Case analysis auxiliary system based on lung cancer diagnosis and treatment

Abstract

The invention discloses a case analysis auxiliary system based on lung cancer diagnosis and treatment, and the system comprises a case data preprocessing module which is used for cleaning, standardizing and enhancing medical image data so as to remove noise, correct distortion and fill missing values; the case data feature extraction module is used for collecting and numbering case analysis accuracy data, case expansion data and model generalization ability data; the case data verification module is connected with the case data feature extraction module through a network, and calculates an accuracy improvement value Xv, a data acquisition expansion rate Yv and a model generalization ability improvement index Rv; and the case intelligent decision module is connected with the case data verification module through a network to improve the case accuracy and expand the case data volume. According to the method, the marking accuracy of the pulmonary nodule samples can be improved, an infinite number of samples can be processed according to an automatic processing process, and the limitation of manual marking on quality and number is overcome.

Description

Technical Field

[0001] The present invention belongs to the technical field of case analysis, and in particular relates to a case analysis auxiliary system based on lung cancer diagnosis and treatment. Background Art

[0002] The case analysis assistance system based on lung cancer diagnosis and treatment is a multidisciplinary system that integrates medical image processing technology, artificial intelligence technology, machine learning technology and data mining technology. This system aims to assist doctors in making more accurate judgments in the early diagnosis and treatment of lung cancer through high-precision medical image analysis. Medical image processing technology enables the system to extract valuable information from examinations such as CT scans, while artificial intelligence and machine learning technologies are responsible for analyzing and interpreting this data to identify lung lesions and other relevant indicators.

[0003] However, the development of such systems faces several technical challenges. The primary issue is obtaining high-quality, large amounts of medical imaging data for training machine learning models. In practice, medical imaging data is often noisy, may contain missing values, and the amount of data is often insufficient for training. In addition, machine learning models typically require large amounts of data to avoid overfitting and ensure that the model has sufficient generalization capabilities to perform well on new data. Therefore, limitations in data quality and quantity are one of the main obstacles facing current technology. Summary of the invention

[0004] To address the deficiencies of the prior art, the purpose of the present invention is to provide a case analysis auxiliary system based on lung cancer diagnosis and treatment, which helps to improve the labeling accuracy of lung nodule samples. According to the automated processing process, an unlimited number of samples can be processed, overcoming the limitations of manual labeling in terms of quality and quantity.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] Case analysis auxiliary system based on lung cancer diagnosis and treatment, including:

[0007] Case data preprocessing module, which is used to clean, standardize and enhance medical image data to remove noise, correct distortion and fill missing values, and use medical image processing techniques such as segmentation, classification and feature extraction to convert raw medical images into structured information that can be used for further analysis;

[0008] Case data feature extraction module, used to collect case analysis accuracy data, case expansion data and model generalization ability data, and number them;

[0009] The case data verification module is connected to the case data feature extraction module through the network, and calculates the accuracy improvement value Xv, the data acquisition expansion rate Yv and the model generalization ability improvement index Rv respectively according to the case analysis accuracy data, case expansion data and model generalization ability data collected by the case data feature extraction module;

[0010] The case intelligent decision-making module is connected to the case data verification module through the network, and improves the case accuracy and expands the case data volume according to the accuracy improvement value Xv and the data acquisition expansion rate Yv.

[0011] Preferably, the aforementioned case data feature extraction module includes:

[0012] The case analysis accuracy unit collects case analysis accuracy data through the hospital Internet client, numbers them, and connects with the case data verification module through the network;

[0013] The case expansion unit collects case expansion data through the hospital Internet client, numbers them, and connects with the case data verification module through the network;

[0014] The model generalization capability unit collects model generalization capability data through the hospital Internet client, numbers them, and connects them to the case data verification module through the network.

[0015] Preferably, the case analysis accuracy unit performs data numbering on the total number of pulmonary nodule samples annotated by the original annotation method, the number of pulmonary nodule samples correctly annotated by the original annotation method, the number of pulmonary nodule samples correctly annotated by the intelligent auxiliary annotation method, and the total number of pulmonary nodule samples annotated by the intelligent auxiliary annotation method according to the case analysis accuracy data characteristics, and the total number of pulmonary nodule samples annotated by the original annotation method, the number of pulmonary nodule samples correctly annotated by the original annotation method, the number of pulmonary nodule samples correctly annotated by the intelligent auxiliary annotation method, and the total number of pulmonary nodule samples annotated by the intelligent auxiliary annotation method are numbered M respectively. 1 、M 2 、M 3 、M 4 .

[0016] Preferably, the aforementioned case data verification module includes:

[0017] The data accuracy improvement unit calculates the accuracy improvement value Xv based on the case analysis accuracy data;

[0018] A data volume expansion unit calculates the data acquisition expansion rate Yv according to the case expansion data;

[0019] The model generalization capability enhancement unit calculates the model generalization capability improvement index Rv based on the model generalization capability data.

[0020] Preferably, the calculation formula of the accuracy improvement value Xv of the aforementioned data accuracy improvement unit is:

[0021]

[0022] In the formula, M 1 、M 2 、M 3 、M 4 They represent the total number of pulmonary nodule samples labeled by the original labeling method, the number of pulmonary nodule samples correctly labeled by the original labeling method, the number of pulmonary nodule samples correctly labeled by the intelligent assisted labeling method, and the total number of pulmonary nodule samples labeled by the intelligent assisted labeling method. represents the accuracy of intelligent labeling, Indicates the original annotation accuracy.

[0023] Preferably, the case expansion unit numbers the original data volume, the auxiliary data volume and the telemedicine system data volume according to the case expansion data characteristics, and the original data volume is numbered as U 1 , U 2 , U 3 ,…U n , the auxiliary data volume number is Ur 1 、Ur 2 、Ur 3 、…Ur n The data volume of the telemedicine system is numbered as Ue 1 , Ue 2 , Ue 3 ,… n .

[0024] Preferably, the calculation formula of the data acquisition expansion rate Yv of the aforementioned data volume expansion unit is:

[0025]

[0026] In the formula, Yv represents the data acquisition expansion rate, U 1 , U 2 , U 3 ,…U n Indicates the amount of original data, Ur 1 、Ur 2 、Ur 3 、…Ur n Indicates the amount of auxiliary data, Ue 1 , Ue 2 , Ue 3 ,… n Represents the amount of data in the telemedicine system.

[0027] Preferably, the model generalization capability unit numbers the number of samples correctly identified by the model under multiple different medical environments, the total number of samples under multiple different medical environments, the number of samples correctly identified by the model under a single medical environment, and the total number of samples under a single medical environment according to the model generalization capability data characteristics, and the number of samples correctly identified by the model under multiple different medical environments, the total number of samples under multiple different medical environments, the number of samples correctly identified by the model under a single medical environment, and the total number of samples under a single medical environment are numbered K 1 , K 2 , K 3 , K 4 .

[0028] Preferably, the calculation formula of the model generalization ability enhancement unit for the model generalization ability improvement index Rv is:

[0029]

[0030] In the formula, Rv represents the index of improvement in model generalization ability, K 1 , K 2 , K 3 , K 4 They represent the number of samples correctly identified by the model under multiple different medical environments, the total number of samples under multiple different medical environments, the number of samples correctly identified by the model under a single medical environment, and the total number of samples under a single medical environment. Indicates the accuracy of multi-environment adaptability, Indicates the accuracy of a single environment.

[0031] Preferably, it also includes: a case intelligent management module, which is connected to the case intelligent decision-making module through a network, and performs parameter management on the case analysis accuracy unit, the case expansion unit and the model generalization ability unit according to the model generalization ability improvement index Rv.

[0032] The benefits of the present invention are as follows: the case intelligent decision module of the present invention can automatically and effectively evaluate the intelligent auxiliary labeling method by calculating the accuracy improvement value Xv, which helps to improve the labeling accuracy of lung nodule samples. According to the automated processing process, an unlimited number of samples can be processed, thereby overcoming the limitations of manual labeling in quality and quantity; by calculating the data acquisition expansion rate Yv, it helps to evaluate the data volume expansion effect after introducing auxiliary data and telemedicine system data, thereby solving the problem of data quality and quantity limitations; by calculating the model generalization ability improvement index Rv, it helps to evaluate the generalization ability of the auxiliary model in different medical environments, ensure the reliability and effectiveness of the auxiliary model in practical applications, and by comparing the accuracy in different environments, the adaptability of the auxiliary model to environmental changes can be evaluated, thereby further guiding the optimization and improvement plan of the auxiliary model. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION

[0034] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0035] See also Figure 1 The case analysis auxiliary system based on lung cancer diagnosis and treatment of the present invention comprises:

[0036] Case data preprocessing module, which is used to clean, standardize and enhance medical image data to remove noise, correct distortion and fill missing values, and use medical image processing techniques such as segmentation, classification and feature extraction to convert raw medical images into structured information that can be used for further analysis;

[0037] The case data feature extraction module includes: a case analysis accuracy unit, which collects case analysis accuracy data through the hospital Internet client, numbers it, and connects it to the case data verification module through the network; a case expansion unit, which collects case expansion data through the hospital Internet client, numbers it, and connects it to the case data verification module through the network; a model generalization capability unit, which collects model generalization capability data through the hospital Internet client, numbers it, and connects it to the case data verification module through the network;

[0038] The case data verification module is connected to the case data feature extraction module through the network, including: a data accuracy improvement unit, which calculates the accuracy improvement value Xv based on the case analysis accuracy data; a data volume expansion unit, which calculates the data acquisition expansion rate Yv based on the case expansion data; a model generalization ability enhancement unit, which calculates the model generalization ability improvement index Rv based on the model generalization ability data

[0039] The case intelligent decision module is connected to the case data verification module through the network to improve the case accuracy and expand the case data volume according to the accuracy improvement value Xv and the data acquisition expansion rate Yv;

[0040] The case intelligent management module is connected to the case intelligent decision-making module through the network, and manages the parameters of the case analysis accuracy unit, case expansion unit and model generalization ability unit according to the model generalization ability improvement index Rv.

[0041] The case analysis accuracy unit performs data numbering on the total number of pulmonary nodule samples annotated by the original annotation method, the number of pulmonary nodule samples correctly annotated by the original annotation method, the number of pulmonary nodule samples correctly annotated by the intelligent auxiliary annotation method, and the total number of pulmonary nodule samples annotated by the intelligent auxiliary annotation method according to the case analysis accuracy data characteristics, and the total number of pulmonary nodule samples annotated by the original annotation method, the number of pulmonary nodule samples correctly annotated by the original annotation method, the number of pulmonary nodule samples correctly annotated by the intelligent auxiliary annotation method, and the total number of pulmonary nodule samples annotated by the intelligent auxiliary annotation method are numbered M respectively. 1 、M 2 、M 3 、M 4 .

[0042] The calculation formula for the accuracy improvement value Xv of the data accuracy improvement unit is:

[0043]

[0044] In the formula, M 1 、M 2 、M 3 、M 4 They represent the total number of pulmonary nodule samples labeled by the original labeling method, the number of pulmonary nodule samples correctly labeled by the original labeling method, the number of pulmonary nodule samples correctly labeled by the intelligent assisted labeling method, and the total number of pulmonary nodule samples labeled by the intelligent assisted labeling method. represents the accuracy of intelligent labeling, Indicates the original annotation accuracy.

[0045] Through the accuracy improvement value Xv, the case intelligent decision module can automatically and effectively evaluate the intelligent assisted labeling method, which helps to improve the labeling accuracy of lung nodule samples. According to the automated processing process, an unlimited number of samples can be processed, thus overcoming the limitations of manual labeling in quality and quantity.

[0046] The case expansion unit numbers the original data, auxiliary data, and telemedicine system data according to the case expansion data characteristics. The original data is numbered as U 1 , U 2 , U 3 ,…U n , the auxiliary data volume number is Ur 1 、Ur 2 、Ur 3 、…Ur n The data volume of the telemedicine system is numbered as Ue 1 , Ue 2 , Ue 3 ,… n .

[0047] The calculation formula of the data volume expansion unit for the data acquisition expansion rate Yv is:

[0048]

[0049] In the formula, Yv represents the data acquisition expansion rate, U 1 , U 2 , U 3 ,…U n Indicates the amount of original data, Ur 1 、Ur 2 、Ur 3 、…Ur n Indicates the amount of auxiliary data, Ue 1 , Ue 2 , Ue 3 ,… n Represents the amount of data in the telemedicine system.

[0050] Calculating the data acquisition expansion rate Yv helps to evaluate the data expansion effect after introducing auxiliary data and telemedicine system data, thereby solving the limitations of data quality and quantity.

[0051] The model generalization capability unit numbers the number of samples correctly identified by the model under multiple different medical environments, the total number of samples under multiple different medical environments, the number of samples correctly identified by the model under a single medical environment, and the total number of samples under a single medical environment according to the model generalization capability data characteristics. The number of samples correctly identified by the model under multiple different medical environments, the total number of samples under multiple different medical environments, the number of samples correctly identified by the model under a single medical environment, and the total number of samples under a single medical environment are numbered K 1 , K 2 , K 3 , K 4 .

[0052] The calculation formula of the model generalization ability enhancement unit for the model generalization ability improvement index Rv is:

[0053]

[0054] In the formula, Rv represents the index of improvement in model generalization ability, K 1 , K 2 , K 3 , K 4 They represent the number of samples correctly identified by the model under multiple different medical environments, the total number of samples under multiple different medical environments, the number of samples correctly identified by the model under a single medical environment, and the total number of samples under a single medical environment. Indicates the accuracy of multi-environment adaptability, Indicates the accuracy of a single environment.

[0055] Calculating the model generalization ability improvement index Rv helps to evaluate the generalization ability of the auxiliary model in different medical environments and ensure the reliability and effectiveness of the auxiliary model in practical applications. By comparing the accuracy in different environments, the adaptability of the auxiliary model to environmental changes can be evaluated, thereby further guiding the optimization and improvement plan of the auxiliary model.

[0056] After implementing a new intelligent assisted labeling method, continue to monitor its performance and use the calculation formula of the accuracy improvement value Xv to regularly evaluate the accuracy improvement value to ensure continuous improvement.

[0057] Expand case data volume:

[0058] Integrate more data sources: Find and integrate more auxiliary data sources and telemedicine system data to increase the amount of available data; Improve data quality and diversity: Ensure that the introduced data is not only large in quantity but also high in quality and can cover different case types and medical environments; Use data enhancement technology: Apply data enhancement technology (data augmentation and generative adversarial networks) to artificially expand the amount of data while maintaining the diversity and representativeness of the data.

[0059] The case intelligent management module manages the parameters of the case analysis accuracy unit, case expansion unit, and model generalization ability unit according to the model generalization ability improvement index Rv. The management process is as follows:

[0060] Case Analysis Accuracy Unit: helps find complex model formats by increasing or decreasing the number of layers and neurons in the model to avoid overfitting or underfitting;

[0061] Case expansion unit: Integrating case data from different medical systems and data sources helps increase the amount of training data and thus improve the generalization ability of the model;

[0062] Model generalization ability unit: By changing the learning rate and number of iterations, the best training strategy can be found to improve the generalization ability of the model.

[0063] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the above embodiments do not limit the present invention in any form, and any technical solution obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present invention.

Claims

1. A case analysis auxiliary system based on lung cancer diagnosis and treatment, characterized in that: include: Case data preprocessing module, which is used to clean, standardize and enhance medical imaging data to remove noise, correct distortion and fill missing values, and convert raw medical images into structured information that can be used for further analysis; Case data feature extraction module, used to collect case analysis accuracy data, case expansion data and model generalization ability data, and number them; The case data verification module is connected to the case data feature extraction module through the network, and calculates the accuracy improvement value Xv, the data acquisition expansion rate Yv and the model generalization ability improvement index Rv respectively according to the case analysis accuracy data, case expansion data and model generalization ability data collected by the case data feature extraction module; The case intelligent decision-making module is connected to the case data verification module through the network, and improves the case accuracy and expands the case data volume according to the accuracy improvement value Xv and the data acquisition expansion rate Yv.

2. The case analysis auxiliary system based on lung cancer diagnosis and treatment according to claim 1 is characterized in that: The case data feature extraction module includes: The case analysis accuracy unit collects case analysis accuracy data through the hospital Internet client, numbers them, and connects with the case data verification module through the network; The case expansion unit collects case expansion data through the hospital Internet client, numbers them, and connects with the case data verification module through the network; The model generalization capability unit collects model generalization capability data through the hospital Internet client, numbers them, and connects them to the case data verification module through the network.

3. The case analysis auxiliary system based on lung cancer diagnosis and treatment according to claim 2 is characterized in that: The case analysis accuracy unit numbers the total number of lung nodule samples labeled by the original labeling method, the number of lung nodule samples correctly labeled by the original labeling method, the number of lung nodule samples correctly labeled by the intelligent assisted labeling method, and the total number of lung nodule samples labeled by the intelligent assisted labeling method according to the case analysis accuracy data characteristics. The total number of lung nodule samples labeled by the original labeling method, the number of lung nodule samples correctly labeled by the original labeling method, the number of lung nodule samples correctly labeled by the intelligent assisted labeling method, and the total number of lung nodule samples labeled by the intelligent assisted labeling method are numbered M1, M2, M3, and M4, respectively.

4. The case analysis auxiliary system based on lung cancer diagnosis and treatment according to claim 3 is characterized in that: The case data verification module includes: The data accuracy improvement unit calculates the accuracy improvement value Xv based on the case analysis accuracy data; A data volume expansion unit calculates the data acquisition expansion rate Yv according to the case expansion data; The model generalization capability enhancement unit calculates the model generalization capability improvement index Rv based on the model generalization capability data.

5. The case analysis auxiliary system based on lung cancer diagnosis and treatment according to claim 4 is characterized in that: The calculation formula of the data accuracy improvement unit for the accuracy improvement value Xv is: In the formula, M1, M2, M3, and M4 represent the total number of pulmonary nodule samples labeled by the original labeling method, the number of pulmonary nodule samples correctly labeled by the original labeling method, the number of pulmonary nodule samples correctly labeled by the intelligent assisted labeling method, and the total number of pulmonary nodule samples labeled by the intelligent assisted labeling method, respectively. represents the accuracy of intelligent labeling, Indicates the original annotation accuracy.

6. The case analysis auxiliary system based on lung cancer diagnosis and treatment according to claim 2 is characterized in that: The case expansion unit numbers the original data volume, the auxiliary data volume and the telemedicine system data volume according to the case expansion data characteristics, and the original data volume is numbered as U1, U2, U3, ..., U n , the auxiliary data quantities are numbered Ur1, Ur2, Ur3, ...Ur n , the data volume of the telemedicine system is numbered as Ue1, Ue2, Ue3, ...Ue n .

7. The case analysis auxiliary system based on lung cancer diagnosis and treatment according to claim 6 is characterized in that: The calculation formula of the data volume expansion unit for the data acquisition expansion rate Yv is: In the formula, Yv represents the data acquisition expansion rate, U1, U2, U3, ...U n Indicates the amount of original data, Ur1, Ur2, Ur3, ...Ur n Indicates the amount of auxiliary data, Ue1, Ue2, Ue3, ...Ue n Represents the amount of data in the telemedicine system.

8. The case analysis auxiliary system based on lung cancer diagnosis and treatment according to claim 2 is characterized in that: The model generalization capability unit numbers the number of samples correctly identified by the model under multiple different medical environments, the total number of samples under multiple different medical environments, the number of samples correctly identified by the model under a single medical environment, and the total number of samples under a single medical environment according to the model generalization capability data characteristics. The number of samples correctly identified by the model under multiple different medical environments, the total number of samples under multiple different medical environments, the number of samples correctly identified by the model under a single medical environment, and the total number of samples under a single medical environment are numbered K1, K2, K3, and K4.

9. The case analysis auxiliary system based on lung cancer diagnosis and treatment according to claim 8, characterized in that: The calculation formula of the model generalization ability enhancement unit for the model generalization ability improvement index Rv is: In the formula, Rv represents the index of improvement in the generalization ability of the model, K1, K2, K3, and K4 represent the number of samples correctly identified by the model under multiple different medical environments, the total number of samples under multiple different medical environments, the number of samples correctly identified by the model under a single medical environment, and the total number of samples under a single medical environment, respectively. Indicates the accuracy of multi-environment adaptability, Indicates the accuracy of a single environment.

10. The case analysis auxiliary system based on lung cancer diagnosis and treatment according to claim 1, characterized in that: Also includes: The case intelligent management module is connected to the case intelligent decision-making module through the network, and manages the parameters of the case analysis accuracy unit, case expansion unit and model generalization ability unit according to the model generalization ability improvement index Rv.

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