Tumor cell intelligent analysis and diagnosis system and method based on deep learning

Abstract

The invention discloses an intelligent tumor cell analysis and diagnosis system and method based on deep learning, and relates to the technical field of tumor cell diagnosis, the system comprises the following components: a data acquisition module, a data preprocessing module, a quantum inspired deep learning model module and a diagnosis result output module; according to the method, model parameters are optimized by introducing a quantum inspired deep learning model and utilizing superposition state and entangled state principles in quantum calculation, the parameter search range is greatly widened, and compared with a traditional method, the method can more effectively avoid falling into a local optimal solution, so that the optimization efficiency and performance of the model are improved, and the method is suitable for large-scale popularization and application. By means of the innovative technology, analysis and diagnosis of tumor cells are more accurate, meanwhile, through the automatic and intelligent processing flow, the diagnosis efficiency is remarkably improved, and doctors can make diagnosis decisions more quickly.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tumor cell diagnosis, in particular to a tumor cell intelligent analysis and diagnosis system and method based on deep learning. BACKGROUND

[0002] With the continuous progress of medical technology, accurate diagnosis of tumor cells is crucial for developing effective treatment plans and improving patient outcomes. Traditional tumor cell diagnosis methods mainly rely on pathologists' experience and morphological observation under a microscope. This method is not only time-consuming and labor-intensive, but also subjective and easily influenced by individual experience and judgment of doctors.

[0003] The traditional technology has shortcomings. Patent No. CN119832000A6 discloses a cerebrospinal fluid cell intelligent analysis and diagnosis system and method based on deep learning. On the one hand, traditional deep learning models can only explore local optimal solutions when optimizing parameters, and cannot fully cover the parameter space, resulting in insufficient extraction of complex tumor cell characteristics and limited diagnostic accuracy. On the other hand, traditional diagnosis methods rely on manual observation and judgment, which is time-consuming and labor-intensive, and is easily affected by doctor fatigue and subjective factors, resulting in low diagnostic efficiency and inability to meet the needs of rapid clinical diagnosis.

[0004] To address the problems of limited accuracy, insufficient model generalization ability, and low diagnostic efficiency of traditional tumor cell diagnosis technology, the present application proposes a tumor cell intelligent analysis and diagnosis system and method based on deep learning. SUMMARY

[0005] The present application aims to overcome the shortcomings of the prior art and provides a tumor cell intelligent analysis and diagnosis system and method based on deep learning. It can introduce a quantum-inspired deep learning model, use the superposition state and entangled state principles in quantum computing to optimize model parameters, greatly expand the parameter search range, effectively avoid the problem of falling into local optimal solutions, and improve the optimization efficiency and performance of the model.

[0006] To solve the above technical problems, the present application provides the following technical solutions: on the one hand, a tumor cell intelligent analysis and diagnosis system based on deep learning, which includes the following components: data acquisition module, data preprocessing module, quantum-inspired deep learning model module, and diagnosis result output module.

[0007] The data acquisition module is used to collect tumor cell image data, related pathological data, and various types of raw data.

[0008] The data preprocessing module is used to perform cleaning, normalization, and enhancement preprocessing operations on the collected raw data.

[0009] The quantum-inspired deep learning model module: based on a deep learning model architecture, superposition state and entangled state principles in quantum computing are introduced in the parameter optimization process, superposition state is used to make parameters simultaneously in multiple possible value states to explore a wider parameter space, and the correlation between parameters is established by simulating entangled state to realize collaborative optimization;

[0010] The diagnostic result output module: visualizes the diagnostic results output by the quantum-inspired deep learning model module.

[0011] Further, the data acquisition module adopts a multi-modal data acquisition method, including acquiring optical microscope image data of tumor cells, acquiring fluorescence microscope image data, acquiring electron microscope image data, and acquiring gene expression data and tumor marker detection data of patients. For image data, different acquisition parameters are set, and the exposure time is set in the range of 0.1ms-100ms according to the sample characteristics to obtain tumor cell images of different detail levels. In the acquisition of gene expression data and tumor marker detection data, standardized data acquisition procedures and equipment are used to ensure the accuracy and consistency of the data. Through multi-modal data acquisition, information of tumor cells can be obtained from multiple dimensions, providing a richer data basis for subsequent analysis and diagnosis. Compared with single-modal data acquisition, the characteristics of tumor cells can be more comprehensively reflected, and the accuracy of diagnosis can be improved.

[0012] Further, when the data preprocessing module cleans the original data, a method based on statistical analysis and machine learning is used. For image data, first, statistical analysis is used to calculate the mean and variance statistics of image pixel values, set a threshold range, and remove noise points with obviously abnormal pixel values. Then, an image denoising model based on convolutional neural network is used. This model can automatically identify and remove complex noise in images by learning a large number of normal image and noise image pairs. For gene expression data and tumor marker detection data, a clustering analysis-based outlier detection method is used to cluster the data and remove data deviating from the cluster center to a certain extent as outliers. In the normalization process, for image data, the method of normalizing to the [0, 1] interval is used, and the formula is: wherein is the original pixel value, and are the minimum and maximum values of the image pixel value, respectively. For gene expression data and tumor marker detection data, the Z-score normalization method is used, and the formula is: wherein is the original data value, is the data mean, The data enhancement operation is for image data, and adopts multiple methods such as rotation, flipping, scaling, and adding random noise. By increasing the diversity of data, the generalization ability of the model is improved, and the fitting problem caused by insufficient data is effectively solved.

[0013] Furthermore, in the quantum-inspired deep learning model module, when the superposition principle is introduced, the model parameters Expressed as a superposition of quantum states:

[0014]

[0015] in is the number of superposition states, is the superposition coefficient, satisfying , Representation parameters No. possible value states, during the training process, by adjusting the superposition coefficient To control the contribution of different parameter values ​​to model training, the superposition coefficient The initial value of is set according to the importance of the parameter and the distribution of the data. The initial value of the corresponding superposition coefficient is relatively large. Then, in the training process, the optimization method based on gradient descent is used to calculate the objective function with respect to the superposition coefficient. Gradient , update the superposition coefficient, the formula is:

[0016]

[0017] in represents the number of training iterations, In this way, the model can explore multiple parameter values ​​simultaneously during training, which greatly broadens the parameter search range. Compared with traditional methods, it can more effectively avoid falling into local optimal solutions and improve the optimization efficiency and performance of the model.

[0018] Furthermore, in the quantum-inspired deep learning model module, when simulating the entangled state principle to establish the correlation between parameters, the entanglement degree matrix between the parameters is defined. , the matrix elements Representation parameters and The degree of entanglement between The initial value of is set according to the function and position relationship of the parameter in the model. During the training process, the entanglement matrix is ​​dynamically adjusted according to the update of the parameters. , the adjustment formula is:

[0019]

[0020] wherein is an adjustment coefficient, and are parameters and is the update amount in the first iteration, in the parameter update, the entanglement matrix is introduced to modify the parameter update, and the formula is:

[0021]

[0022] wherein is the total number of model parameters, in this way, the collaborative optimization between parameters is realized, the parameter update can affect each other, the blindness of parameter update is avoided, the convergence speed of the model is accelerated, compared with the traditional parameter update method, the global optimal solution can be found more efficiently.

[0023] Further, the quantum-inspired deep learning model module adopts an improved convolutional neural network structure, a quantum-inspired feature fusion layer is added after the convolutional layer, in the quantum-inspired feature fusion layer, the superposition and entanglement principle of quantum states is used to fuse the features extracted by different convolutional layers, specifically, the feature maps extracted from different convolutional layers are , which is expressed as the superposition form of quantum states:

[0024]

[0025] wherein is a fusion coefficient, satisfying , the initial value of the fusion coefficient is set according to the importance and correlation of the feature maps, in the training process, the superposition coefficient update method in the superposition state principle is adopted, the gradient of the objective function about the fusion coefficient is calculated to update the fusion coefficient, at the same time, the entanglement relationship between the feature maps is considered, the entanglement matrix between the feature maps is defined, the feature fusion process is adjusted according to the entanglement matrix, so that the fused features can better retain the feature information of tumor cells, improve the extraction and recognition ability of the model to tumor cell features, compared with the traditional convolutional neural network structure, the complex features of tumor cells can be extracted more effectively, and the diagnostic precision is improved.

[0026] Further, the diagnostic result output module adopts a combination of three-dimensional visualization and dynamic visualization when performing visual display. For image data diagnostic results of tumor cells, two-dimensional image data is reconstructed into a three-dimensional model. Through rotation and scaling operations, doctors can observe the morphological structure of tumor cells from different angles. Meanwhile, for growth change or treatment effect evaluation diagnostic results of tumor cells, dynamic visualization is adopted to display the trend of various indexes of tumor cells over time with time series as the axis. During the visual display process, color coding and annotation information are introduced. Different colors are used to distinguish different types of tumor cells or different diagnostic results, and detailed annotation information is added, including the type, size, number, and malignancy degree of tumor cells. Through this rich and diverse visual display method, doctors can more intuitively and comprehensively present the diagnostic results to doctors, which helps doctors more accurately understand the diagnostic results and improves the efficiency and accuracy of diagnostic decision-making.

[0027] Further, the system further includes a model evaluation and update module. New tumor cell data is regularly collected, and the new data is divided into a test set and a training set. The test set is used to evaluate the current quantum-inspired deep learning model. The evaluation indexes include accuracy, recall rate, F1 value, and AUC value. When the evaluation indexes of the model on the test set are lower than the set threshold, the training set data and historical training data are merged, and the model is retrained. During the retraining process, related parameters in the quantum-inspired deep learning model are adjusted according to the characteristics of the new data and the training of the model. In this way, the model can continuously adapt to new tumor cell data features and clinical needs, maintain the accuracy and effectiveness of the model, and continuously improve the performance of tumor cell analysis and diagnosis.

[0028] On the other hand, a deep learning-based intelligent analysis and diagnosis method for tumor cells is provided, which is characterized by the following specific steps:

[0029] S1, data acquisition step: using a data acquisition module to collect original data related to tumor cells;

[0030] S2, data preprocessing step: using a data preprocessing module to perform cleaning, normalization, and enhancement operations on the original data;

[0031] S3, model training step: inputting the preprocessed data into a quantum-inspired deep learning model module. During the training process, the model parameters are optimized based on the superposition state and entangled state principles of quantum computing. The deep learning network structure is used to learn the feature patterns of tumor cells, and the model parameters are continuously adjusted until the model converges.

[0032] S4, a diagnostic analysis step: inputting the tumor cell data to be diagnosed into the trained quantum-inspired deep learning model module, the model analyzes and processes the data, and outputs the diagnostic result;

[0033] S5, a result output step: visualizing and displaying the diagnostic result through the diagnostic result output module.

[0034] Compared with the prior art, the present application has the following beneficial effects:

[0035] (1) The present application introduces a quantum-inspired deep learning model, which optimizes the model parameters using the superposition state and entangled state principles in quantum computing, greatly broadening the parameter search range, and can more effectively avoid falling into a local optimal solution compared with traditional methods, thereby improving the optimization efficiency and performance of the model. This innovative technology makes the analysis and diagnosis of tumor cells more accurate, and through the automated and intelligent processing flow, the diagnostic efficiency is significantly improved, which helps doctors make diagnostic decisions faster.

[0036] (2) The present application adopts a multi-modal data acquisition method, including optical microscope images, fluorescence microscope images, electron microscope images, and gene expression data and tumor marker detection data, which can obtain information of tumor cells from multiple dimensions, providing a richer data basis for subsequent analysis and diagnosis. In addition, the diagnostic result output module adopts a combination of three-dimensional visualization and dynamic visualization to intuitively and comprehensively display the diagnostic result to the doctor, including the morphological structure of tumor cells, growth changes and treatment effect evaluation. This visual display method helps doctors more accurately understand the diagnostic result and improve the efficiency and accuracy of diagnostic decision-making.

[0037] Other advantages, objects and features of the present application will be set forth in part in the following specification, and in part will become apparent to those skilled in the art upon examination of the following, or can be learned from practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0039] Figure 1 It is a kind of deep learning based tumor cell intelligent analysis and diagnosis system flow operation diagram;

[0040] Figure 2This is a flowchart of a tumor cell intelligent analysis and diagnosis method based on deep learning. DETAILED DESCRIPTION

[0041] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0042] Example 1:

[0043] The pathology department of a tertiary hospital received a lung nodule puncture sample from a 45-year-old long-term smoker. The patient was highly suspected of having a malignant tumor. Due to the significant differences in treatment options for lung cancer subtypes (such as adenocarcinoma and squamous cell carcinoma), an accurate diagnosis required a combination of cell morphology, molecular markers, and genetic variation characteristics.

[0044] An Olympus CX43 microscope equipped with a 100x oil objective lens was used to capture 2048×2048 pixel images of five different fields of view with an exposure time of 80 ms (sample transmittance 60%) targeting densely populated areas of the sample, focusing on capturing cell nuclear size, nucleolus number, and chromatin distribution.

[0045] The samples were immunofluorescently stained (labeling CK7 and TTF-1 adenocarcinoma markers) with an excitation wavelength of 488 nm and an exposure time of 30 ms to obtain fluorescent images of cell membrane surface antigen expression for distinguishing non-small cell lung cancer (NSCLC) subtypes.

[0046] Transmission electron microscopy (TEM) imaging of suspected cancer cell areas was performed at a magnification of 5000 times and a resolution of 1024×1024 to observe ultrastructural features such as mitochondrial swelling and loss of microvilli.

[0047] The expression levels of 10 lung cancer-related genes (EGFR, KRAS, ALK, etc.) were detected by RNA-seq. The original data were expressed in TPM (transcripts per million mapped reads), among which the EGFR expression level was 230 TPM (normal tissue <50 TPM).

[0048] The serum CEA level was detected by electrochemiluminescence method using Roche Cobase 601 equipment, and the measured value was 18 ng / mL (normal reference value <5 ng / mL).

[0049] Calculate pixel mean , standard deviation , set the threshold range Abnormal pixels below 40 or above 190 (such as dye contamination noise) are removed, a pre-trained U-Net model (trained based on 100,000 normal lung cell images) is used to automatically remove defocus blur noise after inputting the optical image, and the output signal-to-noise ratio is improved to 35 dB.

[0050] The positive staining area is extracted by threshold segmentation algorithm (Otsu method), the mean fluorescence intensity is calculated, and the proportion of CK7 positive cells (78%) is quantified.

[0051] The EGFR expression data is subjected to DBSCAN clustering analysis, and 3 outliers (expression > 500 TPM, possibly experimental contamination) are identified and removed.

[0052] The formula is used , wherein ng / mL (mean value of healthy population), ng / mL (standard deviation), and the normalized value is

[0053] Random rotation (-20°~+20°), horizontal flip (probability 50%), Gaussian blur (kernel size 3x3, σ=1.0), 20 times expansion data is generated to alleviate the problem of small sample overfitting.

[0054] In the convolutional layer of ResNet-50, the 3x3 convolution kernel parameters are represented as five superposition states , and the initial superposition coefficients are set according to the contribution of parameters in feature extraction: main branch (responsible for edge detection) , side branch (responsible for texture analysis) , and the rest During training, the is updated by gradient descent: learning rate After 1000 iterations, the loss function decreases to 0.23, at which time is updated to 0.35, is updated to 0.38, indicating that the model pays more attention to texture features (related to the gland lumen structure of adenocarcinoma).

[0055] The entanglement matrix of the convolutional layer parameters (responsible for spatial features) and the fully connected layer parameters (responsible for classification decision) is defined, and the initial value is According to the hierarchical distance, the decay coefficient is set (such as adjacent layers , and cross-layer ), and the is dynamically adjusted during iterationWhen the EGFR-related feature parameters are detected update amount , classification layer parameters update amount , from 0.2 to , strengthen the relevance of genetic features and classification results.

[0056] Using the VTK library to reconstruct the optical image into a three-dimensional model with transparency, the doctor rotates by dragging the mouse, and observes that the cell nucleus is irregular lobulated (the number of lobes is ≥3), the nucleus-cytoplasm ratio is >0.7 (normal <0.5), which meets the malignant characteristics.

[0057] Call the patient's CEA detection data in the past 3 months (22 ng / mL before treatment→18 ng / mL after 2 weeks of treatment→15 ng / mL after 4 weeks of treatment), and dynamically display the downward trend of the area under the curve with a time axis, combined with color coding (red→orange→yellow) to intuitively reflect the reduction of tumor load. The cell type is "lung adenocarcinoma", the cell nucleus volume (normal <200), TTF-1 positive rate 92%, EGFR mutation abundance 15% (suggesting suitable for targeted therapy), AUC value 0.96 (high reliability of model prediction).

[0058] Example two:

[0059] A county-level hospital is equipped with a portable pathological detection device, and needs to quickly screen 100 breast cancer samples, with a single sample processing time <30 minutes, to prioritize identifying high-risk patients for referral to higher-level hospitals.

[0060] Using MobileScopeML100 portable microscope (built-in CMOS sensor), combined with 40x objective lens, scanning breast puncture liquid smear, collecting 1024x1024 pixel image, magnification equivalent to 400x of traditional microscope, for the scattered cell area in the sample, set the automatic exposure mode (average exposure time 50ms), avoid feature occlusion caused by cell overlap.

[0061] Through Alerei portable detector, two tumor markers CA15-3 and CA125 are detected simultaneously, and the results are obtained within 15 minutes. The CA15-3 value of a certain sample is 38 U / mL (normal reference value <28 U / mL), and the CA125 value is 35 U / mL (normal <35 U / mL, critical value needs to be combined with image analysis).

[0062] The original 512x512 pixel image was upsampled to 1024x1024 using bicubic interpolation, and the contrast between cytoplasm and nucleus was enhanced using adaptive histogram equalization (CLAHE) to highlight abnormal features such as thickened nuclear membrane (thickness > 2 pm) and enlarged nucleolus (diameter > 1.5 pm).

[0063] Median filtering (kernel size 3x3) was used to remove electronic noise, and statistical-based background subtraction was used to remove dye precipitates on the smear (areas > 50 pixels were judged as noise).

[0064] The CA15-3 data was linearly scaled to map the original range [10, 50 U / mL] to [0, 1], with the formula The normalized value of this sample is (38-10) / 40 = 0.7.

[0065] The improved MobileNetV3 architecture was used, with a quantum-inspired feature fusion layer added after the bottleneck layer to fuse three feature maps of different depths (shallow edge features, middle texture features, and deep shape features). By setting the initial fusion coefficient values (0.3 for shallow, 0.4 for middle, and 0.3 for deep), the key morphological features such as cell boundary clarity and nucleus-cytoplasm ratio were prioritized.

[0066] The hospital's historical accumulation of 2000 breast cell images (including 500 malignant samples) were used for transfer learning, with the first 15 convolutional layers frozen and only the last 5 layers and fully connected layers trained. The batchsize was set to 32, and after 200 iterations, the accuracy on the validation set reached 88% (satisfying the needs of primary screening).

[0067] A two-dimensional pseudo-color image was displayed on the screen of the portable device, and suspicious areas were quickly located through color mapping (cool tones representing normal cells and warm tones representing abnormal cells). After the doctor clicked on the area of interest, the system automatically labeled the cell diameter (22 pm, normal < 15 pm) and the nucleus-cytoplasm ratio 0.65 (normal < 0.5).

[0068] Combining image features and marker data, the system outputs "low risk", "medium risk", and "high risk" warnings. This sample was judged as "high risk" due to elevated CA15-3 and irregular nuclear morphology (circularity < 0.7), and was recommended for referral to a higher-level hospital for further biopsy within 48 hours.

[0069] A PDF report containing image screenshots, marker values, and risk levels was automatically generated, supporting Bluetooth printing or transmission to the pathology department of the higher-level hospital through the hospital cloud platform. The entire process took 28 minutes, meeting the requirements of rapid screening.

[0070] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application in any form. Although the present application has been disclosed with the preferred embodiments as above, it is not intended to limit the present application. Any person skilled in the art can make some changes or modifications to the above disclosed technical content to obtain equivalent embodiments with equivalent changes, as long as the changes or modifications do not deviate from the technical solution of the present application. Any modification, change, equivalent change and modification of the above embodiments made according to the technical essence of the present application still belong to the scope of the technical solution of the present application.

Claims

1. A tumor cell intelligent analysis and diagnosis system based on deep learning, characterized by: The system consists of the following components: data acquisition module, data preprocessing module, quantum-inspired deep learning model module, and diagnosis result output module: The data acquisition module is used to collect various types of original data such as tumor cell image data and related pathological data; The data preprocessing module is used to clean, normalize and enhance the preprocessing operations on the collected raw data; The quantum-inspired deep learning model module: Based on the deep learning model architecture, it introduces the superposition and entanglement principles of quantum computing into the parameter optimization process. It uses superposition to make parameters simultaneously in multiple possible value states to explore a wider parameter space. It also simulates entanglement to establish correlations between parameters and achieve collaborative optimization. The diagnostic result output module is used to visualize the diagnostic results output by the quantum-inspired deep learning model module.

2. The deep learning-based intelligent analysis and diagnosis system for tumor cells according to claim 1, characterized in that: The data acquisition module adopts a multimodal data acquisition method, including collecting optical microscope image data of tumor cells, fluorescence microscope image data, electron microscope image data, as well as patient gene expression data and tumor marker detection data. For image data, by setting different acquisition parameters, the exposure time is set within a range according to the sample characteristics. When collecting gene expression data and tumor marker detection data, standardized data acquisition processes and equipment are used. Through multimodal data acquisition, information on tumor cells can be obtained from multiple dimensions.

3. The deep learning-based intelligent analysis and diagnosis system for tumor cells according to claim 1, characterized in that: When the data preprocessing module cleans the raw data, it uses a method based on a combination of statistical analysis and machine learning. For image data, it first uses a statistical analysis method to calculate the mean and variance statistics of the image pixel values, sets a threshold range, and removes noise points with abnormal pixel values. Then, it uses an image denoising model based on a convolutional neural network. This model can automatically identify and remove complex noise in the image by learning a large number of normal image and noise image pairs. For gene expression data and tumor marker detection data, it uses an outlier detection method based on cluster analysis to cluster the data. Data that deviates from the cluster center to a certain extent is regarded as an outlier and removed. In terms of normalization processing, the image data is normalized to the [0, 1] interval. The formula is: ,in is the original pixel value, and are the minimum and maximum values ​​of the image pixel values, respectively. For gene expression data and tumor marker detection data, the Z-score normalization method is used, and the formula is: ,in is the original data value, is the data mean, The data enhancement operation is for image data, using multiple methods such as rotation, flipping, scaling, and adding random noise.

4. The deep learning-based intelligent analysis and diagnosis system for tumor cells according to claim 1, characterized in that: In the quantum-inspired deep learning model module, when the superposition principle is introduced, the model parameters Expressed as a superposition of quantum states: in is the number of superposition states, is the superposition coefficient, Representation parameters No. possible value states, during the training process, by adjusting the superposition coefficient To control the contribution of different parameter values ​​to model training, the superposition coefficient The initial value of is set according to the importance of the parameter and the distribution of the data. The initial value of the corresponding superposition coefficient is relatively large. Then, in the training process, the optimization method based on gradient descent is used to calculate the objective function with respect to the superposition coefficient. Gradient , update the superposition coefficient, the formula is: in represents the number of training iterations, is the learning rate.

5. The deep learning-based intelligent analysis and diagnosis system for tumor cells according to claim 1, characterized in that: In the quantum-inspired deep learning model module, when simulating the entangled state principle to establish the correlation between parameters, the entanglement degree matrix between the parameters is defined. , the matrix elements Representation parameters and The degree of entanglement between The initial value of is set according to the function and position relationship of the parameter in the model. During the training process, the entanglement matrix is ​​dynamically adjusted according to the update of the parameters. , the adjustment formula is: in is the adjustment coefficient, and The parameters are and In the The update amount in the iteration is introduced when the parameters are updated. Correct the parameter update, the formula is: in is the total number of model parameters.

6. The deep learning-based intelligent analysis and diagnosis system for tumor cells according to claim 1, characterized in that: The quantum-inspired deep learning model module adopts an improved convolutional neural network structure, and adds a quantum-inspired feature fusion layer after the convolution layer. In the quantum-inspired feature fusion layer, the superposition and entanglement principle of quantum states is used to fuse the features extracted from different convolution layers. Specifically, the feature maps extracted from different convolution layers are set as , which is expressed as a superposition of quantum states: in is the fusion coefficient, the fusion coefficient The initial value of is set according to the importance and relevance of the feature map. During the training process, the superposition coefficient update method in the superposition state principle is adopted to calculate the objective function with respect to the fusion coefficient. The gradient is used to update the fusion coefficient. At the same time, considering the entanglement relationship between the feature maps, the entanglement degree matrix between the feature maps is defined. , the feature fusion process is adjusted according to the entanglement matrix.

7. The deep learning-based intelligent analysis and diagnosis system for tumor cells according to claim 1, characterized in that: The diagnostic result output module adopts a combination of three-dimensional visualization and dynamic visualization when performing visual display. For the diagnostic results of tumor cell image data, the two-dimensional image data is reconstructed into a three-dimensional model. Through rotation and scaling operations, doctors can observe the morphological structure of tumor cells from different angles. At the same time, for the growth changes of tumor cells or the evaluation of treatment effects, a dynamic visualization method is adopted, with time series as the axis, to display the changing trends of various indicators of tumor cells over time. In the process of visual display, color coding and annotation information are also introduced. Different types of tumor cells or different diagnostic results are distinguished by different colors, and detailed annotation information is added, including key indicators of tumor cell type, size, number, and malignancy.

8. The deep learning-based intelligent analysis and diagnosis system for tumor cells according to claim 1, characterized in that: The system also includes a model evaluation and update module, which regularly collects new tumor cell data, divides the new data into a test set and a training set, and uses the test set to evaluate the current quantum-inspired deep learning model. The evaluation indicators include accuracy, recall rate, F1 value, and AUC value. When the evaluation indicators of the model on the test set are lower than the set threshold, the training set data is merged with the historical training data, and the model is retrained. During the retraining process, the relevant parameters in the quantum-inspired deep learning model are adjusted according to the characteristics of the new data and the training status of the model.

9. A method for intelligent analysis and diagnosis of tumor cells based on deep learning, applicable to the intelligent analysis and diagnosis system for tumor cells based on deep learning according to any one of claims 1 to 9, characterized in that: The specific steps of this method are: S1, data acquisition step: using the data acquisition module to collect raw data related to tumor cells; S2, data preprocessing step: the raw data is cleaned, normalized, and enhanced through the data preprocessing module; S3, model training step: The preprocessed data is input into the quantum-inspired deep learning model module. During the training process, the model parameters are optimized based on the superposition and entanglement principles of quantum computing. The deep learning network structure is used to learn the characteristic patterns of tumor cells, and the model parameters are continuously adjusted until the model converges; S4, diagnostic analysis step: the tumor cell data to be diagnosed is input into the trained quantum-inspired deep learning model module, the model analyzes and processes the data and outputs the diagnostic results; S5, result output step: the diagnosis result is visually displayed through the diagnosis result output module.

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