119 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.

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 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 relates to the technical field of image enhancement, in particular to an E7 protein cell staining screening device, method and system for cervical lesions, and the method comprises the steps: obtaining a target RGB image corresponding to a stained cervical cell sample, and carrying out connected domain extraction on a region formed by non-cytoplasm pixel points; carrying out cell nucleus shape rule characteristic analysis processing on the candidate connected domain, and screening out a target connected domain representing a cell nucleus in the non-uniformly dyed cell; according to different preset enhancement parameters, enhancing all the cytoplasm pixel points; obtaining an R cytoplasm region and a B cytoplasm region of the target connected domain under preset enhancement parameters; determining a target enhancement parameter corresponding to the target connected domain; and performing E7 protein cell staining screening based on the target enhanced image. According to the method, the self-adaptive enhancement of non-uniformly dyed cells in the target RGB image is realized, and the image enhancement effect is improved, so that the accuracy of E7 protein cell dyeing screening 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 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 discloses a cervical cell pathology classification system based on mixed multiple attention, on the basis of Faster RCNN, a Resnet101 module in a network model is improved, a mixed multiple attention pipeline module is provided, the mixed multiple attention pipeline module comprises a class enhanced attention (CEA) module branch implanted with a class channel attention (CCA) module and a regional shuffling attention (RSA) module branch, and the CCA module branch and the RSA module branch are connected with the network model. And fusing the output features of the two branches and the local representation. According to the method, the relationship between different ROI regions is obtained through three attention mechanism modules, and the ROI features are enhanced through the context of the relationship between cells, so that the sensitivity of the model to details and the accuracy of classification are enhanced, and the MMAP model is more efficient and accurate when processing complex images.

The invention discloses an end-to-end cervical cell classification integrated framework. The system mainly comprises four modules: an enhancement module for expanding and preprocessing an original data set; the fusion module is used for training an improved primary learner on the data set and fusing, outputting and generating a new data set; the purification module is used for purifying the new data set to obtain an integration result of the primary learner and retaining misclassified samples; and the relearning module is used for carrying out relearning by using a secondary learner and carrying out final classification. In addition, a self-adaptive data enhancement method is provided in the training stage of the primary learner and the secondary learner. According to the method, through stage-by-stage fine-grained optimization, the problems that existing cervical cell data sets are unbalanced in category distribution and rare in number are solved, and meanwhile, the classification precision is remarkably improved by emphatically learning easily-confused image features through an integrated framework. Experiments show that the method can effectively assist in cervical cancer screening, the workload of pathologists is greatly reduced, and the method has good application value and huge social benefits.

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 invention relates to the technical field of image recognition, and provides a multi-scale cervical cell automatic detection method and system based on a Cell-YOLOv8 model obtained after YOLOv8 improvement. The method comprises the following steps of: optimizing Bottleneck in a C2F module by adopting a CAFM to form a C2F-CAFM module, and then replacing the C2F module with the C2F-CAFM module in a feature extraction network; a CARAFE up-sampling operator is introduced into a feature fusion network, and then re-parameterization design is performed on the CARAFE up-sampling operator, so that the model can better detect and classify cervical cells, and the improved model effectively overcomes the limitations of more information loss and low feature learning efficiency when YOLOv8 processes fine cell features. While the real-time processing capability is maintained, the capability of capturing cervical cell characteristics is enhanced, so that the detection precision is improved.

The invention discloses a cervical cell detection method based on multi-scale spatial information, and relates to the technical field of medical images. According to the method, a filtering type feature selection method and a package type feature selection method are effectively combined, association among all features is fully considered, and population initialization is carried out by adopting three filtering type feature selection methods including mutual information, variance threshold and chi-square verification; the problems of high calculation cost and overfitting of a traditional single package type feature selection method are solved; a multi-objective optimization algorithm-UNSGAIII package type method is adopted, and different from other optimization algorithms, the UNSGAIII method is an algorithm suitable for multi-objective tradeoff solving problems, so that the problems that multi-scheme solving can be provided for special users, and single filtering type feature selection prediction precision is low are solved; and the convergence speed and the global search capability 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.

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.

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 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.

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.

The present invention comprises methods and devices comprising a cutting element used to contact circumferentially and longitudinally the surface walls of the endocervical canal to provide for broad and complete contact of the intended surface with the device, resulting in attainment of a sufficient volume and comprehensive tissue sample for analysis as an endocervical curettage or screening pap smear. The device may provide for a reservoir for the obtained sample to be contained when removing from the cervical canal and vagina. The device may be comprised of a detachable means or be of a material that allows the user to cut the sampling head of the device for placement in collection means to maximize the amount of tissue or cells being sent to the laboratory for analysis.

The invention provides a cervical cell image comparative learning and automatic identification method and system, and the method comprises the steps: carrying out the parallel fusion of a bimodal backbone network through the combination of unsupervised pre-training and supervised tuning, making full use of unlabeled data and labeled data, and cooperating with the careful selection of a loss function at a pre-training stage and a tuning stage, thereby achieving the automatic recognition of a cervical cell image. The training effect and generalization ability of the deep neural network model are significantly improved, efficient and accurate recognition and classification of the cervical cell image are realized, and the method has important application value and market prospect.