112 results about "Cervical cell" patented technology

The invention belongs to the technical field of biological detection, and particularly relates to a monoclonal antibody 4F6 for HPV16 type E7 protein detection and application. The heavy chain variable region CDR sequences and the light chain variable region CDR sequences of the antibody are SEQ ID NO.1-3 and SEQ ID NO.4-6 respectively, and the antibody can be specifically combined with HPV16 type E7 protein without cross reaction. A double-antibody sandwich ELISA detection system constructed based on the antibody is combined with a biotin-avidin amplification technology, the sensitivity reaches 100 pg / mL, and the antibody is suitable for rapid detection of the HPV16 type E7 protein in a cervical exfoliated cell sample. The invention further provides the HPV16 type E7 recombinant protein which is obtained through prokaryotic expression and has immunocompetence, and the HPV16 type E7 recombinant protein is applied to antibody screening and detection. Compared with nucleic acid detection, the method has a lower false positive rate, can be used for early screening of cervical cancer and precancerous lesions, and has a good application prospect.

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.

Disclosed herein is a multi-supervised deep framework that can automatically detect, segment, and classify abnormal cervical cells. The multi-supervised framework has semi-supervised, weakly-supervised and unsupervised modes which are effective in detecting cervical cells and classifying the cells as being normal or abnormal. The various modes address the situation in which labelled training data is sparse compared to unlabeled training data.

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 biological detection, in particular to a monoclonal antibody combination for HPV18 type E6 protein detection and application. The provided combination is composed of 5G3 and 2C7, and the amino acid sequences of complementary determining regions of variable regions of a heavy chain and a light chain of the combination are clear and are respectively shown as SEQ ID NO.1-12. The antibody combination has high specificity and sensitivity, the lowest detection limit can reach 100 pg / ml, and cross reaction with other HPV subtypes is avoided. According to a double-antibody sandwich ELISA and biotin-avidin amplification detection system constructed based on the combination, the signal intensity and the detection accuracy are remarkably improved, and the combination is suitable for rapid detection of the HPV18 type E6 protein in a cervical exfoliated cell sample and has application value in early diagnosis of cervical cancer, risk stratification, vaccine research and development and curative effect evaluation.

The invention provides a method and device for directly carrying out HPV prediction by utilizing a cervical cell pathology image, electronic equipment, a storage medium and a program, and relates to the technical field of medical image processing, and the method comprises the following steps: S10, data acquisition: obtaining a same-period cervical cell pathology full-slide image of a detected person which has been subjected to cervical tissue pathology analysis and a conclusion of which is evaluated as ASCUS; and S20, lesion area positioning: determining an abnormal cell area in the cervical cell pathological full-slide image by using the trained target detection model. Compared with the prior art, the method has the following beneficial effects: firstly, suspicious cells are screened out through a target detection method, and an existing method in a tissue pathology all-slide image is migrated into a cell pathology image; secondly, due to application of unsupervised learning and an attention mechanism, the model can automatically learn and emphasize the most important features for HPV detection in the image; and thirdly, the HPV infection state of the ASCUS patient is directly and accurately predicted from the cervical image.

The invention relates to the technical field of medical image processing, in particular to an AI-based cervical cell image analysis auxiliary method and system, and the method comprises the steps: carrying out the multi-modal data collection, including cervical cell image data, clinical data and patient gene data, and carrying out the preprocessing of the collected cervical cell image data; constructing a cervix uteri cell image generative adversarial network to perform image enhancement and feature extraction on the cervix uteri cell image; fusing the extracted features with clinical data and patient gene data, and training a cervical cell classification model by using the fused feature data; and inputting a real cervical cell image into the trained cervical cell classification model, and outputting whether the cervical cells are abnormal or not and an analysis result of the abnormal type and degree. According to the method, multi-modal data acquisition is combined with an image enhancement and classification method based on a deep learning technology, so that the efficiency of cervical cell image analysis is improved, and the workload of doctors is reduced.

The invention relates to a cervical TCT image cell segmentation method based on improved U-Net, and belongs to the field of image processing technology and medical technology. The problems that complex morphological characteristics of cells are not fully considered and fine cell segmentation is insufficient in cervical cancer intelligent screening research based on cervical TCT images are solved. Comprising the following steps: preprocessing a cervical cell TCT image; constructing a segmentation network based on the EGE-UNet; evaluating the cell image segmentation model based on qualitative and quantitative evaluation; and obtaining a cervical cell segmentation result based on the evaluated model. According to the method, high-quality cervical TCT cell image segmentation and fine segmentation of cervical cell nucleuses and cytoplasm can be realized by using the EGE-UNet model, and related research of subsequent cervical cell intelligent diagnosis is facilitated; based on an accurate cell segmentation result, the method can assist in extracting various cell morphological characteristics, has clinical guiding significance, and provides more interpretable quantitative data for subsequent cervical cell research.

The present disclosure relates to a method and device for identifying abnormal cell images in cervical cell images. The method comprises: inputting a multi-scale cervical cell image into a pre-trained multi-scale fusion network, wherein the multi-scale cervical cell includes different sizes of the same cervical cell image; extracting key node features of the cervical cell image of each scale in the multi-scale cervical cell image through the multi-scale fusion network; determining the grading results of abnormal cell images in the cervical cell image corresponding to the key node features of the cervical cell image of each scale through the Berthesda grading system; fusing the grading results of abnormal cell images in the cervical cell image of each scale, and outputting the abnormal cell image in the cervical cell image and the abnormal cell image with the highest level in the grading results. The present method can identify and determine abnormal cell images in cervical cell images without manual work, and can grade abnormal cell images.

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 discloses a multi-scale feature fusion cervical abnormal cell detection method, and relates to the technical field of medical image processing and deep learning. Firstly, a cervical lesion cell detection method based on multi-scale feature fusion is used, efficient capture of multi-scale features from local details to an overall structure in a cervical lesion cell image is achieved by providing a CSPM, the CSPM can dynamically extract the features among different scales, context information is fully utilized, and the detection accuracy of the cervical lesion cells is improved. The method comprises the following steps of: firstly, designing a multi-scale fusion attention module, so that the defect of single-scale characteristics in expression capability is overcome, the problems of large cell size change and complex background are effectively solved, secondly, the designed multi-scale fusion attention module further enhances the characteristic fusion capability, and the MSFA can adaptively adjust the weights of different-scale characteristics according to the importance of the cell characteristics, so that the fusion accuracy is improved. And local and global information is fully fused, so that the capability of sensing and identifying the abnormal cervical cells by the network is improved.

The present invention relates to the field of biological detection technology, and in particular to a monoclonal antibody combination and application for the detection of HPV18 type E6 protein. The provided combination consists of 5G3 and 2C7, and the amino acid sequences of the complementary determining regions of the heavy chain and light chain variable regions are clear, as shown in SEQ ID NO.1-12, respectively. The antibody combination has high specificity and sensitivity, with a minimum detection limit of up to 100 pg / ml, and does not cross-react with other HPV subtypes. The double-antibody sandwich ELISA and biotin-avidin amplification detection system constructed based on the combination significantly improves the signal intensity and detection accuracy, is suitable for the rapid detection of HPV18 type E6 protein in cervical exfoliated cell samples, and has application value in the early diagnosis of cervical cancer, risk stratification, vaccine development and efficacy evaluation.

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.

Disclosed herein is a multi-supervised deep framework that can automatically detect, segment, and classify abnormal cervical cells. The multi-supervised framework has semi-supervised, weakly-supervised and unsupervised modes which are effective in detecting cervical cells and classifying the cells as being normal or abnormal. The various modes address the situation in which labelled training data is sparse compared to unlabeled training data.

The invention relates to the technical field of biological detection, in particular to a nucleic acid sequence combination for detecting cervical cell gene methylation, a kit and application. The nucleic acid sequence combination for detecting the methylation of the cervical cell gene, provided by the invention, can specifically detect the methylation of ABCC9 and LINC00605 genes of an endometrial cancer cervical exfoliated cell sample, which are different from those of a healthy human cervical exfoliated cell sample, and the kit for detecting the methylation of the cervical cell gene, which is prepared by utilizing the nucleic acid sequence combination, can be used for detecting the methylation of the cervical cell gene. The kit is applied to diagnosis / screening of endometrial cancer, has relatively high detection sensitivity and specificity on stage I and stage II endometrial cancer, is simple to operate and beneficial to popularization and application, and noninvasive material drawing is realized.

The invention provides a target detection method and system for a cervical cell image, and relates to the technical field of target detection, and the specific scheme comprises the steps: obtaining a to-be-detected cervical cell image; inputting the cervical cell image into a trained target detection model to obtain a candidate target area of the cervical cells; wherein the target detection model is used for fusing low-level feature information and high-level information based on enhanced semantic information and position information by adopting a multi-level connected feature pyramid network on the basis of YOLOv5, and generating candidate target areas through fused features; according to the method, the extraction capability of cell characteristics and the perception capability of the model are improved, so that better detection performance is obtained.

The invention discloses a high-risk cervical cancer shunt kit based on MassARRAY methylation detection. The kit comprises an amplification primer combination, an extension primer combination, a multiple PCR (Polymerase Chain Reaction) reaction system and an extension reaction system, wherein the amplification primer combination is used for detecting methylation levels of five CpG sites of PAX1, SOX1, MIR124-2 and FAM19A4 genes. According to the kit, hydrosulfite conversion is carried out on DNA of a cervical or near-cervical cell sample, then a MassARRAY technology is combined, specific methylation detection primers are designed, amplification, extension and mass spectrometry are carried out on five CpG sites, and the methylation level is calculated according to methylation C peak HEIGHT data and unmethylated T peak HEIGHT data obtained through mass spectrometry; and finally, predicting the disease risk of the subject according to a methylation detection result. The gene methylation result provided by the invention perfects epigenetics information of gene detection, has the characteristics of large flux, high accuracy, low cost and the like, and can assist clinicians in shunting HPV positive subjects, reduce TCT leak detection and avoid excessive medical treatment.

The utility model provides a cervical exfoliated cell collector. The cervical exfoliated cell collector comprises a rubber tube, a sampling rod and a clamping structure, wherein the rubber tube is used for containing preserving fluid and is of a tube body structure with an open upper end and a closed lower end, and the upper end of the rubber tube is detachably connected with a cover body; a through hole is formed in the cover body, and a thin film is arranged in the through hole; the sampling rod is used for puncturing the film and inserting into the rubber tube, and the inserting end is connected with a brush; a blocking disc is arranged at the upper end of the sampling rod, and the clamping structure is arranged between the blocking disc and the cover body; when the moving stopping disc is connected with the cover body through a clamping structure, the moving stopping disc can rotate relative to the cover body by taking the sampling rod as a shaft. After the sampling rod is inserted into the rubber tube and the moving stopping disc is connected with the cover body through a clamping structure, the rubber tube can be pinched by a hand, and meanwhile, the sampling rod is rotated, so that a cell sample is quickly separated from the hairbrush and immersed into the preserving fluid. According to the cervical exfoliated cell collector provided by the invention, the collection efficiency of cervical exfoliated cells can be improved, and the collection effect of the cervical exfoliated cells is ensured.

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.

The present invention provides a high-grade cervical cell lesion risk prediction method. The method comprises: collecting an original test result of a patient, and performing data masking processing to obtain test result data; performing data preprocessing on the test result data to obtain original analysis data; separately inputting the original analysis data into a first analysis model, a second analysis model and a third analysis model to obtain a first prediction result, a second prediction result and a third prediction result; extracting, from the original analysis data, a feature value corresponding to a key feature type as target analysis data; and inputting the first prediction result, the second prediction result, the third prediction result and the target analysis data into a fourth prediction model to obtain a fourth prediction result. The method comprehensively evaluates the risk of high-grade cervical lesions by integrating various cervical test data, and can improve the accuracy and stability of high-grade cervical lesion prediction by using an ensemble learning method.

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 invention provides a cervical cell identification method and system. The method comprises the following steps: identifying a qualified visual field area in a pathological image to be detected; extracting contour coordinate information of each to-be-detected cervical cell in the qualified view area, and segmenting and extracting a feature vector of each to-be-detected cervical cell; and querying in a query set based on the feature vector of each to-be-detected cervical cell to obtain the category of each to-be-detected cervical cell. According to the method, the image quality of each view area in the pathological image to be detected is analyzed, so that the output reliability of the final recognition result is ensured, and prediction deviation and errors caused by low-quality data are avoided; each cervical cell in the qualified visual field area is identified, so that main targets to be detected are not missed, and the workload of labeling is reduced by learning the common morphological characteristics of the bottom layers of various cervical cells; through a query classification mode, the satisfactory identification accuracy can be achieved only through fewer abnormal cell annotations compared with a traditional method.

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.