CN110689958A - Cancer pathology auxiliary diagnosis method based on artificial intelligence technology - Google Patents
Cancer pathology auxiliary diagnosis method based on artificial intelligence technology Download PDFInfo
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Abstract
The invention discloses an artificial intelligence technology-based cancer pathology auxiliary diagnosis method, which specifically comprises the following steps: s1, scanning pathological pictures to be diagnosed into the system through the case image scanning module, and controlling the image classification and sorting unit to perform image classification processing through the central processing module. The auxiliary diagnosis method for cancer pathology based on the artificial intelligence technology can realize grouping processing on scanned pathological images to accelerate image processing progress, well achieves the purpose of reducing system processing load through grouping processing, avoids the occurrence of the situation of sitka pause or crash, achieves the purpose of analyzing and processing pathological images quickly and accurately, does not need a system to process one image, avoids spending a large amount of working time of working personnel, and greatly facilitates the diagnosis work of the working personnel.
Description
Technical Field
The invention relates to the technical field of pathology auxiliary diagnosis, in particular to a cancer pathology auxiliary diagnosis method based on an artificial intelligence technology.
Background
Medically, cancer refers to malignant tumor originated from epithelial tissue, which is the most common kind of malignant tumor, and correspondingly, malignant tumor originated from mesenchymal tissue is commonly referred to as sarcoma, and there are a few malignant tumors not named according to the above principle, such as nephroblastoma, malignant teratoma, etc., generally, the "cancer" is traditionally and widely referred to as all malignant tumors, which has biological characteristics of abnormal cell differentiation and proliferation, growth loss control, infiltration and metastasis, and the occurrence of the cancer is a multi-factor and multi-step complex process, which is divided into carcinogenesis, cancer promotion and evolution processes, closely related to smoking, infection, occupational exposure, environmental pollution, unreasonable diet and genetic factors, and the conventional cancer diagnosis is based on analyzing cell and tissue samples from tumor or suspected tumor tissue, and through tissue staining, individual cells or cell groups can be identified, conventional diagnosis relies on analysis of morphological features such as changes in cell shape, size and staining characteristics, and irregularities in tissue structure, so cancer diagnosis is a subjective judgment of morphological variation from corresponding normal tissues, and accurate and reliable diagnosis skills require extensive experience, which is a challenge for even the most experienced pathologists and cytologists, and this category of problems is increasing since complete morphological criteria for tumor diagnosis have not been developed.
The current intelligent cancer auxiliary diagnosis needs a lot of scanned pathological images, and the system needs one image to process, needs to spend a lot of working time of the staff, can not realize the grouped processing of the scanned pathological images, accelerates the image processing progress, can not reach the purpose of reducing the processing load of the system through the grouped processing, can not avoid the occurrence of the situation of west tower blockage or dead halt, can not reach the purpose of analyzing and processing the pathological images quickly and accurately, thereby bringing great inconvenience to the diagnosis work of the staff.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a cancer pathology auxiliary diagnosis method based on an artificial intelligence technology, which solves the problems that the conventional intelligent cancer auxiliary diagnosis cannot realize grouped processing on scanned pathological images to accelerate the progress of image processing because more scanned pathological images are needed, a system needs one image for processing, a large amount of working time of workers is needed, the purpose of reducing the processing load of the system through grouped processing cannot be achieved, the occurrence of west tower blockage or dead halt cannot be avoided, and the purpose of analyzing and processing pathological images quickly and accurately cannot be achieved.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme: an artificial intelligence technology-based cancer pathology auxiliary diagnosis method specifically comprises the following steps:
s1, firstly, a pathological picture to be diagnosed is scanned into the system through a case image scanning module, and then the central processing module controls the image classification and sorting unit to perform image classification processing;
s2, an image algorithm preprocessing module in the image classifying and sorting unit identifies scanned image data, meanwhile, labeling lesion areas in the images, then an image pathological area classifying module classifies the images labeled by the same lesion areas into a group, the number of the groups to be classified is determined by the type of the labeled lesion areas, and then an image grouping processing module separates and packages the groups of images;
s3, the central processing module controls the image group processing unit to process the multiple groups of image groups obtained in the step S2, the image group feature acquisition module in the image group processing unit acquires feature data in each group of images, then the feature data are collected through the feature data drawing module, after the collection is finished, the same feature data are removed through a removing algorithm in the repeated feature removing module, and then the overall pathological analysis image is drawn through the pathological analysis image drawing module according to the collected feature data;
s4, the central processing module controls the pathology analysis image extraction module in the pathology image recognition unit to extract the whole pathology analysis image drawn in the step S3 from the image group processing unit, meanwhile, the database lesion feature image is extracted from the pathology image diagnosis database module by referring to the pathology image extraction module, then the intelligent algorithm comparison module compares the drawn whole pathology analysis image with the database lesion feature image through a comparison algorithm, and the comparison result analysis module analyzes and processes the comparison result after the comparison is finished;
s5, the central processing module controls the display module to display, and transmits the analysis result to the doctor analysis terminal for the doctor to observe and analyze, and after the analysis is finished, the central processing module controls the diagnosis report printing module to print out the diagnosis report;
and S6, in the whole system processing process, the central processing module can control the data storage and management module to store and manage the data.
Preferably, the image classifying and sorting unit in step S2 includes an image algorithm preprocessing module, an image pathological region classifying module, and an image grouping processing module, wherein an output end of the image algorithm preprocessing module is connected to an input end of the image pathological region classifying module, and an output end of the image pathological region classifying module is connected to an input end of the image grouping processing module.
Preferably, the image group processing unit in step S3 includes an image group feature acquisition module, a feature data summarization module, a repeated feature removal module, and a pathology analysis image rendering module, and an output end of the image group feature acquisition module is connected to an input end of the feature data summarization module.
Preferably, the output end of the characteristic data summarizing module is connected with the input end of the repeated characteristic eliminating module, and the output end of the repeated characteristic eliminating module is connected with the input end of the pathological analysis image drawing module.
Preferably, the pathological image recognition unit in step S4 includes a pathological analysis image extraction module, a reference pathological image extraction module, an intelligent algorithm comparison module, and a comparison result analysis module, wherein the outputs of the pathological analysis image extraction module and the reference pathological image extraction module are connected to the input of the intelligent algorithm comparison module, and the output of the intelligent algorithm comparison module is connected to the input of the comparison result analysis module.
Preferably, in step S5, the central processing module is bidirectionally connected to the doctor analysis terminal, and an output end of the central processing module is respectively connected to input ends of the display module and the diagnosis report printing module.
Preferably, in step S6, the central processing module is connected to the data storage and management module in a bidirectional manner.
(III) advantageous effects
The invention provides a cancer pathology auxiliary diagnosis method based on an artificial intelligence technology. Compared with the prior art, the method has the following beneficial effects: the auxiliary diagnosis method for cancer pathology based on the artificial intelligence technology specifically comprises the following steps: s1, firstly, a pathological picture to be diagnosed is scanned into a system through a case image scanning module, then a central processing module controls an image classification and sorting unit to perform image classification, S2 an image algorithm preprocessing module in the image classification and sorting unit identifies scanned image data and marks lesion areas in the images, then the image pathological area classification module summarizes the images marked by the same lesion areas into one group, S3 the central processing module controls an image group processing unit to process a plurality of groups of image groups obtained in the step S2, an image group feature acquisition module in the image group processing unit acquires feature data in each group of images, S4, then the central processing module controls a pathological analysis image extraction module in the pathological image recognition unit to extract an integral pathological analysis image drawn in the step S3 into the image group processing unit, meanwhile, the pathological image extraction module is referred to extract the pathological characteristic images of the database from the pathological image diagnosis database module, S5, the central processing module controls the display module to display, and transmits the analysis result to the doctor analysis terminal to be observed and analyzed by the doctor, after the analysis is finished, the central processing module controls the diagnosis report printing module to print the diagnosis report, S6, in the whole system processing process, the central processing module can control the data storage and management module to store and manage the data, the scanned pathological images can be processed in a grouping mode to accelerate the image processing progress, the purpose of reducing the system processing load through grouping processing is well achieved, the occurrence of the situation of sitka or dead halt is avoided, the purpose of analyzing and processing the pathological images quickly and accurately is achieved, and the system does not need to process one image, the method avoids spending a large amount of working time of the workers, thereby greatly facilitating the diagnosis work of the workers.
Drawings
FIG. 1 is a schematic block diagram of the architecture of the system of the present invention;
FIG. 2 is a schematic block diagram of the structure of the image sorting and sorting unit according to the present invention;
fig. 3 is a schematic block diagram of the structure of the pathological image recognition unit according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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 invention.
Referring to fig. 1-3, an embodiment of the present invention provides a technical solution: an artificial intelligence technology-based cancer pathology auxiliary diagnosis method specifically comprises the following steps:
s1, firstly, a pathological picture to be diagnosed is scanned into the system through a case image scanning module, and then the central processing module controls the image classification and sorting unit to perform image classification processing;
s2, an image algorithm preprocessing module in the image classifying and sorting unit identifies scanned image data, meanwhile, labeling lesion areas in the images, then an image pathological area classifying module classifies the images labeled by the same lesion areas into a group, the number of the groups to be classified is determined by the type of the labeled lesion areas, and then an image grouping processing module separates and packages the groups of images;
s3, the central processing module controls the image group processing unit to process the multiple groups of image groups obtained in the step S2, the image group feature acquisition module in the image group processing unit acquires feature data in each group of images, then the feature data are collected through the feature data drawing module, after the collection is finished, the same feature data are removed through a removing algorithm in the repeated feature removing module, and then the overall pathological analysis image is drawn through the pathological analysis image drawing module according to the collected feature data;
s4, the central processing module controls the pathology analysis image extraction module in the pathology image recognition unit to extract the whole pathology analysis image drawn in the step S3 from the image group processing unit, meanwhile, the database lesion feature image is extracted from the pathology image diagnosis database module by referring to the pathology image extraction module, then the intelligent algorithm comparison module compares the drawn whole pathology analysis image with the database lesion feature image through a comparison algorithm, and the comparison result analysis module analyzes and processes the comparison result after the comparison is finished;
s5, the central processing module controls the display module to display, and transmits the analysis result to the doctor analysis terminal for the doctor to observe and analyze, and after the analysis is finished, the central processing module controls the diagnosis report printing module to print out the diagnosis report;
and S6, in the whole system processing process, the central processing module can control the data storage and management module to store and manage the data.
In the present invention, the image classifying and sorting unit in step S2 includes an image algorithm preprocessing module, an image pathological region classifying module, and an image grouping processing module, wherein an output end of the image algorithm preprocessing module is connected to an input end of the image pathological region classifying module, and an output end of the image pathological region classifying module is connected to an input end of the image grouping processing module.
In the present invention, the image group processing unit in step S3 includes an image group feature acquisition module, a feature data summarization module, a repeated feature removal module, and a pathology analysis image rendering module, wherein an output end of the image group feature acquisition module is connected to an input end of the feature data summarization module, an output end of the feature data summarization module is connected to an input end of the repeated feature removal module, and an output end of the repeated feature removal module is connected to an input end of the pathology analysis image rendering module.
In the present invention, the pathological image recognition unit in step S4 includes a pathological analysis image extraction module, a reference pathological image extraction module, an intelligent algorithm comparison module, and a comparison result analysis module, wherein output terminals of the pathological analysis image extraction module and the reference pathological image extraction module are connected to an input terminal of the intelligent algorithm comparison module, and an output terminal of the intelligent algorithm comparison module is connected to an input terminal of the comparison result analysis module.
In the invention, the central processing module and the doctor analysis terminal realize bidirectional connection in step S5, and the output end of the central processing module is respectively connected with the input ends of the display module and the diagnosis report printing module.
In the present invention, step S6 is performed by the central processing module being bidirectionally coupled to the data storage and management module.
To sum up the above
The invention can realize the grouping processing of the scanned pathological images to accelerate the image processing progress, well achieves the purpose of reducing the processing load of the system through grouping processing, avoids the occurrence of sitka pause or crash, achieves the purpose of quickly and accurately analyzing and processing the pathological images, does not need the system to process one image, avoids spending a large amount of working time of working personnel, and greatly facilitates the diagnosis work of the working personnel.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. An auxiliary cancer pathology diagnosis method based on artificial intelligence technology is characterized in that: the method specifically comprises the following steps:
s1, firstly, a pathological picture to be diagnosed is scanned into the system through a case image scanning module, and then the central processing module controls the image classification and sorting unit to perform image classification processing;
s2, an image algorithm preprocessing module in the image classifying and sorting unit identifies scanned image data, meanwhile, labeling lesion areas in the images, then an image pathological area classifying module classifies the images labeled by the same lesion areas into a group, the number of the groups to be classified is determined by the type of the labeled lesion areas, and then an image grouping processing module separates and packages the groups of images;
s3, the central processing module controls the image group processing unit to process the multiple groups of image groups obtained in the step S2, the image group feature acquisition module in the image group processing unit acquires feature data in each group of images, then the feature data are collected through the feature data drawing module, after the collection is finished, the same feature data are removed through a removing algorithm in the repeated feature removing module, and then the overall pathological analysis image is drawn through the pathological analysis image drawing module according to the collected feature data;
s4, the central processing module controls the pathology analysis image extraction module in the pathology image recognition unit to extract the whole pathology analysis image drawn in the step S3 from the image group processing unit, meanwhile, the database lesion feature image is extracted from the pathology image diagnosis database module by referring to the pathology image extraction module, then the intelligent algorithm comparison module compares the drawn whole pathology analysis image with the database lesion feature image through a comparison algorithm, and the comparison result analysis module analyzes and processes the comparison result after the comparison is finished;
s5, the central processing module controls the display module to display, and transmits the analysis result to the doctor analysis terminal for the doctor to observe and analyze, and after the analysis is finished, the central processing module controls the diagnosis report printing module to print out the diagnosis report;
and S6, in the whole system processing process, the central processing module can control the data storage and management module to store and manage the data.
2. The auxiliary diagnosis method for cancer pathology based on artificial intelligence technology as claimed in claim 1, wherein: the image classification and arrangement unit in the step S2 includes an image algorithm preprocessing module, an image pathological region classification module, and an image grouping processing module, wherein an output end of the image algorithm preprocessing module is connected to an input end of the image pathological region classification module, and an output end of the image pathological region classification module is connected to an input end of the image grouping processing module.
3. The auxiliary diagnosis method for cancer pathology based on artificial intelligence technology as claimed in claim 1, wherein: the image group processing unit in the step S3 includes an image group feature acquisition module, a feature data summarization module, a repeated feature removal module, and a pathology analysis image rendering module, and an output end of the image group feature acquisition module is connected with an input end of the feature data summarization module.
4. The auxiliary diagnosis method for cancer pathology based on artificial intelligence technology as claimed in claim 3, wherein: the output end of the characteristic data summarizing module is connected with the input end of the repeated characteristic eliminating module, and the output end of the repeated characteristic eliminating module is connected with the input end of the pathological analysis image drawing module.
5. The auxiliary diagnosis method for cancer pathology based on artificial intelligence technology as claimed in claim 1, wherein: the pathological image recognition unit in the step S4 includes a pathological analysis image extraction module, a reference pathological image extraction module, an intelligent algorithm comparison module, and a comparison result analysis module, wherein output ends of the pathological analysis image extraction module and the reference pathological image extraction module are connected to an input end of the intelligent algorithm comparison module, and an output end of the intelligent algorithm comparison module is connected to an input end of the comparison result analysis module.
6. The auxiliary diagnosis method for cancer pathology based on artificial intelligence technology as claimed in claim 1, wherein: in the step S5, the central processing module is bidirectionally connected to the doctor analysis terminal, and the output end of the central processing module is respectively connected to the input ends of the display module and the diagnosis report printing module.
7. The auxiliary diagnosis method for cancer pathology based on artificial intelligence technology as claimed in claim 1, wherein: and step S6, the central processing module and the data storage and management module realize bidirectional connection.
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