64 results about "Cervical cells" patented technology

The invention discloses an overlapped cervical cytoplasm region segmentation method based on deep learning and a conditional diffusion model, and relates to the technical field of artificial intelligence analysis of medical images. According to the method, accurate segmentation of the overlapped cytoplasm region in the cervical cell image is realized through a morphological prior guided conditional diffusion process. The method comprises the following steps: constructing a multi-scale cervical cytoplasm mask pair image; designing a cytoplasm specific data enhancement and preprocessing process; building a multi-branch cervical cell morphology perception condition diffusion network; using a self-adaptive multi-scale combination loss function to optimize training; and a hierarchical classifier is adopted to freely guide sampling for reasoning. According to the method, the frequency domain and space domain features are fused, a cellular morphology and statistics priori knowledge base is established, and a strategy of generating complete cytoplasm by adopting non-overlapped parts is adopted, so that the problem that the traditional method is difficult to segment in complex backgrounds and overlapped regions is successfully solved, and reliable technical support is provided for early screening of cervical cancer.

The invention discloses a cervical cell image intelligent diagnosis system based on a multi-modal visual language large model, belongs to the field of cervical cell image recognition, and particularly relates to the cervical cell image intelligent diagnosis system based on the multi-modal visual language large model. In order to solve the problems of low diagnosis accuracy, strong subjectivity, insufficient efficiency and lack of interpretation in the prior art, the invention provides a cervical cell image intelligent diagnosis system based on a multi-modal visual language large model. The system comprises an image block acquisition module, an image preprocessing and color normalization module, an effective tissue region screening module, a cell region extraction and post-processing module, an image end and text end processing module, a visual language large model acquisition and training module, a cell description text generation module in an inference stage, and a risk judgment and classification module. An interpretability verification and credibility evaluation module; and a structured diagnosis report output module.

The invention discloses a cervical cytopathy detection method based on a hypergraph convolutional network. The method comprises the following steps: performing sliding window slicing processing, unsupervised image decomposition and dyeing normalization on a cervical cytopathy image, generating normalized image input, and constructing an image input sample set; a target detection model is constructed based on YOLO11, a block-level feature extraction network (BBMM) module is embedded to enhance the perception ability, and a sparse attention module is adopted to perform key region feature enhancement; constructing a hypergraph neural network HGNN module based on a Patch-level relationship, and extracting a structural relationship between cells; integrating an uncertainty quantification mechanism, and generating a confidence thermodynamic diagram; a front-end and rear-end separated diagnosis platform is built, image uploading, detection result display, frame selection correction and interactive management are supported, and whole-process auxiliary diagnosis is achieved; the method has the advantages of high detection accuracy, high interpretability, flexible deployment and the like, and is suitable for intelligent early screening and clinical auxiliary diagnosis scenes of cervical cytopathy.

The invention relates to the technical field of clinical diagnosis, in particular to a cervical cancer risk diagnosis system. The image processing module is used for extracting image features based on a preprocessed cervical cell pathological image; the text processing module is used for extracting text features based on the preprocessed medical record book data; the feature alignment module is used for aligning the image features and the text features to obtain the aligned image features and text features; the feature fusion module is used for fusing the aligned image features and text features by using a mutual attention mechanism to obtain fused features; and the prediction module is used for predicting the cervical cancer onset risk of the target patient by using a preset tumor risk prediction model based on the fusion features to obtain a dichotomy prediction result. Therefore, through the cervical cancer risk diagnosis system, the problem of low screening accuracy caused by manual limitation or difficulty in multi-modal data fusion in the existing diagnosis technology is solved, and the accuracy of early screening of cervical cancer is improved.

The invention provides a cell pathology intelligent auxiliary method based on a CLIP-RETRIEVAL model, and relates to the technical field of medical image processing, and the method comprises the following steps: S10, obtaining data of a cervical cell pathology image and a pathology text, and constructing a structured data set of an image-text pair; s20, preprocessing the cervical cell pathological image, and respectively generating corresponding feature vectors according to the preprocessed cervical cell pathological image and pathological text; s30, constructing a unified medical semantic embedding space, aligning feature vectors of the image-text pairs through the dynamic projection matrix P, and outputting alignment features; and S40, inputting the alignment features into an LLM model, and generating a structured pathology report. Through data acquisition, preprocessing, multi-modal feature extraction, semantic alignment and vertical question and answer function adaptation, the problems of high misuse rate of medical terms and semantic segmentation of images and texts are solved, and the accuracy and interpretability of pathological diagnosis are improved.

The invention discloses a cervical cell segmentation method combining semantic condition diffusion and knowledge verification, which comprises the following steps: S1, constructing a cervical cancer diagnosis vertical field multi-modal large model, and performing fine tuning on an open-source multi-modal large model through a low-rank adaptation supervision fine tuning method to obtain the cervical cancer diagnosis vertical field multi-modal large model; s2, generating a semantic vector for guiding segmentation; s3, performing preliminary segmentation based on a conditional diffusion model, performing iterative denoising on the to-be-segmented image, and generating a preliminary cervical cell segmentation mask; and S4, performing segmentation post-processing based on domain knowledge: performing morphological processing on the preliminary segmentation mask to obtain a final cervical cell segmentation result. According to the method, large-scale text data can be fully utilized and converted into refined semantic information for guiding image segmentation, so that the challenge of scarcity of labeled samples is overcome, the performance, generalization ability and clinical applicability of a cervical cell abnormal region segmentation model are remarkably improved, and finally more accurate and more reliable cervical cell pathological diagnosis assistance is realized.

This invention discloses a cervical cell classification method based on an attention mechanism and the Swing Transformer, belonging to the field of medical image processing technology. This invention proposes a CFA-Former network model based on a CFA module. By combining channel attention and spatial attention, it overcomes the limitations of traditional models in capturing multi-scale features. Furthermore, by strengthening the focus on important information and location, this model effectively improves the accuracy and robustness of cervical cell classification tasks. In the model design, the CFA module adaptively focuses on and suppresses important features through two learning paths, comprising two sub-modules: CDA and SFA. The CDA module optimizes information extraction in the channel dimension through a lightweight channel attention mechanism, while the SFA module enhances the model's feature representation ability in the spatial dimension through a strengthened spatial attention mechanism, demonstrating significant advantages, especially when dealing with complex cell images.

Disclosed herein is a method for training a network to detect and classify abnormal pathologies in images if cells. Specifically, the network uses a deep framework optimally trained to detect and classify abnormal cervical cells in pap smear images.

The invention relates to a cell morphology feature extraction and prediction analysis method, in particular to a cell morphology feature extraction and prediction analysis method for a high-throughput cervical TCT image. The method aims at solving the problems that in the cell analysis technology of the cervical TCT image, complex morphological characteristics of cells are not fully considered, the characteristic extraction precision is low, and the manual dependence degree is high. The method comprises the following steps: selecting cellular morphology characteristics of cervical TCT image auxiliary diagnosis; obtaining a segmentation result of the cell nucleus and the cytoplasm based on the segmentation network; extracting, measuring and calculating cell morphological characteristics based on a cervical TCT image segmentation result; and analyzing and visualizing the measurement and calculation results of the morphological characteristics of the cells. According to the method, medical priori knowledge and data-driven research methods are fused, and more interpretable quantitative data are provided for subsequent cervical cell research through conjoint analysis of shape features and texture features. The invention belongs to the technical field of medical image analysis and computer vision.

The application relates to a kind of mitochondria-based single cell feature extraction and analysis method and system, comprising: obtaining the multiple modal images such as bright field image, nucleus fluorescent image and mitochondria fluorescent image of single cell;Image preprocessing is carried out to the three modal images obtained;For different structures such as mitochondria, morphological and texture features are extracted, and feature analysis is carried out;Further, through the fusion of mitochondria and machine learning technology, the automatic classification of cells is realized.The application is used for the classification of human cervical epithelial cells (H8) and cervical cancer cells (HeLa), and the machine learning analysis of morphological features and texture features shows the potential of mitochondria in the classification of cervical cells.The application has strong applicability, can be combined with machine learning and other analysis methods, and can be applied to various biological cells, has universality, and is easy to popularize.

A cervix uteri cell image enhancement method based on a generative adversarial network comprises the following steps: carrying out statistical analysis on a cervix uteri cell image data set, and confirming that a high category imbalance problem exists; according to morphological characteristics and clinical importance of cells of different lesion categories, designing differentiated enhancement strategies and target quantities; for extremely scarce high-risk categories, a StyleGAN2-ADA model constrained by medical knowledge is adopted for generation, and for other categories, a traditional enhancement method combining CLAHE, elastic deformation and the like is adopted; and performing quality control through Masked-SSIM and Med-FID medical image evaluation indexes and pathology expert auditing to obtain a high-quality enhanced image. According to the method, the problem of class imbalance in cervical cell image data is effectively solved, the recognition performance of a deep learning model on high-risk cervical lesions is remarkably improved, and the method has important clinical auxiliary diagnosis value.

The invention discloses a cervical cancer identification method based on a neural network, and the method comprises the following steps: S1, collecting a cervical cell image, marking the types of normal cells and lesion cells of different levels, so as to construct a training data set; S2, carrying out the color enhancement and normalization preprocessing of the image, and improving the subsequent identification effect; s3, designing an improved neural network algorithm, and dynamically optimizing a model training process through multi-scale feature extraction, a second-order attention mechanism and an adaptive regularization strategy for introducing the change rate of a ramp signal in a signal system; S4, training a neural network model by using the preprocessed image; and S5, finally identifying the to-be-detected image based on the trained model, and outputting whether the cervical cancer exists or not and a grading result. According to the method, the image feature readability is enhanced through preprocessing, the regularization intensity is dynamically adjusted by combining an innovative neural network structure and the change rate 0 of a ramp signal in a signal system, the accuracy of cervical cancer recognition and the model generalization ability are effectively improved, and an efficient and reliable technical scheme is provided for automatic screening of cervical cancer.

According to the cervical cell automatic detection method based on the improved YOLOv8 model, the method is improved based on the YOLOv8 model, the framework of the YOLOv8 model is emphatically optimized, and the recognition precision is improved. A large separable kernel attention (LSKA) module is introduced into the improved YOLOv8 model, and a large Separable KernelAttention (LSKA) module is introduced into the improved YOLOv8 model. The module can more effectively capture the multi-scale features of cervical cells through large-kernel separation convolution and an attention mechanism, improves the recognition capability of cell details, and achieves more comprehensive and accurate target positioning and recognition. The method provided by the invention effectively improves the recognition accuracy of cervical cells, provides an efficient and reliable auxiliary means for early diagnosis and analysis of cervical diseases, has a wide application prospect, and is expected to play an important role in the field of medical diagnosis.

A multi-scale dual-stream abnormal cervical cell detection method that integrates pathological knowledge. The present invention relates to the problem of sparse features and difficulty in detecting abnormal cells of different sizes in the technology of abnormal cell detection in panoramic images of cervical cell pathology. Early detection of abnormal cervical cells in cervical cancer screening can increase the chance of timely treatment, but manual detection requires experienced pathologists, is time-consuming and prone to errors. The technology of abnormal cell detection in panoramic images of cervical cell pathology has the problem of sparse features of abnormal cells in panoramic images and difficulty in detecting abnormal cells of different sizes. To improve this problem, the present invention proposes a multi-scale dual-stream abnormal cervical cell detection method that integrates pathological knowledge. Experiments show that this method can effectively integrate pathological knowledge, significantly improve the quality of multi-scale detection area suggestions, robustly detect cells of different sizes, and improve the accuracy, sensitivity and specificity of cervical cell detection. It provides effective and efficient technical support for cervical cancer screening and improving pathology workflows, helping pathologists make more accurate diagnoses. The present invention is mainly used for abnormal cell detection in panoramic images of cervical cell pathology.

The invention provides a cervical cell characterization classification method based on a structure constraint network, and the method comprises the following steps: A, carrying out the data enhancement of a cell image, and generating different enhanced views of the same cell; b, performing feature extraction on the enhanced view by adopting a convolutional neural network to obtain cell space structure features; c, introducing a bulldozer distance EMD (Earth Mover's Distance) to apply spatial structure consistency constraint on the enhanced view, and taking the spatial structure consistency constraint as a measurement standard for structure distribution alignment; and D, inputting the features subjected to structure alignment into a classifier to complete cell category identification. According to the method, the EMD measurement is combined to apply the structural consistency constraint in the self-supervised learning framework, so that the network can extract stable and discriminative features under an unsupervised condition, and the problems of cellular morphology diversity, dyeing difference, insufficient labeling and the like are effectively solved. According to the method, excellent accuracy is obtained in an open-source cervical cell data set experiment, good robustness is shown, and an efficient and reliable technical scheme is provided for large-scale automatic analysis of clinical cervical cells.

The invention relates to the technical field of biological medicine, and particularly discloses a targeted nano NK cell exosome gel loaded with lycopene and application thereof, the targeted nano NK cell exosome gel loaded with lycopene is prepared from the following components in percentage by mass: 0.1 to 1 weight percent of lycopene oleoresin, 0.5 to 5 weight percent of NK cell exosome, 60 to 80 weight percent of gel system and the balance of ultrapure water; the lycopene oleoresin is prepared by the following steps: firstly, adding a eutectic solvent into a tomato fermentation extracting solution, carrying out ultrasonic extraction on lycopene, and then uniformly mixing the lycopene, sophorolipid and vegetable oil. According to the application, the NK cell exosome and the lycopene are organically matched, the NK cell exosome directly acts on the local part of the cervix uteri through the NK cell, and meanwhile, the lycopene can repair the damaged cervix uteri cell and restore the damaged cervix uteri cell to a normal structure, so that the purposes of removing viruses and preventing and treating cervical cancer are achieved.

The invention relates to the technical field of medicines, in particular to an intelligent response type cervical auxiliary fertility gel which comprises the following components in percentage by mass: 0.1-2% of a gel matrix which is selected from one or more of hydroxypropyl methyl cellulose, hydroxyethyl cellulose, sodium alginate and gellan gum; 0.05%-0.5% of a gel adjusting auxiliary agent which is selected from zinc chloride or calcium lactate; the osmotic pressure regulator is selected from one or more of glycerol, glucose, sodium chloride, mannitol, sorbitol, polyethylene glycol and propylene glycol; 0.1%-2% of an acid-base regulator which is selected from one or more of disodium hydrogen phosphate, sodium dihydrogen phosphate, dipotassium phosphate and potassium dihydrogen phosphate; according to the present invention, the toxicity grade of the gel on cervical cells is less than or equal to grade 2 (ISO 10993-5 standard), the cell survival rate is more than or equal to 70%, and the safety is high.

The invention relates to a cervical image processing method and system, and relates to the technical field of cervical image processing. The cervical image processing method comprises the following steps: acquiring a cervical cell nucleus cluster mask image corresponding to a cervical liquid-based microscopic image; determining whether an overlapping mask area exists between each cervical cell nucleus mask and the adjacent cervical cell nucleus mask in the cervical cell nucleus cluster mask image; if the overlapped mask area exists, extracting the cervical cell nucleus pixel characteristics of the cervical cell nucleus cluster image corresponding to the cell nucleus cluster mask image; and deleting cervical cell nucleus pixels corresponding to an overlapping mask region in the cervical cell nucleus cluster image or determining cervical cell nucleus pixels corresponding to a non-overlapping mask region in the cervical cell nucleus cluster image. According to the embodiment of the invention, the cervical image can be processed.

The application relates to an intelligent diagnosis method for cervical cell atrophy levels, and relates to the problem that intelligent discrimination of cervical cell atrophy degrees is lacking in automatic pathological diagnosis technology. Cervical vaginal squamous epithelium is divided into surface layer cells, middle layer cells and basal layer cells, and ovarian estrogen affects the growth and maturity of the cells; a decrease in estrogen level can cause symptoms such as atrophic vaginitis and osteoporosis, which need to be treated in time; at present, squamous cell atrophy diagnosis is not clear enough, and there are few reports on atrophy degree diagnosis research, so it is of great significance to establish a systematic and intelligent diagnosis process for cervical cell atrophy degree discrimination. In order to improve the problem, the application provides an intelligent diagnosis method for cervical cell atrophy levels; the method first detects the surface layer cells, the middle layer cells and the basal layer cells by using a target detection model, then segments the cell nucleus of each layer of cells detected by using an instance segmentation model, and finally calculates the cell quantity ratio, the nucleus-cytoplasm ratio and the cell crowding degree index of each layer; the indexes are input into a random forest classification model to grade the atrophy degree; it is known through sufficient experimental verification that good effects are achieved in cervical cell atrophy degree discrimination. The application is applied to cervical cell atrophy degree discrimination.

The application provides a cervical cell nucleus image segmentation method and system based on a residual network and a hollow convolution, a low-layer feature extraction network of sequential convolution is reused, low-layer feature information is added to high-layer information, the low-layer information and the high-layer information are complementary, the utilization rate of a low-layer feature layer with more detailed information is improved, and the learning ability of the network for cells is enhanced.

The application discloses a cervical abnormal cell recognition method and device and electronic equipment, and relates to the technical field of cervical cancer detection. The method comprises the following steps: acquiring a cervical cell slice image; inputting the cervical cell slice image into a pre-trained channel separation module to obtain an H image and a DAB image, wherein the H image is a hematoxylin staining image, and the DAB image is an immunohistochemical staining image; and recognizing cervical abnormal cells by using the H image and the DAB image. The technical scheme of the application can obviously improve the cell detection speed, enhance the recognition accuracy and improve the algorithm execution efficiency in cervical cancer positive cell detection.

The invention discloses a cervical cell image classification method based on dynamic topological optimization and Bayesian reasoning. The method comprises the following steps: constructing and pre-training a basic feature extraction model; training a dynamic topology network; carrying out adaptive Bayesian optimization; performing uncertainty quantification and reasoning; image uploading and model selection are realized through a graphical interface, and confidence distribution and a feature heat map of a classification result are displayed to assist clinical diagnosis; according to the method, through the synergistic effect of the stages, the classification accuracy and robustness are remarkably improved.

The invention relates to a multi-round dialogue type intelligent diagnosis interaction system, in particular to a cervical cell panorama-oriented multi-round dialogue type intelligent diagnosis interaction system. The objective of the invention is to solve the problems that existing diagnostic information is single, insufficient in explanatory property and poor in interaction flexibility, and finer diagnostic information is difficult to obtain; the problems that multi-round interaction and context memory ability are lacked, and continuous and coherent response cannot be made in combination with historical information are solved. Comprising an image block acquisition module used for acquiring a pre-processed full slice image WSI; the image segmentation and effective area screening module is used for obtaining effective image blocks; the image-text pair construction and pathological attribute acquisition module is used for forming attribute vectors; the context-aware multi-round question and answer module is used for obtaining a final answer; and the final diagnosis suggestion generation module is used for obtaining a final diagnosis suggestion based on knowledge graph reasoning and probability model prediction. The method is applied to the field of multi-round dialogue type intelligent diagnosis interaction.

The invention discloses a rapid classification detection and positioning method for cervical cancer. The method comprises the following steps: step 1, inputting a training sample and performing data enhancement on the sample; 2, training a multi-task learning model; step 3, carrying out rapid target detection based on YOLOv5; and step 4, generating a classification and positioning result of the cervical cancer cell lesion area. According to the method, a multi-task learning model is adopted, the number of training samples is increased by enhancing and optimizing cervical cell image data, and the problems that the number of the samples is insufficient and categories are unbalanced are solved; a multi-task learning framework with shared features is utilized, classification and positioning tasks are considered, and the accuracy and robustness of the model in cervical cancer detection are remarkably improved; by introducing the YOLOv5 network model, the cervical cell lesion area is rapidly detected and positioned, the extracted lesion area features are classified and regressed, and the method has remarkable advantages in the aspects of detection speed and positioning precision.

The application provides a cervical cancer detection method based on a Simsiam Mae self-supervised model, comprising the following steps: S1, acquiring and preprocessing a cervical cell image; S2, data preprocessing; S3, Simsiam Mae self-supervised model training, wherein the Simsiam Mae self-supervised model uses a contrast learning method to train an encoding path, a projection layer and a prediction layer to extract low-frequency information features of the cell image; a masked image modeling method is used to train the encoding path and a decoding path to extract high-frequency information features of the cell image; S4, downstream task training; and S5, using the trained Simsiam Mae self-supervised model to detect cervical cancer. The application greatly reduces the dependence on artificial labeled data, saves time and cost, and improves the classification accuracy of cervical cell images.

The application relates to the technical field of medical image processing, and particularly discloses a cervical cancer screening pathological large model construction method based on DINOv3-TCT. The technical problem to be solved by the application is that when a general visual Transformer model is directly applied to a cervical cell instance segmentation task, there are problems of mismatch between model internal feature representation and instantiation target and high calculation redundancy. Therefore, the application proposes to transform the DINOv3 encoder: inject a learnable query token at a specific layer, and innovatively construct an instance decoupling loss term acting on an attention weight matrix to explicitly guide the encoder to realize instance-level feature separation during training. The method optimizes the internal attention distribution of the model, improves the accuracy of dense cell segmentation, and simultaneously reduces the model parameter quantity and calculation overhead through integrated design, so that the balance between high accuracy and high efficiency is realized.

The invention discloses an intelligent labeling method and system for a multi-modal image of a cervical cell, which can realize primary position category labeling and secondary multi-attribute labeling of a multi-target area of a medical image. The system comprises seven modules of image display, prediction assistance, annotation box editing, category label management, attribute label management, annotation storage and annotation recovery, coordinate mapping of an original image and a display image is established through an image scaling factor, loading of pre-training models such as YOLO and the like is supported to automatically generate candidate annotation boxes, and a user can execute operations such as creation and movement on the annotation boxes. According to the scheme, when the image is reloaded, the first-level labeling box and the second-level multi-attribute label can be completely recovered, the cervical cell and pathological image labeling content is effectively expanded, the data reliability is improved, and the method can be directly used for training an interpretable medical image reasoning large model.

The application discloses a cervical cell detection method based on multi-scale space information and relates to the technical field of medical images. The application combines the advantages of high speed and high precision of Sparse R-CNN, the extraction ability of a multi-scale space information extraction branch and a channel attention module for space features and channel features, and better detection performance is obtained, so that the problem of insufficient detection precision of existing cervical lesion cells is solved.

The application relates to a visual attention-based unsupervised cervical cell instance segmentation method, and relates to the problems of missing labeled data and accurate segmentation of cervical cells in intelligent auxiliary diagnosis technology of cervical cancer. Computer intelligent auxiliary diagnosis technology is widely applied, and cell segmentation technology is the basis of various downstream tasks. A deep learning model needs a large amount of labeled data for training, pixel-level labeling is time-consuming and labor-consuming, and there are impurities such as bacteria, white blood cells and bubbles caused by physiological reasons and film reasons, in addition, there are problems such as overlapping adhesion and visual inseparability of cervical cell images. In order to improve these problems, a visual attention-based unsupervised cervical cell instance segmentation method is provided. Experiments show that the method can effectively improve the accuracy of segmentation and reduce the missing detection problems caused by the interference of impurities and incomplete labels. The application is applied to accurate segmentation of cervical cells under the condition of no label.