Method and apparatus for classifying medical data based on mutual correction and information fusion

Abstract

To provide a method and device for classifying medical data based on mutual correction and information fusion.SOLUTION: The method includes: acquiring a CT image and / or an MRI image of a target object; obtaining a medical data classification result output by a fuzzy classification model; training and internally correcting decision information of a CT view and an MRI view; and performing information fusion on the decision information of the two views to obtain the medical data classification result.EFFECT: According to the embodiments of the present disclosure, the zero order T-S fuzzy classifier is used as a basic training unit to ensure the interpretability of the model training, the KL-divergence is used to reduce the feature dimension to relieve the pressure of the decision process, the decision information required for classification is corrected and information fusion is performed on the two views through a plurality of sub-views to capture diverse decision information, and the advantage of each view in the decision direction is used to further optimize the decision information and improve the classification accuracy.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present application relates to the field of medical data processing technology, and in particular to a medical data classification method and apparatus based on mutual correction and information fusion. [Background technology]

[0002] With the development of artificial intelligence technology, more and more medical data can be used to discover relevant pathological features. For example, computed tomography (CT) data or magnetic resonance imaging (MRI) data can be used to discover the digitized features of relevant tumors. In clinical diagnosis, many attempts have been made to improve the medical data classification capabilities of artificial intelligence tools. For example, more advanced networks such as three-dimensional convolutional neural networks (CNNs), recurrent neural networks based on courtesy gate control recurrent units, support vector machines (SVMs), and U-net networks have already been successfully applied to the classification of various medical data, such as nasopharyngeal carcinoma.

[0003] The key to classifying medical data is to obtain appropriate key feature information that influences the final decision. Prior art approaches have used the idea of ​​genetic algorithm feature selection or the idea of ​​combining clinical features with MRI-based radiological features. However, these methods use endoscopic images as a single viewing angle, and do not take into account the different resolutions of imaging techniques used in different imaging methods. While subsequent prior art approaches have adopted multi-view models of CT and MRI images to address real-world situations, such models lack clear interpretation for feature learning. Furthermore, data images from multiple views often contain a large amount of image information, making automatic detection time-consuming. Due to limitations in the model's classification effectiveness in certain scenarios, manual intervention by a physician is always required to determine classification results, preventing advanced automatic classification. Summary of the Invention [Problem to be solved by the invention]

[0004] In view of this, embodiments of the present application are directed to a method and apparatus for medical data classification based on mutual correction and information fusion. [Means for solving the problem]

[0005] In a first aspect, an embodiment of the present invention provides a method for medical data classification based on mutual correction and information fusion, comprising: acquiring one or more CT images and / or one or more MRI images of the target object; inputting the plurality of CT images and the plurality of MRI images into a fuzzy classification model, and obtaining a medical data classification result output from the fuzzy classification model; Wherein, the training process of the fuzzy classification model is training decision information of a CT view based on sample data in a CT image sample dataset and its corresponding classification tag, wherein the CT image sample datasets are respectively used for a scan subview, a first contrast-enhanced subview, and a second contrast-enhanced subview in the CT view to mutually correct the decision information; training decision information for an MRI view based on sample data in an MRI image sample dataset and corresponding classification tags, wherein the MRI image sample data are respectively used for a plurality of different organ sub-views in the MRI view to mutually correct the decision information; and performing information fusion on the trained decision information of the CT view and the MRI view, respectively, and then outputting and obtaining a medical data classification result of the target object.

[0006] Optionally, the step of training CT view determination information based on sample data in the CT image sample dataset and corresponding classification tags specifically includes: inputting any sample in the CT image sample dataset into a first target subview corresponding to the CT view, and obtaining a first actual classification result output from the first target subview; obtaining a judgment tag from the first actual classification result and a classification tag corresponding to any of the samples; correcting decision information corresponding to the CT view based on the decision tag until training of all samples is completed; and obtaining a final decision of the CT view by a minimum learner method based on the decision information corresponding to the corrected CT view.

[0007] Optionally, the step of correcting the decision information corresponding to the CT view based on the decision tag specifically includes: If the decision tag indicates that any of the samples is correctly classified into the first target subview, the decision information in the first target subview is retained; otherwise, the decision information in the first target subview is corrected based on decision information of other subviews other than the first target subview.

[0008] The step of selectively correcting decision information of the first target subview based on decision information of other subviews other than the first target subview specifically includes: if the decision tag indicates that any of the samples is correctly classified into a candidate subview other than the first target subview, correcting decision information in the first target subview based on decision information of the candidate subview; and if the decision tag indicates that any of the samples is correctly classified into multiple candidate subviews other than the first target subview, correcting the decision information in the first target subview based on the decision information of the multiple candidate subviews by a separation degree calculation.

[0009] Optionally, the step of training MRI view determination information based on sample data in an MRI image sample dataset and corresponding classification tags specifically includes: inputting any sample in the MRI image sample dataset into a second target organ sub-view corresponding to the MRI view, and obtaining a second actual classification result output from the second target organ sub-view; obtaining a judgment tag from the second actual classification result and a classification tag corresponding to any of the samples; correcting decision information corresponding to the MRI view based on the decision tag until training of all samples is completed; and obtaining a final decision for the MRI view by a minimum learner method based on the decision information corresponding to the corrected MRI view.

[0010] Optionally, the step of correcting the decision information corresponding to the MRI view based on the decision tag specifically includes: If the decision tag indicates that any of the samples is correctly classified into the second target organ subview, the decision information in the second target organ subview is retained; otherwise, the decision information in the second target organ subview is corrected based on decision information of other organ subviews other than the second target organ subview.

[0011] The step of selectively correcting the determination information in the second target organ subview based on determination information of another subview other than the second target organ subview specifically includes: if the decision tag indicates that any of the samples is correctly classified into a candidate subview other than the second target organ subview, correcting decision information in the second target organ subview based on decision information of the candidate subview; and if the decision tag indicates that any of the samples is correctly classified into a plurality of candidate subviews other than the second target organ subview, correcting the decision information in the second target organ subview based on the decision information of the plurality of candidate subviews by a separation degree calculation.

[0012] Optionally, the step of outputting a medical data classification result of a target object after information fusion of the trained decision information of the CT view and the MRI view is specifically: determining optimal decision information for each of the CT view and the MRI view based on the corrected outputs and corresponding classification tags for each of the CT view and the MRI view; determining one of the CT view and the MRI view as a main view based on a separation error and determining the other as an auxiliary view, and fusing the determination information of the auxiliary view with the determination information of the main view based on a preset synthesis parameter; and calculating and obtaining a medical data classification result of the target object based on the fused decision information.

[0013] Optionally, the plurality of different organ subviews are a lateral parotid gland view, a unilateral parotid gland view, a primary nasopharyngeal carcinoma tumor total volume view, and a metastatic lymph node view.

[0014] In a second aspect, an embodiment of the present invention provides a medical data classification apparatus based on mutual correction and information fusion, comprising: an input module for acquiring one or more CT images and / or one or more MRI images of the target object; a classification module for inputting the plurality of CT images and the plurality of MRI images into a fuzzy classification model and obtaining a medical data classification result output from the fuzzy classification model; a training module for training the fuzzy classification model, a CT view correction submodule for training decision information of a CT view based on sample data in a CT image sample dataset and corresponding classification tags, wherein the CT image sample dataset is respectively used for a scan subview, a first contrast-enhanced subview, and a second contrast-enhanced subview in the CT view to perform mutual correction of the decision information; an MRI view correction submodule for training decision information of an MRI view based on sample data in an MRI image sample dataset and corresponding classification tags, wherein the MRI image sample data are respectively used for a plurality of different organ subviews in the MRI view to perform mutual correction of the decision information; and an information fusion sub-module for fusing the trained decision information of the CT view and the MRI view, and then outputting and obtaining a medical data classification result of the target object. [Effects of the Invention]

[0015] The medical data classification method and apparatus based on mutual correction and information fusion provided in the embodiments of the present invention provides a new fuzzy classifier to ensure high interpretability by implementing the constructed training model. The interpretable zero-order TS fuzzy classifier is used as the basic training unit, and the data generated in the model training process, such as fuzzy rules, rule output, and model output, can be interpreted. Based on the characteristics of high-dimensional features possessed by medical data such as CT and MRI images, KL divergence is used to reduce feature dimension and select more key features from high-dimensional medical data, further facilitating decision-making and reducing the pressure of the decision-making process. From the two views of CT and MRI, multiple subviews in each view are used to correct the decision information required for classification. The advantages of different observation angles complement each other to improve the decision information, thereby improving classification accuracy. Furthermore, information fusion between CT views and MRI views is used to capture diverse decision information, and the advantages of each view in decision-making aspects are utilized to further optimize the decision information. [Brief explanation of the drawings]

[0016] In order to more clearly explain the technical solutions of the embodiments of the present application, the drawings used in the embodiments of the present application will be briefly introduced below.

[0017] [Figure 1] 1 is a flowchart of a medical data classification method based on mutual correction and information fusion provided in an embodiment of the present invention. [Figure 2] 1 is a schematic diagram of a CT view determination information correction method provided in an embodiment of the present invention; [Figure 3] 1 is a flowchart of an MRI view determination information correction method provided in an embodiment of the present invention. [Figure 4] 2 is a flowchart of a decision information information fusion method provided in an embodiment of the present invention; [Figure 5] 1 is a schematic diagram of the structure of a medical data classification device based on mutual correction and information fusion provided in an embodiment of the present invention; [Figure 6] 1 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention; DETAILED DESCRIPTION OF THE INVENTION

[0018] Hereinafter, the technical solutions in the embodiments of the present application will be described with reference to the drawings in the embodiments of the present application.

[0019] In the following drawings, like symbols and letters indicate like items, and therefore, once an item is defined in one drawing, it does not need to be further defined or interpreted in the subsequent drawings. Also, in the description of this application, the terms "first," "second," etc. are merely used to distinguish between the descriptions, and cannot be understood as indicating or implying relative importance.

[0020] With the development of artificial intelligence technology, more and more medical data can be used to discover relevant pathological features. For example, computed tomography (CT) data or magnetic resonance imaging (MRI) data can be used to discover the digitized features of relevant tumors. In clinical diagnosis, many attempts have been made to improve the medical data classification capabilities of artificial intelligence tools. For example, more advanced networks such as three-dimensional convolutional neural networks (CNNs), recurrent neural networks based on courtesy gate control recurrent units, support vector machines (SVMs), and U-net networks have already been successfully applied to the classification of various medical data, such as nasopharyngeal carcinoma.

[0021] The key to classifying medical data is to obtain appropriate key feature information that influences the final decision. Prior art approaches have used the idea of ​​genetic algorithm feature selection or the idea of ​​combining clinical features with MRI-based radiological features. However, these methods use endoscopic images as a single viewing angle, and do not take into account the different resolutions of imaging techniques used in different imaging methods. While subsequent prior art approaches have adopted multi-view models of CT and MR images to address real-world situations, such models lack clear interpretation for feature learning. Furthermore, data images from multiple views often contain a large amount of image information, making automatic detection time-consuming. Due to limitations in the model's classification effectiveness in certain scenarios, manual intervention by a physician is always required to determine the classification results, preventing advanced automatic classification.

[0022] Based on this, an embodiment of the present invention provides a medical data classification method based on mutual correction and information fusion. Figure 1 shows a flowchart of the medical data classification method based on mutual correction and information fusion provided in an embodiment of the present invention.

[0023] In step S110, one or more CT images and / or one or more MRI images of the target object are acquired.

[0024] In the embodiments of the present invention, the target subject is a subject from whom pathological information is extracted, and the pathological information is specifically CT images and MRI images. CT has advantages in observing bones, lungs, bleeding, etc., while MRI has high resolution for soft tissue synovium, blood vessels, nerves, muscles, tendons, ligaments, and transparent cartilage. Therefore, when classifying pathological information, the two images complement each other to provide more comprehensive diagnostic information. In the embodiments of the present invention, the pathology may be of multiple conditions, such as tumors of the head, chest, abdomen, spine, and limbs, tuberculosis, malformations, and inflammation, as long as both the CT image and the MRI image can contribute to the classification of pathological information. In the following embodiments, the pathological information of nasopharyngeal carcinoma is mainly used as an example, and redundant explanations of the application of other pathological information will be omitted.

[0025] Specifically, the CT image and MRI image output in this step may be one or more, and the two types of image data may be input independently or jointly.

[0026] When performing medical data classification, observation information obtained from a single viewing angle of a CT or MRI is largely one-sided. Because the viewing angles are different, it is often difficult to obtain disease information derived from both image features and the location of feedback imaging. In an embodiment of the present invention, information from two different observation viewing angles is fine-tuned, i.e., the decision information from the two viewing angles is modified to obtain more comprehensive decision support. Furthermore, imaging of pathologies such as nasopharyngeal tumors is significantly affected by the imaging technology and the organ itself. However, the decision information obtained from the imaging technology lacks focus on key features of the disease location. Furthermore, the decision information obtained from the organ perspective does not include important features of the imaging technology. By fully utilizing the key features from the two viewing angles and organically combining the decision information obtained from the two viewing angles, greater effectiveness can be achieved.

[0027] In step S120, the plurality of CT images and the plurality of MRI images are input into a fuzzy classification model, and a medical data classification result is output from the fuzzy classification model.

[0028] To ensure high interpretability when implementing the constructed training model, an embodiment of the present invention provides a new fuzzy classifier. It uses an interpretable zero-order Takagi-Sugeno-Kang (TS) fuzzy classifier as the basic training unit, achieving mutual correction and fusion of two types of view angles. The data generated from the fuzzy rules, rule output, and model output training model must be interpretable. The TS fuzzy classifier has natural nonlinear approximation capabilities and concise language interpretability. Therefore, it is widely used in trajectory tracking control, fault prediction, medicine, and other fields.

[0029] As those skilled in the art will appreciate, a TS fuzzy classifier may include an input layer, a membership layer, a rule layer including a multi-layer structure, and an output layer. A zero-order TS fuzzy classifier has excellent nonlinear approximation capabilities and uses "IF-THEN" statements to define rules in the rule layer of a fuzzy system. This classifier always has linguistically interpretable fuzzy rules. The kth rule of the classifier is:

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[0030] Also,

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[0031] In an embodiment of the present invention, CT and MR images have high-dimensional features, which is directly related to their huge processing requirements. Therefore, the TS fuzzy classifier uses KL divergence (KLIC) as a metric to evaluate the similarity of two probability distributions, thereby reducing the feature dimension and selecting more key features from high-dimensional medical images, further making the decision more convenient and reducing the pressure of the decision-making process.

[0032] Specifically, KLIC used in the embodiment of the present invention is the degree to which the uncertainty of the data set information is reduced under the condition of a certain feature, which reflects the feature's ability to distinguish data samples. It is a common feature selection method and can be expressed as follows:

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[0033] Data set

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[0034] The key to the embodiment of the present invention is the training process of the fuzzy classification model, which specifically includes the following steps S130 to S150:

[0035] In step S130, the decision information of the CT view is trained based on the sample data in the CT image sample dataset and its corresponding classification tag, and the CT image sample dataset is respectively used for the scan subview, the first contrast-enhanced subview and the second contrast-enhanced subview in the CT view to perform mutual correction of the decision information.

[0036] As shown in the flowchart of the CT view determination information correction method provided in the embodiment of the present invention in FIG. 2, step S130 specifically includes: Step S131: inputting any sample in the CT image sample dataset into a first target subview corresponding to the CT view, and obtaining a first actual classification result output from the first target subview; Step S132 of obtaining a judgment tag from the first actual classification result and a classification tag corresponding to any of the samples; Step S133: correcting the decision information corresponding to the CT view based on the decision tag until training of all samples is completed; and step S134 of obtaining a final decision of the CT view by a minimum learner method based on the decision information corresponding to the CT view after correction.

[0037] Furthermore, step S134 specifically includes: if the decision tag indicates that any of the samples is correctly classified into the first target subview, retaining decision information in the first target subview; Otherwise, if the decision tag indicates that any of the samples is correctly classified into a candidate subview other than the first target subview, correct the decision information in the first target subview based on the decision information of the candidate subview; if the decision tag indicates that any of the samples is correctly classified into multiple candidate subviews other than the first target subview, correct the decision information in the first target subview based on the decision information of the multiple candidate subviews by separation degree calculation.

[0038] In step S140, the decision information of the MRI view is trained based on the sample data in the MRI image sample dataset and the corresponding classification tags, and the MRI image sample data are respectively used for multiple different organ subviews in the MRI view to perform mutual correction of the decision information.

[0039] As shown in the flowchart of the MRI view determination information correction method provided in the embodiment of the present invention in FIG. 3, step S140 specifically includes: Step S141: inputting any sample in the MRI image sample data set into a second target organ subview corresponding to the MRI view, and obtaining a second actual classification result output from the second target organ subview; Step S142 of obtaining a judgment tag from the second actual classification result and a classification tag corresponding to any of the samples; Step S143: correcting the decision information corresponding to the MRI view based on the decision tag until training of all samples is completed; and step S144 of obtaining a final decision for the MRI view by a minimum learner method based on the decision information corresponding to the corrected MRI view.

[0040] Specifically, step S144 further includes retaining decision information in the second target organ subview if the decision tag indicates that any of the samples is correctly classified into the second target organ subview; otherwise, if the decision tag indicates that any of the samples is correctly classified into a candidate subview other than the second target organ subview, correcting decision information in the second target organ subview based on decision information of the candidate subview; If the decision tag indicates that any of the samples is correctly classified into multiple candidate subviews other than the second target organ subview, a separation degree calculation is performed to correct the decision information in the second target organ subview based on the decision information of the multiple candidate subviews.

[0041] In terms of the specific execution process of steps S130 and S140, this embodiment of the present invention trains two training views, namely, a CT view and an MRI view, which constitute a fuzzy classification model, and both views use the TS fuzzy classifier as a basic training unit. The training purpose is to complete mutual correction of each sub-view within each of the CT view and the MRI view at a single viewing angle, thereby generating decision information used in the rule layer of the TS fuzzy classifier after correction for each view.

[0042] Here, the CT view includes a scan subview, a first contrast-enhanced subview, and a second contrast-enhanced subview, where the scan subview corresponds to medical data obtained from the original CT scan, the first contrast-enhanced subview corresponds to medical data from the CT scan after enhancement with a first contrast parameter, and the second contrast-enhanced subview corresponds to medical data from the CT scan after enhancement with a second contrast parameter.

[0043] The MRI view includes multiple different organ subviews, and the number of organ subviews is determined according to the specific pathology. For example, for the pathology of nasopharyngeal carcinoma, the multiple different organ subviews may be four organ subviews, such as a lateral parotid gland view, a unilateral parotid gland view, a primary nasopharyngeal carcinoma tumor total volume view, and a metastatic lymph node view.

[0044] Next, a CT view determination information correction method in specific steps S131 to S134 included in step S130 will be described in detail.

[0045] This step performs decision compensation for three subviews from the perspective of CT imaging technology, calculating computed tomography (CT), contrast-enhanced T1 weighted (CET1) and contrast-enhanced T2 weighted (CET2). The TS fuzzy classifier trains these three modules to generate filtering conditions and decision compensation information. The training dataset is

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[0046] First, we use FCM clustering to find the training set

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[0047] Second, select a fuzzy rule based on Equation 8: ω ik is calculated and used as the antecedent of rule k.

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[0048] Next, the output parameters of the hidden layer are calculated using the method described above.

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[0049] As mentioned above, the minimum learning matrix (LLM) method is used to calculate the output weight vector

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[0050] Based on the above method, the training is completed for the viewing angle of CT imaging technology. The decision information is the key to fuzzy classification, and the optimized classifier is the decision information

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[0051] H (1,1) , H (1,2) and H (1,3) are the output matrix parameters of the CT, CET1, and CET2 models, respectively, and Y (1,1) , Y (1,2) and Y (1,3) are the corresponding real outputs. The output class tag set is

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[0052] T (1,1) , T(1,2) and T (1,3) are the output classification tag sets of the CT, CET1, and CET2 models, respectively. Taking the CT module as an example, the class tags output by the CT model are

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[0053] The decision information is processed sequentially to obtain the decision parameter Z (1,1) Calculate and correct.

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[0054] Case A: A single training unit is correctly identified (i.e.,

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[0055] Case B: A single training unit is misidentified (i.e.,

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[0056] Case B-1: When one training unit accurately recognizes one view (i.e., e n (1) =1) Decision information selected only if classification p1′ of image technology tag matches

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[0057] Case B-2: When the training unit's accurate recognition of a certain view is not unique (i.e., ..., e n (1) ≠1) Furthermore, a set of image technologies p1 that meet the conditions

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[0058] degree of separation

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[0059] After correcting the decision information, the consequent parameters are generated using LLM.

[0060] Similarly, an MRI view is similar to a CT view, and next, a method for correcting CT view determination information in specific steps S131 to S134 included in step S130 will be described in detail.

[0061] Each sample was observed using four organ subviews: the lateral parotid gland subview (CPG), the unilateral parotid gland subview (IPG), the total volume of the primary nasopharyngeal carcinoma tumor subview (GTVnp), and the metastatic lymph node subview (GTVn). Again, the nasopharyngeal carcinoma pathology was used as the reference, and other pathologies were similar and will not be described.

[0062] Then, by comparing the classification results, the decision information of each subview is corrected and the decision information of each module is optimized.

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[0063] Using equations (7) to (14), TS fuzzy training is performed on the MRI views, and the decision information is

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[0064] Output matrix parameters H of CPG, IPG, GTVnp, and GTVn modules (2,1) , H (2,2) , H (2,3) andH (2,4) and the actual output Y (2,1) , Y (2,2) , Y (2,3) andY (2,4) , and the output class tag set T (2,1) , T (2,2) , T (2,3) andT (2,4) It is known that the output class tag results of the CPG module are

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[0065] Case C: A single training unit is correctly identified (i.e.,

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[0066] Case D: A single training unit is misidentified (i.e.,

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[0067] Case D-1: When one training unit accurately identifies one view (i.e., e n (2) =1) Decision information selected only if the classification of organ tag p′2 matches

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[0068] Case D-2: The training unit is not unique in correctly identifying one view (i.e., e n (2) ≠1) Furthermore, the set of organ subviews p2 that meet the conditions

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[0069] After correcting the decision information, the subsequent parameters are calculated by LLM.

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[0070] In step S150, the trained decision information of the CT view and the MRI view is fused, and then the medical data classification result of the target object is obtained as an output.

[0071] Specifically, as shown in the flowchart of the information fusion method for decision information in FIG. 4, step S150 is further divided into steps S151 to S153.

[0072] In step S151, optimal decision information for each of the CT view and the MRI view is determined based on the corrected outputs of each of the CT view and the MRI view and the corresponding classification tags.

[0073] In step S152, one of the CT view and the MRI view is determined as a main view based on the separation error, and the other is determined as an auxiliary view, and the determination information of the auxiliary view is merged with the determination information of the main view based on a preset synthesis parameter.

[0074] In step S153, the medical data classification result of the target object is calculated based on the fused decision information.

[0075] In an embodiment of the present invention, the classification of various pathologies relies on CT image information and MRI organ tissue structure information. Without physician intervention, these two perspectives interact and cannot mutually support decision information. Looking solely at the perspective of CT imaging technology, the pathology-specific characteristics of the examined organ are ignored. Similarly, analyzing solely from the perspective of MRI organ image data does not take into account the differences in function space between each CT imaging technology. After performing internal correction for the two perspectives, the decision information is adjusted alternately. Specific implementation steps are as follows:

[0076] First, before combining the information from the two views, it is necessary to perform internal selection on the optimization decision information provided by each view, determine the synthesis parameters for each view, and simplify the subsequent synthesis process. Taking the CT view as an example, the optimized output

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[0077] Considering the representation of the final decision from two views, the model uses the decision information from one view as the center and coordinates the information from the other view with the center. The separation error v is a key selection parameter applied to the main decision information. The phase separation error of the two views is

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[0078] Further more were obtained

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[0079] After correcting the decision information, consequent parameters are generated and are the key parameters of the testing process that subsequently direct the synthesis of the training algorithm of the embodiment of the present invention.

[0080] The medical data classification method based on mutual correction and information fusion provided in the embodiment of the present invention provides a new fuzzy classifier to ensure high interpretability by implementing the constructed training model. It uses an interpretable zero-order TS fuzzy classifier as the basic training unit, making the data generated in the model training process, such as fuzzy rules, rule output, and model output, interpretable. Based on the characteristics of high-dimensional features possessed by medical data such as CT and MRI images, KL divergence is used to reduce feature dimension and select more key features from high-dimensional medical data, further facilitating decision-making and reducing the pressure of the decision-making process. From the two views of CT and MRI, multiple subviews in each view are used to correct the decision information required for classification. The advantages of different observation angles complement each other to improve the decision information, thereby improving classification accuracy. Furthermore, information fusion between CT views and MRI views is used to capture diverse decision information, utilizing the advantages of each view in decision-making aspects to further optimize the decision information.

[0081] Based on any of the above embodiments, FIG. 5 illustrates a medical data classification device based on mutual correction and information fusion provided in an embodiment of the present invention, the device including: an input module 510 for acquiring one or more CT images and / or one or more MRI images of the target object; a classification module 520 for inputting the plurality of CT images and the plurality of MRI images into a fuzzy classification model and obtaining a medical data classification result output from the fuzzy classification model; a training module 530 for training the fuzzy classification model, a CT view correction submodule 531 for training decision information of a CT view based on sample data in a CT image sample dataset and its corresponding classification tag, wherein the CT image sample dataset is respectively used for a scan subview, a first contrast-enhanced subview, and a second contrast-enhanced subview in the CT view to perform mutual correction of the decision information; an MRI view correction sub-module 532 for training decision information of an MRI view based on sample data in an MRI image sample dataset and its corresponding classification tag, wherein the MRI image sample data are respectively used for a plurality of different organ sub-views in the MRI view to perform mutual correction of the decision information; and a training module 530 including an information fusion sub-module 533 for fusing the trained decision information of the CT view and the MRI view, and then outputting and obtaining a medical data classification result of the target object.

[0082] The medical data classification device based on mutual correction and information fusion provided in the embodiment of the present invention provides a new fuzzy classifier to ensure high interpretability by executing the constructed training model. It uses an interpretable zero-order TS fuzzy classifier as the basic training unit, making the data generated in the model training process, such as fuzzy rules, rule output, and model output, interpretable. Based on the characteristics of high-dimensional features possessed by medical data such as CT and MRI images, KL divergence is used to reduce feature dimension and select more key features from high-dimensional medical data, further facilitating decision-making and reducing the pressure of the decision-making process. From the two views of CT and MRI, multiple subviews in each view are used to correct the decision information required for classification. The advantages of different observation angles complement each other to improve the decision information, thereby improving classification accuracy. Furthermore, information fusion between CT views and MRI views is used to capture diverse decision information, utilizing the advantages of each view in decision-making aspects to further optimize the decision information.

[0083] Based on any of the above embodiments, Figure 6 shows a schematic diagram of the physical structure of an electronic device provided in an embodiment of the present invention, which may include a processor 610, a communications interface 620, a memory 630, and a communications bus 640, where the processor 610, the communications interface 620, and the memory 630 communicate with each other via the communications bus 640. The processor 610 can invoke logic instructions in the memory 630 to perform the following method:

[0084] acquiring one or more CT images and / or one or more MRI images of the target object; inputting the plurality of CT images and the plurality of MRI images into a fuzzy classification model, and obtaining a medical data classification result output from the fuzzy classification model; Wherein, the training process of the fuzzy classification model is training decision information of a CT view based on sample data in a CT image sample dataset and its corresponding classification tag, wherein the CT image sample datasets are respectively used for a scan subview, a first contrast-enhanced subview, and a second contrast-enhanced subview in the CT view to mutually correct the decision information; training decision information for an MRI view based on sample data in an MRI image sample dataset and corresponding classification tags, wherein the MRI image sample data are respectively used for a plurality of different organ sub-views in the MRI view to mutually correct the decision information; and outputting the medical data classification result of the target object after information fusion of the trained decision information of the CT view and the MRI view.

[0085] Furthermore, the logic instructions in the memory 630 can be implemented in the form of a software functional unit and stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solutions of the embodiments of the present invention can be essentially embodied in the form of a software product, or a portion of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a U disk, a portable hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0086] On the other hand, an embodiment of the present invention further provides a non-transitory computer-readable storage medium storing a computer program, which when executed by a processor, performs the method provided in the above embodiment, e.g. acquiring one or more CT images and / or one or more MRI images of the target object; inputting the plurality of CT images and the plurality of MRI images into a fuzzy classification model, and obtaining a medical data classification result output from the fuzzy classification model; Wherein, the training process of the fuzzy classification model is training decision information of a CT view based on sample data in a CT image sample dataset and its corresponding classification tag, wherein the CT image sample datasets are respectively used for a scan subview, a first contrast-enhanced subview, and a second contrast-enhanced subview in the CT view to mutually correct the decision information; training decision information for an MRI view based on sample data in an MRI image sample dataset and corresponding classification tags, wherein the MRI image sample data are respectively used for a plurality of different organ sub-views in the MRI view to mutually correct the decision information; and outputting the medical data classification result of the target object after information fusion of the trained decision information of the CT view and the MRI view.

[0087] The above-described device embodiments are merely illustrative, and the units described as separate components may or may not be physically separate, and the components represented as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Depending on actual needs, some or all of the modules may be selected to achieve the objectives of the solutions of the present embodiments. Those skilled in the art can understand and implement them without any creative effort.

[0088] From the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be realized by adding a required general-purpose hardware platform to software, and of course by hardware. Based on this understanding, the above technical solutions can essentially be embodied or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product is stored in a computer-readable storage medium such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to execute the method described in each embodiment or a certain part of the embodiment.

[0089] Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention, and are not intended to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can still modify the technical solutions described in the above embodiments or replace some technical features with equivalents, and these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

[0090] (Addendum) (Appendix 1) acquiring one or more CT images and / or one or more MRI images of the target object; inputting the plurality of CT images and the plurality of MRI images into a fuzzy classification model, and obtaining a medical data classification result output from the fuzzy classification model; Wherein, the training process of the fuzzy classification model is training decision information of a CT view based on sample data in a CT image sample dataset and its corresponding classification tag, wherein the CT image sample datasets are respectively used for a scan subview, a first contrast-enhanced subview, and a second contrast-enhanced subview in the CT view to mutually correct the decision information; training decision information for an MRI view based on sample data in an MRI image sample dataset and corresponding classification tags, wherein the MRI image sample data are respectively used for a plurality of different organ sub-views in the MRI view to mutually correct the decision information; and performing information fusion on the trained decision information of the CT view and the MRI view, and then outputting the medical data classification result of the target object.

[0091] (Appendix 2) The step of training CT view determination information based on sample data in the CT image sample dataset and corresponding classification tags specifically includes: inputting any sample in the CT image sample dataset into a first target subview corresponding to the CT view, and obtaining a first actual classification result output from the first target subview; obtaining a judgment tag from the first actual classification result and a classification tag corresponding to any of the samples; correcting decision information corresponding to the CT view based on the decision tag until training of all samples is completed; and obtaining a final decision for the CT view by a minimal learner method based on decision information corresponding to the corrected CT view.

[0092] (Appendix 3) Specifically, the step of correcting the decision information corresponding to the CT view based on the decision tag includes: 3. The medical data classification method of claim 2, further comprising the step of: retaining decision information in the first target subview if the decision tag indicates that any of the samples is correctly classified into the first target subview; and otherwise correcting the decision information in the first target subview based on decision information of other subviews other than the first target subview.

[0093] (Appendix 4) Specifically, the step of correcting the determination information of the first target subview based on the determination information of subviews other than the first target subview includes: if the decision tag indicates that any of the samples is correctly classified into a candidate subview other than the first target subview, correcting decision information in the first target subview based on decision information of the candidate subview; and correcting decision information in the first target subview based on decision information of the plurality of candidate subviews by separation degree calculation when the decision tag indicates that any of the samples is correctly classified into a plurality of candidate subviews other than the first target subview.

[0094] (Appendix 5) Specifically, the step of training MRI view determination information based on sample data in the MRI image sample dataset and corresponding classification tags includes: inputting any sample in the MRI image sample dataset into a second target organ sub-view corresponding to the MRI view, and obtaining a second actual classification result output from the second target organ sub-view; obtaining a judgment tag from the second actual classification result and a classification tag corresponding to any of the samples; correcting decision information corresponding to the MRI view based on the decision tag until training of all samples is completed; and obtaining a final decision for the MRI view by a minimal learner method based on decision information corresponding to the corrected MRI view.

[0095] (Appendix 6) Specifically, the step of correcting the decision information corresponding to the MRI view based on the decision tag includes: The medical data classification method described in Appendix 5, characterized in that it includes a step of retaining decision information in the second target organ subview if the decision tag indicates that any of the samples is correctly classified into the second target organ subview, and otherwise correcting the decision information in the second target organ subview based on decision information of other organ subviews other than the second target organ subview.

[0096] (Appendix 7) Specifically, the step of correcting the determination information in the second target organ subview based on the determination information of another subview other than the second target organ subview includes: if the decision tag indicates that any of the samples is correctly classified into a candidate subview other than the second target organ subview, correcting decision information in the second target organ subview based on decision information of the candidate subview; and correcting the decision information in the second target organ subview based on the decision information of the plurality of candidate subviews by a separation degree calculation when the decision tag indicates that any of the samples is correctly classified into a plurality of candidate subviews other than the second target organ subview.

[0097] (Appendix 8) The step of outputting the medical data classification result of the target object after information fusion of the trained decision information of the CT view and the MRI view, specifically, determining optimal decision information for each of the CT view and the MRI view based on the corrected outputs and corresponding classification tags for each of the CT view and the MRI view; determining one of the CT view and the MRI view as a main view based on a separation error and determining the other as an auxiliary view, and fusing the determination information of the auxiliary view with the determination information of the main view based on a preset synthesis parameter; and calculating and obtaining a medical data classification result for the target object based on the fused decision information.

[0098] (Appendix 9) 2. The medical data classification method of claim 1, wherein the plurality of different organ subviews are a lateral parotid gland view, a unilateral parotid gland view, a primary nasopharyngeal carcinoma tumor total volume view, and a metastatic lymph node view.

[0099] (Appendix 10) an input module for acquiring one or more CT images and / or one or more MRI images of the target object; a classification module for inputting the plurality of CT images and the plurality of MRI images into a fuzzy classification model and obtaining a medical data classification result output from the fuzzy classification model; a training module for training the fuzzy classification model, a CT view correction submodule for training decision information of a CT view based on sample data in a CT image sample dataset and corresponding classification tags, wherein the CT image sample dataset is respectively used for a scan subview, a first contrast-enhanced subview, and a second contrast-enhanced subview in the CT view to perform mutual correction of the decision information; an MRI view correction submodule for training decision information of an MRI view based on sample data in an MRI image sample dataset and corresponding classification tags, wherein the MRI image sample data are respectively used for a plurality of different organ subviews in the MRI view to perform mutual correction of the decision information; and an information fusion sub-module for fusing the trained decision information of the CT view and the MRI view, and then outputting and obtaining a medical data classification result of the target object.

Claims

1. acquiring one or more CT images and / or one or more MRI images of the target object; inputting the plurality of CT images and the plurality of MRI images into a fuzzy classification model, and obtaining a medical data classification result output from the fuzzy classification model; Wherein, the training process of the fuzzy classification model is training decision information for a CT view based on sample data in a CT image sample dataset and its corresponding classification tag, wherein the CT image sample dataset is respectively used for a scan sub-view, a first contrast-enhanced sub-view, and a second contrast-enhanced sub-view in the CT view to perform mutual correction of the decision information; training decision information for an MRI view based on sample data in an MRI image sample dataset and corresponding classification tags, wherein the MRI image sample data are respectively used for a plurality of different organ sub-views in the MRI view to perform mutual correction of the decision information; and performing information fusion on the trained decision information of the CT view and the MRI view, and then outputting the medical data classification result of the target object.

2. Specifically, the step of training CT view determination information based on sample data in the CT image sample dataset and corresponding classification tags includes: inputting any sample in the CT image sample dataset into a first target subview corresponding to the CT view, and obtaining a first actual classification result output from the first target subview; obtaining a decision tag from the first actual classification result and a classification tag corresponding to any of the samples; correcting decision information corresponding to the CT view based on the decision tag until training of all samples is completed; and obtaining a final decision for the CT view by a minimum learner method based on the decision information corresponding to the corrected CT view.

3. Specifically, the step of correcting the decision information corresponding to the CT view based on the decision tag includes:

3. The medical data classification method of claim 2, further comprising the step of: retaining decision information in the first target subview if the decision tag indicates that any of the samples is correctly classified into the first target subview; and otherwise correcting the decision information in the first target subview based on decision information of other subviews other than the first target subview.

4. Specifically, the step of correcting the determination information of the first target subview based on the determination information of subviews other than the first target subview includes: if the decision tag indicates that any of the samples is correctly classified into a candidate subview other than the first target subview, correcting decision information in the first target subview based on decision information of the candidate subview; and correcting decision information in the first target subview based on decision information of the plurality of candidate subviews by a separation degree calculation when the decision tag indicates that any of the samples is correctly classified into a plurality of candidate subviews other than the first target subview.

5. Specifically, the step of training MRI view determination information based on sample data in the MRI image sample dataset and its corresponding classification tag includes: inputting any sample in the MRI image sample dataset into a second target organ sub-view corresponding to the MRI view, and obtaining a second actual classification result output from the second target organ sub-view; obtaining a decision tag from the second actual classification result and a classification tag corresponding to any of the samples; correcting decision information corresponding to the MRI view based on the decision tag until training of all samples is completed; and obtaining a final decision for the MRI view by a minimum learner method based on decision information corresponding to the corrected MRI view.

6. Specifically, the step of correcting the decision information corresponding to the MRI view based on the decision tag includes:

6. The medical data classification method of claim 5, further comprising the step of: retaining decision information in the second target organ subview if the decision tag indicates that any of the samples is correctly classified into the second target organ subview; and otherwise correcting the decision information in the second target organ subview based on decision information of other organ subviews other than the second target organ subview.

7. Specifically, the step of correcting the determination information in the second target organ subview based on the determination information of another subview other than the second target organ subview includes: if the decision tag indicates that any of the samples is correctly classified into a candidate subview other than the second target organ subview, correcting decision information in the second target organ subview based on decision information of the candidate subview; 7. The medical data classification method of claim 6, further comprising: if the decision tag indicates that any of the samples is correctly classified into a plurality of candidate subviews other than the second target organ subview, correcting the decision information in the second target organ subview based on the decision information of the plurality of candidate subviews by a separation degree calculation.

8. The step of outputting a medical data classification result of a target object after information fusion of the trained decision information of the CT view and the MRI view, specifically, includes: determining optimal decision information for each of the CT view and the MRI view based on the corrected outputs and corresponding classification tags for each of the CT view and the MRI view; determining one of the CT view and the MRI view as a main view and the other as an auxiliary view based on a separation error, and fusing the determined information of the auxiliary view with the determined information of the main view based on a preset synthesis parameter; and calculating and obtaining a medical data classification result of the target object based on the fused decision information.

9. 2. The method of claim 1, wherein the plurality of different organ subviews are a lateral parotid gland view, a unilateral parotid gland view, a primary nasopharyngeal carcinoma tumor total volume view, and a metastatic lymph node view.

10. an input module for acquiring one or more CT images and / or one or more MRI images of the target object; a classification module for inputting the plurality of CT images and the plurality of MRI images into a fuzzy classification model and obtaining a medical data classification result output from the fuzzy classification model; a training module for training the fuzzy classification model, a CT view correction submodule for training decision information of a CT view based on sample data in a CT image sample dataset and its corresponding classification tag, wherein the CT image sample dataset is respectively used for a scan subview, a first contrast-enhanced subview, and a second contrast-enhanced subview in the CT view to perform mutual correction of the decision information; an MRI view correction sub-module for training decision information of an MRI view based on sample data in an MRI image sample dataset and its corresponding classification tag, wherein the MRI image sample data is respectively used for a plurality of different organ sub-views in the MRI view to perform mutual correction of the decision information; and an information fusion sub-module for fusing the trained decision information of the CT view and the MRI view, and then outputting the medical data classification result of the target object.

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