, the apparatus further comprises:
the dividing module is used for dividing the symptom information image into a plurality of different importance priorities, and each importance priority corresponds to a different preset credibility value interval.
In some embodiments, the apparatus further comprises:
and the display module is used for sequentially displaying the sorted symptom information images in the plurality of groups of symptom information image groups based on the importance priority.
In some embodiments, the image of symptom information includes symptom sub-information of a plurality of sub-information types, and the grouping module includes:
the first obtaining submodule is used for obtaining a weight value corresponding to each sub-information type;
the first calculation sub-module is used for calculating the reliability value of each symptom information image based on each symptom sub-information and the weight value corresponding to the type of the sub-information;
and the determining submodule is used for determining the importance priority of each sign information image according to a preset confidence value interval in which the confidence value of each sign information image is positioned.
In some embodiments, the apparatus further comprises:
the third determination module is used for responding to the scoring operation aiming at the symptom sub-information, and determining the target sub-information type of the target symptom sub-information corresponding to the scoring operation;
and the adjusting module is used for adjusting the target weight value corresponding to the target sub-information type.
In some embodiments, the adjustment module comprises:
the second obtaining submodule is used for obtaining the scoring value of the scoring operation;
the second calculation submodule is used for calculating the difference value between the score value and a preset score value to obtain a first result if the score value is larger than the preset score value;
and the increasing submodule is used for increasing the target weight value corresponding to the target sub-information type based on the first result.
A computer readable storage medium, wherein a plurality of instructions are stored in the computer readable storage medium, and the instructions are suitable for being loaded by a processor to execute the steps of the method for displaying the symptom information image.
Computer equipment, comprising a memory, a processor and a computer program stored on the memory and capable of running on the processor, characterized in that the processor implements the steps of the above-mentioned sign information image display method when executing the program.
According to the embodiment of the application, the medical image is obtained, and the symptom information image is extracted from the medical image; determining the importance priority of each symptom information image, and grouping the symptom information images according to the importance priority to obtain a plurality of groups of symptom information image groups; and sorting the symptom information images in each symptom information image group to obtain the symptom information image group with sorted symptom information images. Therefore, the extracted symptom information images are grouped according to the priority of importance, and the symptom information images with high importance and the front serial numbers are preferentially displayed to the doctor in a mode of sequencing the symptom information images in each group of the symptom information images, so that the efficiency of the doctor in analyzing the symptoms is improved, and the error probability is reduced.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1a is a system diagram illustrating a method for displaying a symptom information image according to an embodiment of the present disclosure.
Fig. 1b is a schematic flowchart of a method for displaying a symptom information image according to an embodiment of the present disclosure.
Fig. 2 is a schematic structural view of a sign information image display device according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The embodiment of the application provides a method and a device for displaying a symptom information image and a computer readable storage medium.
Referring to fig. 1a, fig. 1a is a schematic system diagram of a system for displaying a symptom information image according to an embodiment of the present disclosure, where the system may include at least one medical scanning device such as a CT medical scanner 1000, at least one computer device 2000, at least one database 3000, and a network 4000. The medical scanning device is used for scanning a body part of a user, such as a heart, a liver, a lung, a blood vessel, a bone, and the like, in a Computer Tomography (CT) image, a Magnetic Resonance examination (MR) image, a 4D ultrasonic image, and the like, so as to acquire a medical image of the body part of the user. And the medical image acquired by the medical scanning apparatus may be transmitted to the computer apparatus 2000 through the network 4000, so that the doctor can see the medical image on the computer apparatus 2000. The network 4000 may be a wireless network or a wired network, for example, the wireless network is a Wireless Local Area Network (WLAN), a Local Area Network (LAN), a cellular network, a 2G network, a 3G network, a 4G network, a 5G network, or the like. In addition, the system may include a plurality of databases 3000, the plurality of databases 3000 may be a database integrated in the computer device 2000 or a third-party database that is connected to the computer device 2000 through a protocol, and the medical image transmitted from the medical scanning device to the computer device 2000 may be stored in the database 3000.
The embodiment of the application provides a method for displaying a symptom information image, which can be executed by computer equipment. As shown in fig. 1a, a body part of a user is scanned by a medical scanning device, so as to obtain a medical image, and the medical image is transmitted to a computer device 2000; the computer device 2000 acquires a medical image, extracts a symptom information image from the medical image; determining the importance priority of each symptom information image, and grouping the symptom information images according to the importance priority to obtain a plurality of groups of symptom information image groups; and sorting the symptom information images in each symptom information image group to obtain the symptom information image group with sorted symptom information images. Based on the method, the extracted symptom information images are grouped according to the priority of importance, and the symptom information images in each group of symptom information image group are sorted, so that the symptom information images with high importance and the front serial numbers are preferentially displayed to the doctor, the efficiency of the doctor in analyzing the symptoms is improved, and the error probability is reduced.
It should be noted that the scene schematic diagram of the symptom information image display system shown in fig. 1a is only an example, and the symptom information image display system and the scene described in the embodiment of the present application are for more clearly illustrating the technical solution of the embodiment of the present application, and do not form a limitation on the technical solution provided in the embodiment of the present application.
In the present embodiment, the description will be made in terms of a sign information image display apparatus, which may be integrated in a computer device having a storage unit and a microprocessor installed therein and having an arithmetic capability.
Referring to fig. 1b, fig. 1b is a schematic flowchart illustrating a method for displaying a symptom information image according to an embodiment of the present disclosure. The method for displaying the symptom information image comprises the following steps:
in step 101, a medical image is acquired, and a symptom information image is extracted from the medical image.
The medical image is obtained by scanning the body part of the user through the medical scanning equipment, and the medical scanning equipment can send the medical image to the computer equipment, so that the computer equipment can obtain the medical image.
Specifically, the symptom information image may be obtained by processing symptom information extracted from the medical image, and the symptom information may include a symptom type, a symptom degree, a symptom location, and the like of the symptom, so the symptom information image may include a plurality of pieces of symptom sub information of different sub information types, such as the symptom type, the symptom area, and the symptom degree corresponding to the symptom area. In order to ensure accurate judgment of the disease condition, the symptom sub-information of the above-mentioned multiple sub-information types can be extracted. For example, when extracting the symptom sub-information of the symptom type, the medical image may be identified by the symptom type identification model, so as to extract the symptom type.
The method comprises the steps of extracting the character sub-information of a plurality of sub-information types, wherein the character sub-information comprises a plurality of types of sub-information, so that the character sub-information can be extracted in series or in parallel through a plurality of identification models during extraction, or the character sub-information of a plurality of types of sub-information can be extracted simultaneously through one identification model capable of identifying a plurality of types of sub-information. The model may be trained by a deep learning network model or a machine learning model. For example, Convolutional Neural Networks (CNN), deconvolution Neural Networks (De-Convolutional Networks, DN), Deep Neural Networks (Deep Neural Networks, DNN), Deep Convolutional Inverse Networks (DCIGN), Region-based Convolutional Networks (RCNN), Region-based fast Convolutional Networks (fast RCNN), and Bidirectional Encoder/decoder (BERT) models.
Specifically, taking the feature region identification model for extracting the feature region as an example, the process of model training may be: before training the sign region recognition model, a large number of medical images are prepared as a training set, sign regions are artificially determined in each medical image as the training set and marked in the medical images, and the marked training set is sent to the model to be trained, so that the trained sign region recognition model is obtained.
In step 102, the importance priority of each of the image information images is determined, and the image information images are grouped according to the importance priority to obtain a plurality of image information image groups.
Wherein, after the symptom information image is extracted, the importance priority of the symptom information image can be determined. The importance priority can be divided into: importance and generally, etc. And dividing the sign information images with the same importance priority into one group, thereby obtaining a plurality of groups of sign information images.
In some embodiments, before the step of determining the importance priority of each of the symptom information images, the method further includes:
the symptom information image is divided into a plurality of different importance priorities, and each importance priority corresponds to a different preset confidence value interval.
The evaluation criteria of the importance priority can be measured by the confidence value of each symptom information image, so that confidence value intervals of various importance priorities can be preset. For example, the importance priority is important, the corresponding confidence value interval is 70-100, the importance priority is general, and the corresponding confidence value interval is 0-70. Therefore, when the importance priority of the sign information image is determined, the importance priority of the sign information image can be determined by determining which preset reliability interval the reliability of the sign information image is within.
In some embodiments, the image of symptom information includes symptom sub-information of a plurality of sub-information types, and the step of determining the priority of importance of each image of symptom information includes:
(1) acquiring a weight value corresponding to each sub-information type;
(2) calculating the credibility value of each symptom information image based on each symptom sub-information and the weight value corresponding to the sub-information type of the symptom sub-information;
(3) and determining the importance priority of each symptom information image according to a preset credibility value interval in which the credibility value of each symptom information image is located.
Because each symptom information image comprises symptom sub-information of different sub-information types, different weight values can be set for different sub-information types, and the reliability value of each symptom information image is calculated based on each symptom sub-information and the weight value corresponding to the sub-information type.
Specifically, the determination of the confidence value may be in a linear calculation manner, for example: confidence k1 + k2 + k 3+ symptom location rating. k1, k2 and k3 are weight values corresponding to the symptom degree, the symptom type level and the symptom position level, respectively. The confidence value may also be determined in a non-linear calculation manner, which is not limited herein.
For example, in a linear calculation manner, the disease condition is liver cancer as an example, the image of the symptom information shows that the symptom type is envelope strengthening, the symptom degree is large-scale distribution, and the symptom position is a designated area which is easy to spread in a plurality of subareas in the liver. The grade of the symptom type corresponding to the capsule enhancement is 10, the grade of the symptom position is 5, the grade of the priority grade of the large-piece distribution of the designated area prone to cancer metastasis is 3, if k1 is 10, k2 is 5, and k3 is 4, the reliability is 10 x 3+5 x 10+4 x 5 x 100, and the reliability value range 70-100 corresponding to the importance is located, the importance degree of the symptom information image is determined to be important.
In some embodiments, the information type of the symptom information image is an image type, and after the step of determining the importance priority of each symptom information image, the method further includes:
and summarizing the symptom information images and/or superposed images generated by superposing the symptom information images.
In which various information such as the type of a symptom, a symptom area, and a symptom information image corresponding to the symptom can be extracted from a medical image. The variety of the symptom information images is various, and the corresponding symptom information images are different according to different symptoms. The image of the symptom information can be an image, and can also be character information or image-text combined information.
When the information type of the symptom information image is an image, the symptom information images can be gathered to form a set of medical image sequence for subsequent analysis; or different credit information images are firstly superposed to form a new credit information image, and then the new superposed credit information images are summarized to form a complete set of medical image sequence for subsequent analysis.
In step 103, the image data of each image data set is sorted to obtain the sorted image data set.
After dividing the image of the symptom information into a plurality of groups of image groups of the symptom information according to the importance priority, the image of the symptom information in each group of image groups of the symptom information can be sorted, so that the image of the symptom information with the same importance priority and the image with the first sequence number in the image of the symptom information with the same importance priority are displayed preferentially.
In some embodiments, the step of sorting the image information images in each image information image group includes:
(1) sorting the symptom information images in each symptom information image group by adopting a trained symptom information image sorting model; or
(2) And sorting the symptom information images in each symptom information image group according to a preset display sequence.
The mode of sorting the symptom information images in each symptom information image group can be sorting according to a preset display sequence such as a medical diagnosis guide or a preset symptom information image display sequence of a user; or the trained image sorting model of the symptom information is adopted to sort the symptom information images in each symptom information image group.
Specifically, the method for training the symptom information image ranking model may be as follows: the display sequence of the symptom information images in the diagnosis process of the doctor is used as input, and the trained symptom information image recommendation model is obtained through multiple times of training in the modes of deep learning neural network or mechanical learning and the like. Therefore, the trained symptom information image recommendation model can be adopted to sort the symptom information images in each symptom information image group, and a symptom information image display sequence which is better than the diagnosis habit of a doctor in the diagnosis process is obtained.
In some embodiments, the information type of the symptom information image is an image type, and after the step of sorting the symptom information images in each of the symptom information image groups, the method further includes:
(1) determining the sequencing serial number of the symptom information images in the symptom information image group;
(2) determining the symptom information image with the sequence number before the designated sequence number as a symptom information image to be processed;
(3) and performing image optimization processing on the to-be-processed symptom information image to obtain an optimized symptom information image.
The method includes the steps of presetting a designated sequence number, such as sequence number 3, determining the symptom information image with the sequence number before the designated sequence number as a to-be-processed symptom information image, wherein the to-be-processed symptom information image is the symptom information image preferentially displayed to a doctor, and performing influence optimization processing, such as image amplification and image enhancement (adjustment of gray scale, resolution ratio and the like), on the to-be-processed symptom information image to obtain the optimized symptom information image, so that the doctor can observe the characteristic more comprehensively.
In some embodiments, after the step of sorting the image information images in each image information set to obtain the sorted image information set, the method further includes:
and sequentially displaying the sorted symptom information images in the plurality of groups of symptom information image groups based on the importance priority.
After the symptom information images are grouped and sorted, the sorted symptom information images in the plurality of groups of symptom information image groups can be sequentially displayed.
Specifically, the sorted image information images in the image group with the important importance priority are displayed in priority, and then the sorted image information images in the general image information group with the important priority are displayed, so that the image information images with high importance and the image information images with the front sequence number are displayed to the doctor in a priority mode by grouping the extracted image information images according to the importance priority and sorting the image information images in each image information group, thereby improving the efficiency of the doctor in analyzing the image and reducing the error probability. In addition, the image group of the sign information after the sign information images are sequenced can be sent to the sequencing model to be used as a training set to continue training the sequencing model, so that the accuracy of the sequencing model is improved.
For example, in the case of liver cancer, the image of the symptom information with importance priority as important: the symptom type is membrane strengthening, the symptom degree is large-scale distribution, the symptom position is a symptom information image of an appointed area which is easy to spread in a plurality of subareas in the liver, the symptom information image is displayed to a doctor for diagnosis and analysis, and then the importance priority is the general symptom information image: the type of the symptom is bovine eye sign, the degree of the symptom is small piece distribution, and the position of the symptom is an image of the symptom information of a designated area which is not easy to spread in a plurality of subareas in the liver. The above is merely an example, and the degree of the symptom may also be determined according to the original size and the historical change size of the symptom, and is not limited herein.
Further, the present invention is also applicable to other types of diseases as well as liver cancer, and for example, in the case of pneumonia, if the symptom information image shows that the symptom type is ground glass, the symptom level is advanced, and the symptom position is one of the two lungs, the serial number of the symptom information image may be arranged in the symptom information image: the symptom type is bovine eye sign, the symptom degree is small piece distribution, and the symptom position is behind the image of the symptom information of the designated area which is not easy to spread in a plurality of subareas in the liver.
In some embodiments, after the step of sequentially presenting the sorted image of the plurality of image groups of image information based on the importance priority, the method further comprises:
(1) in response to a scoring operation aiming at the symptom sub-information, determining a target sub-information type of the target symptom sub-information corresponding to the scoring operation;
(2) and adjusting a target weight value corresponding to the target sub-information type.
In the display process, a scoring interface aiming at the symptom sub-information is provided for the doctor, and the doctor can score certain symptom sub-information, so that the importance priority of the symptom information image is improved.
Specifically, different sub-information types correspond to different weight values, so that after a doctor performs a scoring operation on a certain symptom sub-information, the target sub-information type of the target symptom sub-information corresponding to the scoring operation is determined, and the target weight value corresponding to the target sub-information type is adjusted. Since the weight value is involved in calculating the confidence value of the symptom information image, changing the weight value also changes the confidence value, thereby changing the importance priority of the symptom information image.
In some embodiments, the step of adjusting the target weight value corresponding to the target sub-information type includes:
(1.1) obtaining a scoring value of the scoring operation;
(1.2) if the score value is larger than a preset score value, calculating a difference value between the score value and the preset score value to obtain a first result;
(1.3) increasing a target weight value corresponding to the target sub information type based on the first result.
A score, for example, 50 points, may be preset, and if the score value given by the scoring operation is greater than the preset score value, it indicates that the doctor considers the target sub-information as an important reference indicator, so that the target weight value of the target sub-information type corresponding to the target sub-information may be correspondingly increased.
Specifically, the weighting value may be increased by calculating a difference between the score and a preset score, for example, if the score is 80 scores and the preset score is 50 scores, the difference is 30 scores as the first result. That is, the target weight value is increased by 30%, and correspondingly, the target weight value can be correspondingly increased by 30%. The specific manner of adjusting the weight value is not limited to the above-mentioned adjusting manner, and is not limited herein.
As can be seen from the above, in the embodiment of the present application, by acquiring a medical image, a symptom information image is extracted from the medical image; determining the importance priority of each symptom information image, and grouping the symptom information images according to the importance priority to obtain a plurality of groups of symptom information image groups; and sorting the symptom information images in each symptom information image group to obtain the symptom information image group with sorted symptom information images. Therefore, the extracted symptom information images are grouped according to the importance priority, and the symptom information images with high importance and the prior serial numbers are preferentially displayed to the doctor in a mode of sequencing the symptom information images in each group of the symptom information images, so that the efficiency of the doctor in analyzing the symptoms is improved, and the error probability is reduced.
The method described in connection with the above embodiments will be described in further detail below by way of example.
In order to better implement the method for displaying the symptom information image provided by the embodiment of the present application, the embodiment of the present application further provides a device based on the method for displaying the symptom information image. The meaning of the noun is the same as that in the above-mentioned method for displaying a symptom information image, and the details of the implementation can refer to the description in the method embodiment.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a symptom information image display apparatus according to an embodiment of the present disclosure, wherein the symptom information image display apparatus may include an extracting module 201, a grouping module 202, a sorting module 203, a display module 204, and the like.
An extraction module 201, configured to acquire a medical image and extract a symptom information image from the medical image;
a grouping module 202, configured to determine an importance priority of each of the symptom information images, and group the symptom information images according to the importance priority to obtain a plurality of groups of symptom information images;
a sorting module 203, configured to sort the symptom information images in each symptom information image group to obtain a symptom information image group after the symptom information images are sorted;
the displaying module 204 is configured to sequentially display the ordered symptom information images in the plurality of groups of symptom information images based on the importance priority.
In some embodiments, the sorting module 203 comprises:
the first sequencing submodule is used for sequencing the symptom information images in each symptom information image group by adopting a trained symptom information image sequencing model; or
And the second sorting submodule is used for sorting the symptom information images in each symptom information image group according to a preset display sequence.
In some embodiments, the information type of the symptom information image is an image type, and the apparatus further includes:
a first determining module, configured to determine a sequence number of the symptom information images in the symptom information image group;
the second determining module is used for determining the symptom information image with the sequencing serial number before the designated serial number as the symptom information image to be processed;
and the optimization module is used for performing image optimization processing on the to-be-processed symptom information image to obtain an optimized symptom information image.
In some embodiments, the apparatus further comprises:
the dividing module is used for dividing the symptom information image into a plurality of different importance priorities, and each importance priority corresponds to a different preset credibility value interval.
In some embodiments, the apparatus further comprises:
and the display module is used for sequentially displaying the sorted symptom information images in the plurality of groups of symptom information image groups based on the importance priority.
In some embodiments, the image of symptom information includes symptom sub-information of a plurality of sub-information types, and the grouping module 202 includes:
the first obtaining submodule is used for obtaining a weight value corresponding to each sub-information type;
the first calculation submodule is used for calculating the reliability value of each symptom information image based on each symptom sub-information and the weight value corresponding to the sub-information type of the symptom information image;
and the determining submodule is used for determining the importance priority of each sign information image according to a preset confidence value interval in which the confidence value of each sign information image is positioned.
In some embodiments, the apparatus further comprises:
the third determination module is used for responding to the scoring operation aiming at the symptom sub-information, and determining the target sub-information type of the target symptom sub-information corresponding to the scoring operation;
and the adjusting module is used for adjusting the target weight value corresponding to the target sub-information type.
In some embodiments, the adjustment module comprises:
the second obtaining submodule is used for obtaining the scoring value of the scoring operation;
the second calculation submodule is used for calculating the difference value between the score value and a preset score value to obtain a first result if the score value is larger than the preset score value;
and the increasing submodule is used for increasing the target weight value corresponding to the target sub-information type based on the first result.
As can be seen from the above, in the embodiment of the present application, the extraction module 201 acquires a medical image, and extracts a symptom information image from the medical image; the grouping module 202 determines the importance priority of each of the symptom information images, and groups the symptom information images according to the importance priority to obtain a plurality of groups of symptom information images; the sorting module 203 sorts the image of the symptom information in each image group of the symptom information to obtain the image group of the symptom information after sorting the image of the symptom information. Therefore, the extracted symptom information images are grouped according to the priority of importance, and the symptom information images with high importance and the front serial numbers are preferentially displayed to the doctor in a mode of sequencing the symptom information images in each group of the symptom information images, so that the efficiency of the doctor in analyzing the symptoms is improved, and the error probability is reduced.
Correspondingly, an embodiment of the present application further provides a computer device, as shown in fig. 3, fig. 3 is a schematic structural diagram of the computer device provided in the embodiment of the present application. The computer device 2000 includes a processor 301 having one or more processing cores, a memory 302 having one or more computer-readable storage media, and a computer program stored on the memory 302 and executable on the processor. The processor 301 is electrically connected to the memory 302. Those skilled in the art will appreciate that the computer device configurations illustrated in the figures are not meant to be limiting of computer devices and may include more or fewer components than those illustrated, or some components may be combined, or a different arrangement of components.
The processor 301 is a control center of the computer device 2000, connects various parts of the entire computer device 2000 by using various interfaces and lines, performs various functions of the computer device 2000 and processes data by running or loading software programs and/or modules stored in the memory 302, and calling data stored in the memory 302, thereby integrally monitoring the computer device 2000.
In the embodiment of the present application, the processor 301 in the computer device 2000 loads instructions corresponding to processes of one or more applications into the memory 302, and the processor 301 executes the applications stored in the memory 302 according to the following steps, so as to implement various functions:
acquiring a medical image, and extracting a symptom information image from the medical image; determining the importance priority of each symptom information image, and grouping the symptom information images according to the importance priority to obtain a plurality of groups of symptom information image groups; sorting the symptom information images in each symptom information image group to obtain a symptom information image group after the symptom information images are sorted; and sequentially displaying the sorted symptom information images in the plurality of groups of symptom information image groups based on the importance priority.
The above operations can be implemented in the foregoing embodiments, and are not described in detail herein.
Optionally, as shown in fig. 3, the computer device 2000 further includes: an input unit 303 and a power supply 304. The processor 301 is electrically connected to the input unit 303 and the power supply 304. Those skilled in the art will appreciate that the computer device configuration illustrated in FIG. 3 does not constitute a limitation of computer devices, and may include more or fewer components than those illustrated, or some components may be combined, or a different arrangement of components.
The input unit 303 may be used to receive input numbers, character information, or user characteristic information (e.g., fingerprint, iris, facial information, etc.), and generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function control.
The power supply 304 is used to power the various components of the computer device 2000. Optionally, the power supply 304 may be logically connected to the processor 301 through a power management system, so as to implement functions of managing charging, discharging, and power consumption management through the power management system. The power supply 304 may also include any component including one or more dc or ac power sources, recharging systems, power failure detection circuitry, power converters or inverters, power status indicators, and the like.
Although not shown in fig. 3, the computer device 2000 may further include a camera, a sensor, a wireless fidelity module, a bluetooth module, etc., which are not described in detail herein.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
As can be seen from the above, the computer device provided in this embodiment may acquire a medical image, and extract a symptom information image from the medical image; determining the importance priority of each symptom information image, and grouping the symptom information images according to the importance priority to obtain a plurality of groups of symptom information image groups; and sorting the symptom information images in each symptom information image group to obtain the symptom information image group with sorted symptom information images. Therefore, the extracted symptom information images are grouped according to the priority of importance, and the symptom information images with high importance and the front serial numbers are preferentially displayed to the doctor in a mode of sequencing the symptom information images in each group of the symptom information images, so that the efficiency of the doctor in analyzing the symptoms is improved, and the error probability is reduced.
It will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions or by associated hardware controlled by the instructions, which may be stored in a computer readable storage medium and loaded and executed by a processor.
To this end, an embodiment of the present application provides a computer-readable storage medium, in which a plurality of computer programs are stored, and the computer programs can be loaded by a processor to execute the steps in any one of the methods for displaying a symptom information image provided by the embodiments of the present application. For example, the computer program may perform the steps of:
acquiring a medical image, and extracting a symptom information image from the medical image; determining the importance priority of each symptom information image, and grouping the symptom information images according to the importance priority to obtain a plurality of groups of symptom information image groups; sorting the symptom information images in each symptom information image group to obtain a symptom information image group after the symptom information images are sorted; and sequentially displaying the sorted symptom information images in the plurality of groups of symptom information image groups based on the importance priority.
The above operations can be implemented in the foregoing embodiments, and are not described in detail herein.
Wherein the storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
Since the computer program stored in the storage medium can execute the steps in any of the methods for displaying a symptom information image provided in the embodiments of the present application, the beneficial effects that can be achieved by any of the methods for displaying a symptom information image provided in the embodiments of the present application can be achieved, which are detailed in the foregoing embodiments and will not be described herein again.
The method, the apparatus, the computer-readable storage medium, and the computer device for displaying a symptom information image provided by the embodiments of the present application are described in detail above, and specific examples are applied in the description to explain the principles and embodiments of the present application, and the description of the embodiments is only used to help understand the method and the core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (11)
1. A method for displaying a symptom information image, comprising:
acquiring a medical image, and identifying a symptom information image from the medical image;
determining the importance priority of each symptom information image, and grouping the symptom information images according to the importance priority to obtain a plurality of groups of symptom information image groups;
and sorting the symptom information images in each symptom information image group to obtain the symptom information image group with sorted symptom information images.
2. The method of claim 1, wherein the step of sorting the sets of symptom information images comprises:
sorting the symptom information images in each symptom information image group by adopting a trained symptom information image sorting model; or
And sorting the symptom information images in each symptom information image group according to a preset display sequence.
3. The method as claimed in claim 2, wherein the information type of the image is image type, and after the step of sorting the image of the image group, the method further comprises:
determining the sequencing serial number of the symptom information images in the symptom information image group;
determining the symptom information image with the sequence number before the designated sequence number as a symptom information image to be processed;
and performing image optimization processing on the to-be-processed symptom information image to obtain an optimized symptom information image.
4. A method as claimed in claim 1, wherein before the step of determining the importance priority of each of the symptom information images, the method further comprises:
the symptom information image is divided into a plurality of different importance priorities, and each importance priority corresponds to a different preset confidence value interval.
5. A method as claimed in claim 4, wherein the image of the symptom information comprises symptom sub-information of a plurality of sub-information types, and the step of determining the priority of importance of each image of the symptom information comprises:
acquiring a weight value corresponding to each sub-information type;
calculating the credibility value of each symptom information image based on each symptom sub-information and the weight value corresponding to the sub-information type of the symptom sub-information;
and determining the importance priority of each symptom information image according to a preset credibility value interval in which the credibility value of each symptom information image is located.
6. The method of claim 5, wherein after the step of sorting the sign information images in each of the sign information image groups to obtain the sorted sign information image groups, the method further comprises:
and sequentially displaying the sorted symptom information images in the plurality of groups of symptom information image groups based on the importance priority.
7. The method as claimed in claim 6, further comprising, after the step of displaying the ordered image information images of the plurality of image information image groups in turn based on the importance priorities:
in response to a scoring operation aiming at the symptom sub-information, determining a target sub-information type of the target symptom sub-information corresponding to the scoring operation;
and adjusting a target weight value corresponding to the target sub-information type.
8. The method of claim 5, wherein the step of adjusting the target weight value corresponding to the target sub-information type comprises:
obtaining the scoring value of the scoring operation;
if the score value is larger than a preset score value, calculating a difference value between the score value and the preset score value to obtain a first result;
and increasing a target weight value corresponding to the target sub-information type based on the first result.
9. A sign information image display apparatus, comprising:
the extraction module is used for acquiring a medical image and extracting a symptom information image from the medical image;
the grouping module is used for determining the importance priority of each symptom information image and grouping the symptom information images according to the importance priority to obtain a plurality of groups of symptom information image groups;
and the sorting module is used for sorting the symptom information images in each symptom information image group to obtain the symptom information image group after the symptom information images are sorted.
10. A computer-readable storage medium storing instructions for loading by a processor to perform the steps of the method for displaying a picture of a symptom information according to any of claims 1 to 8.
11. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to perform the steps of the method for presenting a picture of a symptom information according to any of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210216806.9A CN114822779A (en) | 2022-03-07 | 2022-03-07 | Symptom information image display method and device, storage medium and computer equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210216806.9A CN114822779A (en) | 2022-03-07 | 2022-03-07 | Symptom information image display method and device, storage medium and computer equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114822779A true CN114822779A (en) | 2022-07-29 |
Family
ID=82528740
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210216806.9A Pending CN114822779A (en) | 2022-03-07 | 2022-03-07 | Symptom information image display method and device, storage medium and computer equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114822779A (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112466439A (en) * | 2018-01-23 | 2021-03-09 | 深圳科亚医疗科技有限公司 | Computer-implemented medical image scheduling method, scheduling system, and storage medium |
| CN113539445A (en) * | 2021-09-02 | 2021-10-22 | 数坤(北京)网络科技股份有限公司 | Medical image processing method and device |
| CN113808125A (en) * | 2021-04-23 | 2021-12-17 | 数坤(北京)网络科技股份有限公司 | Medical image processing method, focus type identification method and related product |
| CN114119578A (en) * | 2021-12-01 | 2022-03-01 | 数坤(北京)网络科技股份有限公司 | Image processing method and device, computer equipment and storage medium |
-
2022
- 2022-03-07 CN CN202210216806.9A patent/CN114822779A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112466439A (en) * | 2018-01-23 | 2021-03-09 | 深圳科亚医疗科技有限公司 | Computer-implemented medical image scheduling method, scheduling system, and storage medium |
| CN113808125A (en) * | 2021-04-23 | 2021-12-17 | 数坤(北京)网络科技股份有限公司 | Medical image processing method, focus type identification method and related product |
| CN113539445A (en) * | 2021-09-02 | 2021-10-22 | 数坤(北京)网络科技股份有限公司 | Medical image processing method and device |
| CN114119578A (en) * | 2021-12-01 | 2022-03-01 | 数坤(北京)网络科技股份有限公司 | Image processing method and device, computer equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10706333B2 (en) | Medical image analysis method, medical image analysis system and storage medium | |
| CN109035187B (en) | Medical image labeling method and device | |
| Luo et al. | Deep mining external imperfect data for chest X-ray disease screening | |
| CN109191451B (en) | Abnormality detection method, apparatus, device, and medium | |
| CN111681219A (en) | New coronary pneumonia CT image classification method, system and equipment based on deep learning | |
| US11756199B2 (en) | Image analysis in pathology | |
| Gao et al. | Chest X-ray image analysis and classification for COVID-19 pneumonia detection using Deep CNN | |
| CN110674773A (en) | Dementia recognition system, device and storage medium | |
| CN110570425B (en) | Pulmonary nodule analysis method and device based on deep reinforcement learning algorithm | |
| CN113808738A (en) | Disease identification system based on self-identification image | |
| EP2449484B1 (en) | Relevance feedback for content-based image retrieval | |
| CN113782184A (en) | Cerebral apoplexy auxiliary evaluation system based on facial key point and feature pre-learning | |
| Pham et al. | Chest x-rays abnormalities localization and classification using an ensemble framework of deep convolutional neural networks | |
| CN114343577A (en) | Cognitive function evaluation method, terminal device, and computer-readable storage medium | |
| EP3467770B1 (en) | Method for analysing a medical imaging data set, system for analysing a medical imaging data set, computer program product and a computer-readable medium | |
| CN111414930A (en) | Deep learning model training method and device, electronic equipment and storage medium | |
| CN114822779A (en) | Symptom information image display method and device, storage medium and computer equipment | |
| CN113903433B (en) | Image processing method and device and electronic equipment | |
| WO2023274599A1 (en) | Methods and systems for automated follow-up reading of medical image data | |
| JP2022069143A (en) | Diagnosis device using ai, learning data generation device, data requesting device, diagnosis system, and program | |
| Mannepalli et al. | An Early Detection of Pneumonia in CXR Images using Deep Learning Techniques | |
| CN115578370B (en) | Brain image-based metabolic region abnormality detection method and device | |
| US20230196557A1 (en) | Late Gadolinium Enhancement Analysis for Magnetic Resonance Imaging | |
| Wen et al. | Computer-aided image analysis aids early diagnosis of connective-tissue diseases | |
| Gunasekara et al. | A feasibility study for deep learning based automated brain tumor segmentation using magnetic resonance images |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20220729 |
|
| RJ01 | Rejection of invention patent application after publication |