Testis pathological tissue cell type automatic identification and statistics system based on deep learning

Abstract

The invention provides a testis pathological tissue cell type automatic identification and statistics system based on deep learning, and aims to significantly improve the efficiency and accuracy of medical diagnosis through an advanced image processing and analysis technology. The system adopts a self-adaptive image preprocessing technology, and the image quality is optimized to adapt to the requirements of a deep learning model. And in combination with a deep semi-supervised learning labeling method, the labeling workload of a pathologist is effectively reduced, and meanwhile, the labeling accuracy is improved. The design of the multi-task deep learning model allows simultaneous identification of cell types and proportion statistics, and the generalization ability of the model is enhanced. In addition, due to the application of the model explanatory visualization technology, the transparency of the model and the credibility of doctors on diagnosis results are improved. And an integrated learning optimization strategy further ensures the accuracy of an identification result. Through a series of innovative technologies, the system provides an efficient and accurate solution for the automatic diagnosis of the pathological tissues of the testis.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical image processing and analysis, and in particular to an automation system combining deep learning technology. Background Art

[0002] In the field of medical diagnosis, testicular pathological tissue analysis is a crucial task, which plays a decisive role in determining the type of spermatogenesis disorder and formulating treatment plans. However, the traditional testicular pathological tissue analysis method mainly relies on the subjective judgment and experience of pathologists to observe the seminiferous tubules one by one under a microscope. This method is not only time-consuming and labor-intensive, but also due to individual subjective differences, the diagnostic results may be inconsistent and subjective, which to a certain extent limits the accuracy and reliability of testicular tissue pathology diagnosis.

[0003] With the rapid development of computer science and artificial intelligence technology, especially the breakthrough progress of deep learning technology in the field of image recognition, new ideas and tools have been provided for the automated analysis of medical images. Deep learning technology can automatically identify patterns and features in images through learning from a large amount of data, thereby achieving accurate classification and recognition of image content. This provides technical support for improving the efficiency and accuracy of medical image analysis, especially in the identification of cell types in pathological tissue images, and has great application potential.

[0004] Existing medical image analysis systems based on deep learning still face some challenges in practical applications. For example, the complexity and diversity of pathological tissue images require the algorithm to have a high generalization ability; the acceptance and trust of pathologists and clinicians in automatic diagnosis results need to be further improved; in addition, how to effectively combine expert knowledge with machine learning models to optimize the annotation process and improve model performance is also a hot topic in current research. Therefore, developing a system that can automatically and accurately identify and count testicular pathological tissue cell types is of great practical significance for improving the efficiency and accuracy of medical diagnosis. Summary of the invention

[0005] The content of the present invention is to propose a deep learning-based testicular pathological tissue cell type automatic identification and statistical system, which realizes efficient and accurate analysis of testicular pathological tissue images through a series of innovative technologies and processes. The following is the details of the present invention:

[0006] One of the cores of the present invention is the adaptive image preprocessing module, which is the primary link of the entire automatic recognition system. It first automatically evaluates the quality of the input testicular pathological tissue image, and then performs a series of precise image optimization operations in sequence according to the evaluation results. Specifically, the module first performs image denoising to eliminate random noise in the image and improve the clarity of the image; then performs contrast enhancement to highlight the cell details in the image; followed by size standardization to ensure that all images have a uniform size for deep learning model processing; the color correction step ensures the consistency of image color to reflect the true color of the tissue; finally, the module also performs rotation correction and flip correction to ensure that the direction and position of the image are correct. This series of orderly preprocessing steps not only improves the overall quality of the image, but also provides more accurate and consistent input data for the subsequent deep learning model, thereby significantly enhancing the model's ability to recognize and analyze image features.

[0007] The deep semi-supervised learning annotation module of the present invention is a key innovation to solve the scarcity of expert annotation data. The module first uses the generator network in the generative adversarial network (GAN) to generate realistic pseudo-annotated data by setting generation parameters such as learning rate and number of iterations. These data are visually similar to the real annotated data, but do not require manual annotation by experts. Then, the generated pseudo-annotated data is mixed with a small amount of real annotated data and input into the discriminator network for quality assessment. The discriminator network screens out high-quality pseudo-annotated data through set evaluation criteria such as loss function and accuracy threshold. Then, these screened pseudo-annotated data are used together with the real annotated data to train the deep learning model, and the model parameters are continuously optimized through self-learning annotation algorithms such as gradient descent and back propagation to improve the model's prediction ability for unlabeled data. Finally, the prediction results of the model are reviewed and revised by experts to form new training data, further enhancing the generalization ability of the model and the accuracy of annotation. This process runs clearly, effectively combines the advantages of expert knowledge and machine learning, significantly reduces the annotation workload of experts, and ensures the high quality and high accuracy of the annotated data.

[0008] The multi-task deep learning model of the present invention is the innovative core in the technical solution. It is built based on convolutional neural network (CNN) and has the dual ability to process cell type classification and cell proportion statistics at the same time. The model first uses a shared network layer, such as the first few convolutional layers and pooling layers, using ReLU activation function and maximum pooling strategy to extract the common features of the image. The parameters of these layers include the convolution kernel size is set to 3x3, the step size is 1, and the pooling window size is set to 2x2. Subsequently, the model adopts a branch structure, in which the main branch further uses a series of convolutional layers and fully connected layers to perform the classification task of the cell type using the Softmax activation function. The parameters of these layers include the number of convolution kernels increasing and the number of neurons in the fully connected layer. The other branch focuses on the statistics of cell proportions through specific convolutional layers and 1x1 convolution operations, in which 1x1 convolution is used to adjust the depth of the feature map to meet the needs of proportion statistics. The network layer of each task is optimized by cross entropy loss function and mean square error loss function, respectively for classification accuracy and the accuracy of proportion estimation. Finally, the model is trained using the back-propagation algorithm and the Adam optimizer, with the learning rate initially set to 0.001 and adjusted based on the performance on the validation set to achieve collaborative optimization of the two tasks. The entire process is clear, ensuring that the model can effectively balance the needs of different tasks when performing multi-task learning, improving the model's generalization and prediction performance.

[0009] The model explanatory visualization module introduced in the present invention is to enhance the transparency and credibility of the model. It intuitively shows the user the feature areas that the model focuses on when making decisions through a series of orderly steps. First, the module uses the attention mechanism, which is a parameterized neural network technology. By setting the attention weight parameters, the model can identify and highlight the feature areas in the image that are most critical for classification and scale estimation. Then, the feature map visualization technology is applied to convert these attention-weighted feature maps into visual heat maps, where the depth of the color represents the importance of the feature. Then, these heat maps are superimposed or displayed side by side with the original image, so that the user can clearly see the image area based on which the model prediction is based. In addition, the module also includes a user interaction interface that allows the user to explore the impact of different feature areas on model decisions by adjusting parameters, further enhancing the interpretability of the model. Finally, the visualization report generated by the module not only helps doctors and researchers understand the internal working mechanism of the model, but also provides strong visual support for further optimization and verification of the model. The whole process runs clearly, ensuring the effectiveness and practicality of the model explanatory visualization module.

[0010] The integrated learning optimization strategy module of the present invention is a key component for improving the overall performance of the system. The module first uses a model selection algorithm to intelligently select the best performing models based on the performance of each model on the validation set, such as performance indicators such as accuracy, recall rate and F1 score. After the selected model set, the module uses a weighted fusion algorithm to assign weights to each model, and these weights are determined through a parameter optimization process, for example, using cross-validation and grid search to determine the optimal weight parameters for each model. The weights are assigned based on the performance and diversity of the model to ensure that the final prediction results are both accurate and stable. Then, the module integrates these weighted prediction results, and may use a voting mechanism, an averaging method or a more complex statistical method, such as Bayesian model averaging, to generate the final recognition result. In addition, the integrated learning module also includes a feedback loop that allows the system to continuously adjust the model selection and weight assignment based on feedback in actual applications to adapt to new data and improve long-term performance. The entire process runs clearly and orderly, ensuring that the integrated learning optimization strategy module can effectively improve the recognition accuracy of the model

[0011] The data management module of the present invention is a key system component to ensure efficient operation and precise control of data flow. The module first stores image data and metadata through a carefully designed database architecture, which contains parameters such as image resolution, acquisition date and patient information to ensure the organization and consistency of data. Subsequently, the module uses an efficient indexing mechanism, such as a hash table based on image features and tags, to achieve fast data retrieval, significantly reducing query response time. In addition, the data management module is also responsible for processing data input and output operations, using parameters such as batch size (batchsize) of 64 and learning rate (learning rate) of 0.01 to optimize the throughput and processing speed of data flow. In order to improve the traceability of data, the module implements a detailed logging system to record the details of each data access and change, including timestamp (timestamp) and operator identity (operator ID). Finally, the data management module also includes a data backup and recovery mechanism, which ensures data security and prevents data loss or damage through regular backup frequencies such as once a week and multi-location storage strategies. The process operation of the entire module is clear and orderly, ensuring the integrity and reliability of data throughout the life cycle.

[0012] The user interaction interface module in the present invention is a key design to enhance user experience and system interactivity. The module first provides an intuitive graphical user interface (GUI), which contains a clear data input interface, and the user can enter image data by simple drag and drop operation or specified path. Then, a real-time preview function is displayed on the interface, allowing the user to view image thumbnails immediately after uploading, and by setting zoom parameters such as zoom level 1.5 times, the image quality is carefully checked. Subsequently, the recognition results are clearly presented through the result display area, and the user can view the classification results and proportional statistics of the cell type. The result display area supports parameters such as font size 14pt and color contrast adjustment to ensure the readability of the results. In addition, the system parameter adjustment interface provides a slider and an input box, allowing the user to adjust model parameters such as learning rate and regularization coefficient as needed, and the parameter adjustment range is clearly marked, such as the learning rate slider range 0.001 to 0.1. In order to enhance interactivity, the interface also includes a help system, which provides operation guidance and parameter descriptions for users by setting the pop-up time parameter of the help prompt to 3 seconds. Finally, the user interface module also includes a feedback collection mechanism, where users can submit their usage experience and improvement suggestions through a feedback form. The feedback form parameters include satisfaction ratings and open text input.

[0013] The system integration of the present invention is an efficient and orderly workflow that tightly integrates all modules to achieve automated medical image analysis. The workflow first starts the adaptive image preprocessing module, receives the testicular pathological tissue image to be analyzed, and performs a series of image quality optimization operations, such as automatic denoising, contrast enhancement and size standardization, to ensure that the image data meets the standards required by the deep learning model. Subsequently, the deep semi-supervised learning annotation module intervenes, generates pseudo-annotated data using the generative adversarial network (GAN) technology, and provides high-quality annotation data for model training through self-learning algorithms combined with expert review. Next, the multi-task deep learning model begins to execute, extracts features through a shared network layer, and classifies and counts cell types through specific branches, and optimizes using cross entropy loss functions and mean square error loss functions to ensure the accuracy of recognition. The model explanatory visualization module follows closely, using attention mechanisms and feature map visualization technology to generate a visual report of the model decision process, enhancing the transparency and credibility of the model. The integrated learning optimization strategy module intelligently fuses the prediction results of multiple models, and improves the accuracy and robustness of the recognition results through methods such as weighted voting or Bayesian model averaging. The data management module plays a key role in the whole process, responsible for the storage, retrieval and backup of image data and recognition results, ensuring the consistency and traceability of data. Finally, the user interface module provides an intuitive operating platform for users to input data, monitor the analysis process, view results and adjust system parameters, ensuring the user-friendliness and high interactivity of the system. The integrated process of the entire system runs clearly and orderly, realizing the automated processing from image input to result output, significantly improving the efficiency and accuracy of medical image analysis.

[0014] The system optimization process of the present invention is a mechanism of continuous iteration and self-improvement, which ensures the continuous improvement of system performance and adaptability to emerging medical image data. First, the system regularly obtains the user's operating experience and improvement suggestions through a user feedback collection module, and sets the feedback collection cycle to once a quarter to ensure timely capture of user needs. Subsequently, the system analysis module conducts an in-depth analysis of the collected user feedback, identifies common problems and potential areas for improvement, and the analysis cycle is set to once a month to quickly respond to user feedback. Then, the system optimization module adjusts the model parameters and algorithms according to the analysis results, such as adjusting the learning rate of the deep learning model (parameter adjustment range 0.0001 to 0.01), increasing the number of network layers or changing the network structure, and optimizing the loss function weight to ensure that the model better adapts to new data features. In addition, the system update module is responsible for regularly updating the system software, introducing new functions and improvement measures, and the update cycle is set to once every six months to integrate the latest technological advances and user feedback. Finally, the system optimization also includes a performance monitoring module to monitor the system operation status and the accuracy of the recognition results in real time, set performance monitoring indicators such as accuracy, recall rate and processing time, and ensure that the system is always running in the best state. The entire system optimization process operates in a clear and orderly manner. Through continuous evaluation, adjustment and updating, it ensures that the system always remains efficient, accurate and user-friendly when processing medical image data.

[0015] The system deployment process of the present invention is a key step to ensure that the technical solution can achieve maximum performance in practical applications in the medical field. The deployment begins with a detailed hardware and software configuration phase. In terms of software, the system needs to be adapted to mainstream operating systems, such as Windows 10 and Linux kernel 4.x, to ensure compatibility and stability; in terms of hardware, it is recommended to use a GPU with at least 4GB of video memory and a server configuration of 16GB of RAM to support the efficient operation of deep learning models. Subsequently, the system installation phase follows standardized procedures, and the installation process is simplified through automated scripts to reduce human errors. After the installation is completed, a rigorous system testing phase is entered, including unit testing, integration testing, and performance testing. The test parameters include, but are not limited to, response time, number of concurrent users, and system resource utilization to verify whether the system functions and performance meet the expected standards. Finally, the system maintenance phase implements a regular inspection and upgrade plan to ensure the long-term stable operation of the system while quickly responding to any potential technical problems or user feedback. Every link in the entire deployment process is designed to provide medical professionals with a reliable, efficient, and easy-to-use tool to support daily medical image analysis work.

[0016] The method of using the system described in the present invention is a highly systematic and precisely controlled multi-step process, specifically designed to achieve in-depth analysis of testicular pathological tissue images. The following are the detailed steps of the method:

[0017] Image input and quality assessment: The method first receives the testicular pathological tissue image to be analyzed and performs a detailed quality assessment. The assessment criteria include image resolution not less than 4K, signal-to-noise ratio higher than 30dB, and color deviation not exceeding ΔE*ab 5 in the CIELAB color space.

[0018] Adaptive image preprocessing: In the adaptive image preprocessing step, the system automatically performs a series of image optimization operations. Specifically, it includes: Denoising: Apply an adaptive median filter, and the filter kernel size is dynamically adjusted according to the image noise level, generally between 3x3 and 5x5 pixels. Contrast enhancement: Use the CLAHE algorithm, and the block size parameter is set to 8 to enhance the local contrast of the image. Size standardization: Adjust all images to a uniform size, such as 1024x1024 pixels, to meet the input requirements of subsequent models. Color correction: Apply a color correction matrix and automatically adjust according to the white balance of the image to ensure the authenticity of the color. Rotation correction: Automatically detect the image rotation angle (-180° to 180°) and correct it to the standard direction. Flip correction: Automatically detect and correct the left-right flip of the image based on the symmetry characteristics of the tissue structure.

[0019] Deep semi-supervised learning annotation: Using the deep semi-supervised learning annotation module, the system self-learns to annotate unlabeled data. The parameters of the generator network include a learning rate of 0.0002 and an iteration of 5000. The discriminator network uses a binary cross entropy loss function for quality assessment.

[0020] Multi-task deep learning model analysis: In the multi-task model analysis step, the system uses a shared network layer to extract image features and performs cell type classification and proportion statistics through a specific network layer. The loss function weights of the classifier and regressor are set to 0.5 respectively to balance the learning efficiency of the two tasks.

[0021] Model interpretable visualization: The model interpretable visualization module uses the attention mechanism to set the weight parameter α=0.5 to highlight the feature areas that the model focuses on during the decision-making process, and generates an intuitive decision-making process report through heat map visualization technology.

[0022] Ensemble learning optimization: The ensemble learning optimization strategy module adopts a weighted fusion algorithm to assign weights to each model with a parameter range of 0.1 to 0.9. It uses the Bayesian model averaging method to improve the accuracy and robustness of the final recognition results.

[0023] Data management: The data management module stores and manages image data, annotation data, and recognition results through a database system. A B+ tree index structure is used with an index depth of 3 to ensure data retrieval efficiency. At the same time, a periodic data backup strategy is implemented with a backup frequency of once a week and a backup retention period of one year.

[0024] User interaction and result display: The user interaction interface module provides high-resolution display and supports multi-touch and gesture operation, making it easy for users to view recognition results and adjust system parameters. The result display area supports zooming in and out, with a magnification ratio of up to 2 times, ensuring that users can carefully examine every detail.

[0025] The technical solution of the present invention can bring the following three key technical effects:

[0026] 1. Significantly improve diagnostic accuracy: The multi-task deep learning model of the present invention can accurately identify different cell types in testicular pathological tissue and accurately count their proportions. Through the integrated learning optimization strategy, the system can integrate the prediction results of multiple models and significantly improve the accuracy of recognition. In addition, the application of the model explanatory visualization module enables clinicians and pathologists to intuitively understand the decision-making process of the model, thereby improving the trust and acceptance of the automatic diagnosis results.

[0027] 2. Significantly improve diagnostic efficiency: The adaptive image preprocessing module can automatically adjust image parameters, provide high-quality input data for the deep learning model, and reduce the time for image preparation. The deep semi-supervised learning annotation module generates pseudo-annotated data through the generative adversarial network (GAN) technology, reducing the reliance on expert annotation and the workload of data preparation. Overall, the application of these technologies makes the analysis process of pathological tissue faster and more efficient.

[0028] 3. Optimize the allocation of medical resources: Since the system can automatically complete most of the image analysis work, pathologists can focus more time and energy on complex cases that require professional judgment. This optimized allocation of resources not only improves the operating efficiency of the medical system, but also enables medical experts to provide patients with higher quality diagnostic services. At the same time, the easy-to-use design of the user interface module allows non-professional users to use the system easily, further expanding the application scope and influence of the technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 The system module block diagram of the present invention is

[0030] Figure 2 The process flow diagram of the system method of the present invention is as follows: DETAILED DESCRIPTION

[0031] The following will be combined with the attached embodiment of the present invention Figure 1 and Figure 2, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] The adaptive image preprocessing module optimizes the input testicular pathological tissue images through a series of orderly automated steps. The module first uses an adaptive threshold algorithm for denoising and sets parameters to identify and eliminate Gaussian noise in the image. Then, through dynamic contrast adjustment technology, contrast enhancement parameters are set to improve the clarity of cell edges in the image. Next, size standardization is performed to adjust all images to a uniform resolution, such as 1024×1024 pixels, to meet the input requirements of the deep learning model. The color correction step adjusts the color balance parameters through color space conversion, such as from RGB to HSV, to ensure the authenticity and consistency of the image color. After that, rotation correction is performed to rotate the image to the correct direction through image feature point detection and geometric transformation. The parameters include rotation angle and center point coordinates. Finally, flip correction is performed to automatically determine and correct the flip error of the image based on the symmetry characteristics of the image. This series of parameterized preprocessing operations not only ensures the quality and consistency of the image data, but also lays a solid foundation for the accurate feature recognition of the deep learning model.

[0033] The deep semi-supervised learning annotation module effectively addresses the challenge of scarce expert-annotated data through a series of fine-tuned parameterized processes. The module first configures the generator of the generative adversarial network (GAN) with a learning rate of 0.0002 and an iteration count of 5000 to generate high-quality and realistic pseudo-annotated data. The generated data is then combined with the real annotated data and input into the discriminator network, which is evaluated by setting the loss function to binary cross entropy and the accuracy threshold to 95% to ensure that the pseudo-annotated data that meets the standards is screened out. Subsequently, these data are mixed with the real annotated data and used to train the deep learning model, where the self-learning annotation algorithm uses the gradient descent method, the learning rate is initially set to 0.01, and is dynamically adjusted as the training progresses, and the error target of the back-propagation algorithm is set to minimize the mean square error. Through the review and correction of the model prediction results by experts, the correction threshold is usually set to 90%, and the model parameters are further optimized to form a more accurate training data set. The operation of the entire process ensures high efficiency and accuracy of annotation, while greatly reducing the dependence on expert annotation.

[0034] The multi-task deep learning model is based on a convolutional neural network (CNN) architecture and is carefully designed to achieve the dual tasks of cell type classification and cell proportion statistics. The initial shared network layers of the model, including the preceding convolutional and pooling layers, use 3x3 convolution kernels with a stride of 1, a ReLU activation function to introduce nonlinearity, and a 2x2 maximum pooling window to reduce the feature dimension. After these shared layers, the model is divided into two task-specific branches: the classification branch further passes through deeper convolutional layers and fully connected layers, where the number of convolution kernels in the convolutional layer increases exponentially with depth, and the fully connected layer adjusts the number of neurons according to task requirements, and finally outputs the classification probability through the Softmax activation function; while the proportion statistics branch uses 1x1 convolution to adjust the depth of the feature map, uses the mean squared error loss function to optimize the network parameters, and focuses on learning accurate estimates of cell proportions. The tasks of the two branches are jointly optimized through backpropagation of their respective loss functions, and the parameters are updated using the Adam optimizer. The initial learning rate is set to 0.001 and dynamically adjusted based on the performance on the validation set. This design not only ensures the high efficiency of the model in feature extraction, but also achieves accurate learning of classification and proportional statistics tasks through task-specific network layer optimization, significantly improving the generalization ability and prediction accuracy of the model in medical image analysis.

[0035] The model interpretability visualization module significantly improves the transparency and credibility of deep learning models through sophisticated parameter settings and orderly processes. The module first uses an attention mechanism to set weight parameters such as α = 0.5 to balance the importance of different feature regions, and normalizes these weights through the Softmax function to ensure that their sum is 1. Subsequently, the feature map is converted into a heat map through a specific visualization technique, where the color mapping parameters are set from blue (low importance) to red (high importance) to intuitively represent the feature strength. These heat maps are then superimposed or displayed side by side with the original image, allowing users to visually identify the key areas in the model decision process. In addition, the user interface in the module allows users to adjust the transparency parameter through a slider, such as from 0 (completely transparent) to 1 (completely opaque), to explore the impact of different feature regions on the model output. Finally, the visualization report generated by the module ensures clear communication and readability of information by setting report format parameters such as resolution and layout. The operation of the entire process ensures the efficiency and accuracy of model interpretability visualization, providing doctors and researchers with a powerful tool to deeply understand the model decision process.

[0036] The ensemble learning optimization strategy module significantly improves the accuracy and robustness of model predictions through a series of precise parameter settings and orderly processes. The module first uses a model selection algorithm to set threshold parameters such as an accuracy threshold of 0.95 based on performance indicators such as accuracy, recall, and F1 score of each model on the validation set, and intelligently selects the best performing model. Then, through a weighted fusion algorithm, weights are assigned to each selected model. The weight parameters are determined through cross-validation and grid search. For example, the weights may be adjusted between 0.1 and 0.5 to find the optimal weighted combination. Then, the module uses methods such as weighted voting or Bayesian model averaging to integrate the prediction results of each model according to the set weighted parameters to generate the final recognition result. In addition, the ensemble learning module also includes a feedback loop in which the system dynamically adjusts model selection and weight assignment based on feedback from actual applications, such as prediction error rate, for example, adjusting the step size to 0.01 and the learning rate to 0.01 to adapt to new data changes. The entire process operates efficiently, ensuring that the ensemble learning optimization strategy module can continuously optimize model performance and achieve highly reliable recognition results.

[0037] The data management module ensures efficient storage, fast retrieval, and accurate processing of image data and its annotation and recognition results through a series of parameterized strategies and orderly processes. The module uses a relational database management system and sets the maximum number of connections to 100 to support multi-user concurrent access. At the same time, a unique identifier is assigned to each data entry to ensure the uniqueness and consistency of the data. In terms of data retrieval, the module implements a B-tree-based index structure and sets the index depth to 3 to optimize query efficiency, reducing the average retrieval time to milliseconds. During data input and output, the module sets the batch size to 64 to balance memory usage and processing speed, and ensures the stability of data transmission by setting the timeout parameter to 30 seconds. Data traceability is achieved through a detailed log system, in which the log interval is set to 1 minute, and the recorded content includes operation type, timestamp, and operator ID. In addition, data security is achieved through a periodic backup strategy, with a backup frequency set at once a week, and a multi-location storage strategy with a backup retention period set at one year to prevent data loss or damage. The design and operation of the entire data management module ensures the integrity, consistency and reliability of data throughout the entire processing process.

[0038] The user interface module greatly improves the ease of use and user interaction experience of the system through carefully designed parameter configuration and intuitive operation process. The module is equipped with a concise and clear graphical user interface (GUI), which enables users to input image data by simply dragging and dropping or clicking the upload button. At the same time, the real-time preview function on the interface supports parameter settings, such as setting the zoom level to 1.5 times, so that users can carefully check every detail of the image. After the recognition is completed, the result display area is displayed in a 14pt font size and high-contrast color to ensure that the classification results and cell ratio statistics are clear and readable. The system parameter adjustment interface provides precise slider control, such as the adjustment range of the learning rate from 0.001 to 0.1, and the adjustment accuracy of the regularization coefficient to two decimal places, allowing users to fine-tune the model performance according to experimental needs. In addition, the built-in help system of the interface provides users with instant operation guidance and parameter descriptions by setting an automatic pop-up time of 3 seconds. Users can also submit their experience and improvement suggestions through the feedback form, which contains satisfaction rating options from 1 to 5 and unlimited open text input boxes to collect user feedback. The design of the entire user interaction interface module ensures that users can have a smooth and intuitive operating experience in every aspect such as data input, result viewing and parameter adjustment.

[0039] The system integrates a series of precise parameter settings and orderly processes to achieve an efficient and automated medical image analysis workflow. First, the system receives the testicular pathological tissue image to be analyzed, and the adaptive image preprocessing module is immediately started to automatically perform denoising (parameter threshold is set to 3), contrast enhancement (parameter contrast gain is set to 1.2), and size normalization (parameter normalization size is set to 1024x1024 pixels). Then, the deep semi-supervised learning annotation module uses the generative adversarial network (GAN) technology to generate pseudo-annotated data (parameter generator learning rate is set to 0.0002, and the number of iterations is set to 5000), and selects high-quality annotated data through self-learning algorithm combined with expert review.

[0040] The multi-task deep learning model then performs cell type recognition and proportional statistics, where the classification task uses the Softmax activation function and the proportional statistics task uses linear regression (the parameter classification loss function weight is set to 0.5 and the proportional statistics loss function weight is set to 0.5). The model explanatory visualization module generates a visual report of the decision process, where the weight parameter α of the attention mechanism is set to 0.5 to balance the importance of the feature area.

[0041] The integrated learning optimization strategy module uses a weighted fusion algorithm to assign weights to each model (the parameter weight range is set to 0.1 to 0.9), and uses the Bayesian model averaging method to improve the accuracy and robustness of the recognition results. The data management module is responsible for data storage and management, setting the backup frequency to once a week and the retention period to one year to ensure data security. Finally, the user interaction interface module provides an interface for user operations, and the parameters include setting the user interface update frequency to 60 frames per second to ensure a smooth user experience. The integrated process of the entire system runs clearly and orderly, and automated processing is achieved from image input to result output, which significantly improves the efficiency and accuracy of medical image analysis.

[0042] The system optimization process of the present invention ensures continuous improvement and performance improvement of the system by setting specific parameters and orderly steps. System optimization begins with the user feedback collection module, which is automatically triggered once a quarter to collect user opinions through satisfaction surveys and open feedback forms. The feedback analysis module then performs quantitative and qualitative analysis on the collected data every month to identify key issues and improvement points. Based on the analysis results, the system optimization module adjusts the parameters of the deep learning model, for example, fine-tuning the learning rate within the range of 0.0001 to 0.01 to optimize the model training effect. The system update module releases software updates every six months, integrating new features and improvements from user feedback. During the update process, the system compatibility test covers 99% of the existing device configurations. The performance monitoring module tracks the key performance indicators of the system in real time, such as accuracy (target value 98%), recall rate (target value 95%) and processing time (target value less than 5 seconds), to ensure that the system reaches or exceeds these preset performance thresholds in actual operation. When the performance indicator is lower than the target value, the system automatically triggers an alarm and starts a rapid response mechanism to make necessary adjustments or optimizations. The orderly operation of the entire system optimization process ensures that the system can adapt to new medical image data and continue to provide high-quality analysis results.

[0043] The system deployment process is a key step to ensure that the technical solution can achieve maximum effectiveness in practical applications in the medical field. The deployment begins with a detailed hardware and software configuration phase. In terms of software, the system needs to be adapted to mainstream operating systems, such as Windows 10 and Linux kernel 4.x, to ensure compatibility and stability; in terms of hardware, it is recommended to use a GPU with at least 4GB of video memory and a server configuration of 16GB of RAM to support the efficient operation of deep learning models. Subsequently, the system installation phase follows standardized procedures, and the installation process is simplified through automated scripts to reduce human errors. After the installation is completed, a rigorous system testing phase is entered, including unit testing, integration testing, and performance testing. The test parameters include but are not limited to response time, number of concurrent users, and system resource utilization to verify whether the system functions and performance meet the expected standards. Finally, the system maintenance phase implements a regular inspection and upgrade plan to ensure the long-term stable operation of the system, while quickly responding to any potential technical problems or user feedback. Every link in the entire deployment process is designed to provide medical professionals with a reliable, efficient, and easy-to-use tool to support daily medical image analysis work.

[0044] The method used is a highly systematic and precisely controlled multi-step process specifically designed to achieve in-depth analysis of testicular pathological tissue images. The following are the detailed steps of the method:

[0045] Image input and quality assessment: The method first receives the testicular pathological tissue image to be analyzed and performs a detailed quality assessment. The assessment criteria include image resolution not less than 4K, signal-to-noise ratio higher than 30dB, and color deviation not exceeding ΔE*ab 5 in the CIELAB color space.

[0046] Adaptive image preprocessing: In the adaptive image preprocessing step, the system automatically performs a series of image optimization operations. Specifically, it includes: Denoising: Apply an adaptive median filter, and the filter kernel size is dynamically adjusted according to the image noise level, generally between 3x3 and 5x5 pixels. Contrast enhancement: Use the CLAHE algorithm, and the block size parameter is set to 8 to enhance the local contrast of the image. Size standardization: Adjust all images to a uniform size, such as 1024x1024 pixels, to meet the input requirements of subsequent models. Color correction: Apply a color correction matrix and automatically adjust according to the white balance of the image to ensure the authenticity of the color. Rotation correction: Automatically detect the image rotation angle (-180° to 180°) and correct it to the standard direction. Flip correction: Automatically detect and correct the left-right flip of the image based on the symmetry characteristics of the tissue structure.

[0047] Deep semi-supervised learning annotation: Using the deep semi-supervised learning annotation module, the system self-learns to annotate unlabeled data. The parameters of the generator network include a learning rate of 0.0002 and an iteration of 5000. The discriminator network uses a binary cross entropy loss function for quality assessment.

[0048] Multi-task deep learning model analysis: In the multi-task model analysis step, the system uses a shared network layer to extract image features and performs cell type classification and proportion statistics through a specific network layer. The loss function weights of the classifier and regressor are set to 0.5 respectively to balance the learning efficiency of the two tasks.

[0049] Model interpretable visualization: The model interpretable visualization module uses the attention mechanism to set the weight parameter α=0.5 to highlight the feature areas that the model focuses on during the decision-making process, and generates an intuitive decision-making process report through heat map visualization technology.

[0050] Ensemble learning optimization: The ensemble learning optimization strategy module adopts a weighted fusion algorithm to assign weights to each model with a parameter range of 0.1 to 0.9. It uses the Bayesian model averaging method to improve the accuracy and robustness of the final recognition results.

[0051] Data management: The data management module stores and manages image data, annotation data, and recognition results through a database system. A B+ tree index structure is used with an index depth of 3 to ensure data retrieval efficiency. At the same time, a periodic data backup strategy is implemented with a backup frequency of once a week and a backup retention period of one year.

[0052] User interaction and result display: The user interaction interface module provides high-resolution display and supports multi-touch and gesture operation, making it easy for users to view recognition results and adjust system parameters. The result display area supports zooming in and out, with a magnification ratio of up to 2 times, ensuring that users can carefully examine every detail.

[0053] 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 present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A deep learning-based automatic identification and statistical system for testicular pathological tissue cell types, characterized in that: The system includes the following modules: an adaptive image preprocessing module, a deep semi-supervised learning annotation module, a multi-task deep learning model, a model explanatory visualization module, an integrated learning optimization strategy module, a data management module, and a user interaction interface module.

2. The system according to claim 1, characterized in that The adaptive image preprocessing module also includes algorithms for image rotation correction and flip correction.

3. The system according to claim 1, characterized in that The deep semi-supervised learning annotation module also includes algorithms for evaluating the quality of pseudo-annotated data and adjusting the generation strategy.

4. The system according to claim 1, characterized in that The multi-task deep learning model also includes a classifier for cell type identification and a regressor for proportional statistics.

5. The system according to claim 1, characterized in that The model explanatory visualization module also includes a function for generating a visualization report of the model decision process.

6. The system according to claim 1, characterized in that The integrated learning optimization strategy module also includes an algorithm for evaluating the performance of each model and adjusting the fusion weight.

7. A method of using the system according to claim 1, characterized in that: The method comprises the following steps: Input the testicular pathological tissue image to be analyzed and perform image quality assessment; Execute the adaptive image preprocessing module to denoise, enhance contrast, normalize size, correct color, rotate, and flip images; Use the deep semi-supervised learning annotation module to self-teach annotation of unlabeled data, generate pseudo-annotated data, and evaluate its quality; A multi-task deep learning model is used to perform feature extraction, cell type classification, and proportion statistics, and a classifier and regressor are used to obtain preliminary recognition results; Apply the model explanatory visualization module to display the model's decision-making process and generate a visualization report; Through the integrated learning optimization strategy module, the prediction results of multiple models are intelligently selected and weighted fused to obtain the final recognition result; Store and manage image data, annotation data and recognition results in the data management module; The recognition results are displayed through the user interaction interface module, allowing users to verify the results and adjust system parameters.

8. The method according to claim 7, characterized in that It also includes post-processing of the recognition results to further improve the recognition accuracy.

9. A computer-readable storage medium storing a computer program of the method according to claim 7, for instructing a computer to execute the method.

10. A computer program product comprising the computer-readable storage medium according to claim 9 and a computer program, for implementing the method according to claim 7.

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