80 results about "Subtype classification" patented technology

The invention relates to the technical field of pathological image analysis and mining, and particularly discloses an ovarian cancer subtype classification method based on prototype learning and multi-view deep embedding clustering, and the method comprises the following steps: S1, collecting a tissue pathological image of an ovarian cancer patient and a corresponding full-view digital pathological image; and S2, generating a multi-view data set. According to the ovarian cancer subtype classification method based on prototype learning and multi-view deep embedding clustering, the problem that in the prior art, patch-level labels are generally lacked in the field of multi-instance pathological images, so that many natural image processing methods cannot be applied to the field of pathological images is solved. A ResNet backbone network is used for extracting features of pathological images under the maximum magnification, a small number of pathology prototypes are introduced to guide deep embedded clustering through pathology expert priori knowledge, a pathology image spectrogram is introduced to serve as a reference view, and the accuracy and stability of clustering are enhanced.

The invention provides a breast cancer molecular subtype recognition and prediction method and system based on an ultrasonic image, and the method comprises the steps: carrying out the feature fusion preprocessing of a breast ultrasonic image based on acoustic impedance difference, and obtaining a standardized ultrasonic image; dividing the image into fan-shaped regions based on the radial fiber structure of the mammary gland, and dynamically adjusting the window size according to tissue characteristics to carry out adaptive window segmentation to obtain a window set carrying anatomical structure position information; inputting the window set into a pre-trained cross window Transform model for feature extraction, and analyzing a feature association mode between the windows through a cross window attention mechanism to obtain a multi-dimensional ultrasonic feature vector; and inputting the feature vector into a classifier for molecular subtype classification prediction to obtain a breast cancer molecular subtype identification prediction result. According to the method, through adaptive window segmentation, breast anatomical structure information is utilized, local and global feature association is captured through a cross window attention mechanism, and the prediction accuracy is improved.

Provided herein is a consensus molecular subtype (CMS) classifier for colorectal cancer patients. Also provided are methods of using the classifier to identify a clinically beneficial therapeutic regime for each patient as well as methods of treating a patient accordingly Custom Nanostring code sets, which work on formalin-fixed, paraffin-embedded samples, are provide for use in determining the CMS for a colorectal cancer patient.

The invention relates to the technical field of bioinformatics, in particular to a method for typing acute respiratory distress syndrome biological subtypes. The method comprises the following steps: a) acquiring multi-omics data and carrying out standardized preprocessing; the multi-omics data comprises transcriptomics data, proteomics data and metabonomics data of a biological sample source; b) constructing a similarity network of each group by using a similarity fusion network (SNF), and obtaining a uniform sample similarity matrix through multi-group network fusion and iteration; multiple collaborative principal component analysis (MCIA) is adopted to carry out dimension reduction on multi-omics data so as to realize visualization of a clustering result; carrying out multi-omics joint discrimination modeling under the guidance of SNF clustering by using a data integration analysis (DIABLO) method so as to identify key feature variables; and carrying out biological subtype classification based on the clustering result of the steps.

The invention discloses a depression recurrence risk intelligent early warning method, system and device based on multi-modal data and artificial intelligence, and relates to the field of depression classification early warning, and the method comprises the steps: carrying out the individualized deviation calculation based on the baseline features and multi-modal brain image features of a target patient, obtaining an individual multi-modal brain image deviation feature vector; dimension reduction processing is carried out on the individualized multi-mode brain image deviation feature vector, the genetic features and the environment and clinical features, the individualized multi-mode brain image deviation feature vector, the genetic features and the environment and clinical features are input into a pre-trained layered integrated classification model and a pre-trained layered integrated risk early warning model, and depression subtype classification tags and risk probabilities are obtained; in the pre-training process of the hierarchical integration classification model and the hierarchical integration risk early warning model, multi-modal feature fusion and hierarchical integration learning strategies are adopted, and samples from a plurality of data centers are used for model training and verification. According to the method, the accuracy and individualization degree of classification and early warning are improved, and the generalization ability and robustness of the model are also improved.

The invention belongs to the technical field of histopathological image auxiliary diagnosis, and particularly relates to a breast cancer molecular typing method and system based on a histopathological image, and the method comprises the steps: obtaining a digital pathological image, carrying out the feature extraction of the digital pathological image, and obtaining the feature embedded representation of the digital pathological image; and inputting the feature embedded representation into a deep learning-based typing model to obtain the probability of each molecular subtype. In a deep learning-based typing model, a long-range dependency relationship is captured through an attention mechanism, a local space structure is extracted by a convolutional neural network module, and effective global and local fusion is further realized through an interaction mechanism. According to the method, the dynamic weighting of the attention mechanism is combined with the residual learning of the convolutional neural network module, so that the modeling capability of the model for a complex tissue mode is effectively improved, and the method has relatively high efficiency in processing high-dimensional spatial heterogeneity data, thereby providing relatively high efficiency and accuracy in triple negative breast cancer molecular subtype classification.

The invention relates to the technical field of image data processing, in particular to an immune cell image data classification and recognition system which comprises a space-time registration module, a dynamic trajectory analysis module, a membrane table deformation detection module and a subtype classification decision module. According to the method, coordinate system alignment is achieved through affine transformation, registration precision is improved in combination with a gray variance threshold screening mechanism, a three-frame sliding window calculates a gray gravity center displacement vector, cosine similarity verifies directional convergence, track continuity capture capability is enhanced, noise interference pseudo tracks are filtered, and the method is suitable for large-scale popularization and application. A curvature-area coupling model quantifies membrane boundary gradient resistance, three-point extreme value detection locates a deformation area, edge detection geometric characterization limitation is broken through, a support vector machine analyzes a track consistency coefficient, dynamic threshold segmentation quantifies resistance mutation, a multi-dimensional nonlinear discrimination boundary is constructed, subtype discrimination specificity and sensitivity are improved, and the method has the advantages of being high in accuracy and high in reliability. And performing spatio-temporal registration, track verification, mechanical analysis and feature fusion progressive processing to realize behavior feature full-dimensional analysis.

The invention discloses an early prediction model construction method for senile sarcopenia and metabolic high-risk phenotypes. The method comprises the steps that 3D scanning data, body composition data and health phenotype data of senile patients are acquired, and data preprocessing is carried out; respectively extracting feature vectors of the three types of data after data preprocessing, and performing alignment and weighted fusion to obtain fusion vectors; inputting the fusion vector into a subtype classification model for training to obtain a preliminary prediction model; in the training process of the subtype classification model, an unsupervised learning method and a supervised learning method are adopted at the same time, and a staged freezing strategy and a five-fold cross validation method are adopted; the preliminary prediction model is verified and finely adjusted by using a verification data set prepared in advance, so that a final early prediction model of senile sarcopenia and metabolic high-risk phenotypes is obtained, and accurate diagnosis of sarcopenia and intelligent identification of metabolic high-risk subtypes can be realized through the constructed prediction model.

Described herein are gene features that provide prognosis, diagnosis, treatment and molecular subtype classification of cancer by genomic and epigenomic profiling, including immune checkpoint regulators such as Programmed Death Ligand 1 (PDL-1). Using the methods and compositions described herein, specific and sensitive detection of biomarkers of interest is provided. Such biomarkers indicate disease pathogenesis, which provides opportunities for selection of treatments, including treatment regimens intended to overcome tolerance mechanisms.

The invention provides a cancer subtype classification method, system, equipment and program product based on ferroptosis related miRNA, and belongs to the field of intelligent medical treatment. The breast cancer is divided into four subtypes on the basis of ferroptosis related miRNA, the breast cancer FAP + subtype is identified on the basis of ferroptosis activity, the molecular characteristics of the FAP + subtype of an individual breast cancer patient are measured by establishing FAPscore, and the higher the FAPscore is, the more remarkable the FAP + subtype characteristics of the breast cancer patient are. The invention also finds that FAPscore can be used for predicting response, prognosis and drug sensitivity of individual cancer patients to immunotherapy response, and the discovery has wide cross-cancer species applicability and is not limited to breast cancer. The invention provides a new insight for a more effective and individualized treatment strategy of cancer.

The application discloses a dynamic prototype multi-model medical image classification method based on feature credibility evaluation, and relates to the technical field of medical image processing.The method effectively solves the problems in traditional medical image subtype classification, such as lack of high-quality labeled data, high uncertainty of pseudo-labels, and difficulty of static prototypes in adapting to dynamic changes of lesions, and the like.Through construction of a multi-structure feature extraction network and completion of hierarchical feature fusion, the method combines feature cross learning, local attention modeling and expanded convolution to supplement context information, and strengthens semantic consistency and structural continuity of lesion region features.Meanwhile, the method constructs a spatial similarity graph through cosine similarity, and models and fuses a spatial uncertainty graph with any and cognitive uncertainty based on a Dirichlet distribution, obtains a reliable evidence graph through exponential fusion, and generates a pseudo-label with sample-level confidence, so that effective supervision information is accurately screened from a feature level, cumulative deviation of false pseudo-labels is greatly reduced, and stability of a semi-supervised learning process is improved.

The invention discloses a disease subtype classification method, a disease subtype classification device, electronic equipment and a computer program product. The method comprises the following steps: acquiring at least two types of omics data of a plurality of patient samples with a target disease; respectively constructing a similarity matrix under each type of omics data, wherein the similarity matrix is used for describing the feature similarity of any two patient samples under the corresponding omics data; fusing the similarity matrixes under various omics data to obtain a fused similarity matrix; based on the fusion similarity matrix, multi-stage clustering analysis is carried out, a typing result for the target disease is obtained, and the multi-stage clustering analysis comprises at least two of the following clustering processing modes: spectral clustering processing, unsupervised clustering processing and consensus clustering processing. The scheme of the invention can help to improve the stability and resolution of typing.

This platform is a privacy-preserving, federated learning system designed to integrate whole-slide digital pathology images with matched molecular profiling and clinical metadata for improved diagnosis, subtyping, and prognostic estimation of gynecologic tumors. The architecture comprises local institutional nodes that retain raw patient data while participating in distributed model training coordinated by a central orchestration server. Each local node preprocesses whole-slide images into patch-level tensors, extracts visual embeddings via convolutional backbones, and processes molecular vectors (e.g., somatic mutations, expression summaries, copy-number measures) via a molecular encoder. A multimodal fusion module - implemented as an attention- based transformer - integrates image and molecular embeddings into a unified representation used by multi-task heads for classification (histologic subtype, diagnostic label) and regression (risk score). The federated learning controller aggregates encrypted model updates (FedAvg) and returns improved global weights without exchanging raw data, enabling cross-site generalization while preserving patient privacy. Explainability components generate attention maps and tile-level saliency (Grad-CAM style) linked to molecular features, providing interpretable morpho- molecular correlations to pathologists. The platform supports API integration with PACS / LIMS, conforms to privacy standards via optional differential privacy and secure aggregation layers, and is extensible to additional omics modalities or transfer / fine-tuning workflows for related tumor types. By combining multimodal fusion, federated training, and clinician-facing interpretability, the system accelerates robust, generalizable AI for precision pathology in gynecologic oncology.

The application relates to the field of biomedical technology, and discloses a diagnostic agent for identifying melanoma molecular subtype typing and application, wherein the diagnostic agent comprises a first antibody specifically combined with a SOX10 protein and a second antibody specifically combined with an EGR1 protein; the diagnostic agent can be applied to preparation of a diagnostic product for evaluating the prognostic effect of a melanoma patient, preparation of a diagnostic product for predicting the treatment sensitivity of melanoma to a BRAF inhibitor, and preparation of a diagnostic product for guiding an individualized treatment scheme of melanoma. The diagnostic agent for melanoma molecular subtype typing can be directly transformed into clinical practice, and can assist in realizing real individualized treatment.

The invention relates to the technical field of medical image processing, and discloses a chronic obstructive pulmonary emphysema distribution quantitative method and system based on multi-scale segmentation, and the method comprises the steps: carrying out the anisotropic diffusion filtering noise reduction of a chest CT image; segmenting a lung field and removing a blood vessel bronchial structure by adopting a region growing algorithm; multi-scale image representation is constructed based on a Gaussian pyramid, an emphysema candidate area is identified in a coarse scale layer, and a boundary is accurately drawn by adopting a self-adaptive threshold value in a fine scale layer; extracting local texture features to distinguish the lobular central emphysema and the total lobular emphysema; dividing severity levels according to spatial aggregation characteristics and density gradient distribution, and calculating an air swelling volume ratio and a distribution heterogeneity index; the three-dimensional pseudo-color volume is used for drawing visualization, a structured quantitative report is generated, accurate segmentation and subtype classification of the emphysema area are achieved, and comprehensive quantitative analysis indexes are provided.

To provide a method for identifying a therapy recommendation for a subject exhibiting dysregulated host response.SOLUTION: A classification of the subject of subtype A, subtype B, or subtype C is obtained. The therapy recommendation for the subject is identified based at least in part on the classification. Responsive to the classification of the subject comprising the subtype A, the therapy recommendation can be no immunosuppressive therapy. Responsive to the classification of the subject comprising the subtype B, the therapy recommendation can be no therapy recommendation, immune stimulation therapy, suppression of immune regulation therapy, blocking of immune suppression therapy, blocking of complement activity therapy, and / or anti-inflammatory therapy. Responsive to the classification of the subject comprising the subtype C, the therapy recommendation can be no therapy recommendation, immune stimulation therapy, suppression of immune regulation therapy, blocking of immune suppression therapy, modulators of coagulation therapy, and / or modulators of vascular permeability therapy.SELECTED DRAWING: Figure 1A

The present application relates to a kind of melancholic depression plasma biomarker kit detection method, for detecting the plasma biomarker of melancholic depression.The present application provides a kind of application method by detecting plasma adiponectin (Adiponectin) protein as the application method of melancholic depression biomarker, the present application method is used for the subtype classification diagnosis of melancholic depression for non-diagnostic purposes, improve the sensitivity and specificity of melancholic depression screening.

The application provides a training method of a cancer histological subtype classification model and a storage medium. The method comprises the following steps: identifying a tumor image region in a HE-stained slice image, and dividing the tumor image region into a plurality of non-overlapping sub-slice images; inputting each sub-slice image corresponding to each HE-stained slice image in a sample set into a feature extraction network to obtain a basic feature vector of the sub-slice image; inputting the basic feature vectors of all sub-slice images of the HE-stained slice image into a connected attention network and a hybrid density network to construct an enhanced feature vector of the HE-stained slice image; inputting the enhanced feature vector of the HE-stained slice image into a classifier to output a cancer histological subtype prediction result of the HE-stained slice image; and adjusting hyperparameters to generate a trained cancer histological subtype classification model.

The invention discloses a full-slice image cancer prediction and subtype classification method, system and device, and relates to the technical field of image processing and medical artificial intelligence. Comprising the following steps: preprocessing a full-slice image, cutting the full-slice image into image blocks with position coordinates, and extracting features; reconstructing the feature sequence into a two-dimensional feature map which retains the original spatial topology through a spatial recovery module; scanning and fusing along eight directions including a horizontal direction, a vertical direction and a plurality of diagonal lines by using a hyper-cross scanning module so as to capture multi-direction local space correlation; multi-scale global features are extracted and fused by adopting convolution layers with different expansion rates through a pyramid module; and finally, outputting a prediction result and a subtype label through a customized classifier, and generating a focus attention heat map. Through the architecture of spatial reconstruction-multidirectional scanning-multi-scale fusion, while the linear calculation complexity of O (n) is kept, the small focus recognition capability and classification precision are remarkably improved, and an efficient and reliable technical scheme is provided for digital pathological diagnosis.

The invention relates to an AD early intervention-oriented multi-modal image omics feature and multi-strategy algorithm collaborative MCI subtype classification system. The system comprises a graph acquisition unit, a multi-modal feature extraction unit, a multi-strategy feature selection unit, a hybrid machine learning classification unit and a model integration and evaluation unit. According to the method, a systematic solution of MCI subtype classification is realized by establishing a complete technical chain from multi-modal feature extraction, multi-strategy feature selection to hybrid algorithm optimization. The system not only overcomes the problem of single feature extraction in traditional research, but also remarkably improves the accuracy and generalization ability of a classification model through multi-strategy feature selection and a hybrid machine learning framework. Especially, a cascading strategy combining traditional machine learning and deep learning is adopted, so that the interpretability of the model is ensured, the feature characterization capability is enhanced, reliable technical support is provided for early diagnosis and intervention of AD, and meanwhile, the stability and clinical applicability of a classification result are improved.

Described herein are gene features for providing prognosis, diagnosis, treatment and molecular subtype classification of cancer by genomic and epigenomic profiling, methods and compositions for determining cancer and subtypes, including breast cancer, and provide specific and sensitive detection of biomarkers of interest. Such biomarkers indicate disease pathogenesis, which provides opportunities to select treatment, including treatment regimens intended to identify responsive candidates and overcome resistance mechanisms.

The invention provides a bladder cancer subtype classification system and molecular typing method based on machine learning, and the molecular typing method comprises the steps: firstly obtaining transcriptome data and survival information of bladder cancer tissue of a patient, extracting data from a preset amino acid metabolism related gene set, and constructing a gene expression matrix; then, clustering the patients by adopting an unsupervised clustering algorithm, and determining at least two types of amino acid metabolism molecule subtypes in combination with a stability index; thirdly, carrying out survival difference analysis on different subtypes, screening out differential expression genes related to survival outcomes, constructing a survival prediction model based on the differential expression genes, and calculating amino acid metabolism scores of the patients; finally, the patients are grouped according to the scores, and molecular typing based on the amino acid metabolism characteristics is completed. According to the invention, stable and accurate typing of the bladder cancer patient is realized.

The embodiment of the invention discloses a learning disorder recognition method and device and electronic equipment, and the method comprises the steps: outputting an interaction interface, and obtaining the learning difficulty degree of a to-be-recognized child in daily life damage, score damage and learning disorder subtype in the interaction interface, and the identity information of the to-be-recognized child; after calculating scores of all the dimensions, inputting the scores and the identity information into a pre-trained target recognition model to obtain an initial judgment result about whether learning obstacles exist or not; when the initial judgment is positive, target user information is judged through subtype truncation values, and classification results of all subtypes are obtained; the target recognition model can be a logistic regression model, a random forest model, a support vector machine or an extreme gradient lifting model. By implementing the application, multi-dimensional information can be automatically collected through the electronic scale, obstacles can be quickly and accurately screened through the machine learning model, subtypes are refined, the evaluation efficiency and objectivity are improved, and the method is suitable for large-scale early intervention of schools and medical institutions.

The invention discloses a cancer typing intelligent decision-making platform fusing genomics and radiomics, and relates to the technical field of medical data analysis. The multi-modal data acquisition module is used for acquiring genome data and medical image data of a patient in parallel; the intelligent preprocessing module carries out variation annotation on the gene data and extracts quantitative features from the image; a gene-image cross-modal association network is constructed through a similar network fusion technology, the problem of data splitting is solved, and non-invasive dynamic monitoring of tumor molecule evolution is achieved; a dynamic constraint optimization strategy is adopted to solve the defects of a traditional fusion method, and the cross-modal correlation precision is improved by 24.7% while the feature dimension is reduced by 80%; in combination with a hierarchical decision model and interpretability analysis, not only is a subtype classification result output, but also a treatment sensitivity quantitative prediction and decision basis visualization report is generated, so that the decision confidence of a clinician is improved by 40%, and the application bottleneck of the prior art is comprehensively broken through.

The application discloses a breast cancer subtype classification method and system based on a graph convolutional neural network, which converts breast cancer gene expression data into a graphical representation and captures the correlation between genes using a graph convolution module. The data is preprocessed, including removing duplicate samples and samples without subtype labels, and filling in missing values. A graph representation dataset of breast cancer gene expression is constructed, combined with biological prior knowledge. The local features of the nodes in the graph are captured using the graph convolution method, and the data is normalized using the batch normalization algorithm. The self-attention pooling mechanism is introduced to learn the contribution of the input data to the output data, and the key features are extracted and hierarchical pooled. The local features and hierarchical features are spliced into the classification model to obtain the classification result of the breast cancer subtype. The application can effectively capture the correlation between genes and improve the accuracy of breast cancer subtype classification, and has potential biomedical application value.

The invention discloses a group of diagnosis, typing and prognostic markers for children Langerhans cell histocytosis, and belongs to the technical field of disease diagnosis. In the invention, an overall metabolic profile of bone marrow aspirates (BMA) of 34 children LCH patients is analyzed, and the patients are divided into three subtypes: single system diseases (SS), multi-system diseases (MS-RO-) without dangerous organ tiredness and multi-system diseases (MS-RO +) with dangerous organ tiredness. The metabonomics characteristics of children diagnosed as LCH are described, the metabolic characteristics of a multi-system LCH (MS-RO +) group with dangerous organs being tired before and after treatment are comprehensively researched, and important metabolic markers for molecular diagnosis, subtype classification, treatment evaluation and prognosis are provided for children patients suffering from LCH, especially high-risk LCH.

The invention belongs to the technical field of machine learning auxiliary diagnosis, and particularly relates to a meningitis rapid diagnosis system based on the combination of an SERS technology and a machine learning method. The system comprises: an input module, which is used for inputting SERS spectral data of cerebrospinal fluid; the preprocessing module is used for preprocessing the SERS spectrum data; and the prediction module is used for calculating the preprocessed SERS spectral data through a machine learning model to obtain a meningitis classification prediction result. According to the method and the system, the SERS technology and the machine learning method are combined for the first time, the method and the system for rapidly and automatically diagnosing the meningitis are realized, and the method and the system have the advantages of simplicity, rapidness, accuracy, capability of carrying out subtype classification and the like, and have a good application prospect in clinical diagnosis of the meningitis, particularly in an early screening process.

The invention provides a recognition method of an endometriosis biomarker based on machine learning and WGCNA (White Graphical Cell Nucleic Acid). The method comprises the following steps: constructing a lactic acid related gene diagnosis model of endometriosis; an analysis process of a patient type of the endometriosis and an immune-related function of the endometriosis is constructed; and constructing a lactic acid related gene regulation and control network of endometriosis and a lactic acid related gene targeting small molecule compound network. On the basis of endometriosis biomarkers, consensus clustering analysis and immune cell and immune function analysis are adopted, subtype classification of endometriosis is provided, and an immune target treatment strategy is provided for endometriosis patients of different subtypes.

This system provides a consensus molecular subtype classification system for esophageal squamous cell carcinoma based on histopathological images. [Solution] In a molecular subtype classification system that achieves high-precision molecular subtype classification for ESCC and provides important evidence for personalized therapy, molecular subtypes are classified into four types. As for the characteristics of each type, ECMS1 involves metabolic pathway abnormalities, NFE2L2 activation, and the selection of drugs to be used as NFE2L2 inhibitors. ECMS2 involves upmodulation of the classical signaling pathway of the tumor and low methylation. ECMS3 has few copy number change events, low tumor mutation burden, high PD-1 expression, and is beneficial for immunosuppressant treatment. ECMS4 involves activation of the epithelial-mesenchymal transition pathway. The system utilizes a spatial algorithm to extract spatial tissue features from the results of automatic contour extraction of histological images and constructs a machine learning model that performs subtype classification for ESCC based on these features.

The invention relates to the technical field of biomedicine, and discloses a diagnostic agent for identifying melanoma molecular subtype typing and application, the diagnostic agent comprises a first antibody specifically binding to SOX10 protein and a second antibody specifically binding to EGR1 protein, the diagnostic agent can be applied to preparation of a diagnostic product for evaluating the prognosis effect of a melanoma patient, preparation of a diagnostic product for predicting the treatment sensitivity of melanoma to a BRAF inhibitor, and preparation of a diagnostic product for guiding an individualized treatment scheme of melanoma. The diagnostic agent for melanoma molecular subtype typing can be directly converted into clinical practice, and real individualized treatment can be achieved in an auxiliary mode.