658 results about "Malignancy" patented technology

The invention discloses a skin cancer pathological image analysis system and method based on a Transform model. The system comprises an image acquisition and quality control module, a multi-scale preprocessing module, a hierarchical feature extraction module, an intelligent diagnostic reasoning module, a knowledge graph aided decision-making module, a result output and feedback module and a federal learning training module. According to the method, multi-spectral image acquisition and quality control are carried out, multi-modal enhancement and dyeing standardization preprocessing are carried out, pathological features are extracted in a layered manner by using an improved Transform model, subtype identification and malignancy degree evaluation are realized in combination with multi-task learning, uncertainty is quantified by means of Monte Carlo dropout, cases and guidelines are associated through a knowledge graph, privacy is protected through federal learning, and the model is optimized. According to the scheme, the diagnosis efficiency and accuracy are greatly improved, the model interpretability is enhanced, various clinical scenes are adapted, diagnosis standardization is promoted, and improvement of basic medical capacity is assisted.

The invention discloses a thyroid ultrasound image diagnosis method based on deep learning, and the method comprises the following steps: collecting a thyroid ultrasound original image set, and carrying out the preprocessing; performing focus segmentation on the standardized thyroid ultrasound image set; performing morphological constraint and boundary refinement; calculating the blood flow direction, blood flow velocity and blood flow power of each thyroid focus area and neighborhood; generating a preliminary fusion feature map based on a feature adaptive deep learning network, and fusing the preliminary fusion feature map with the thyroid focus blood flow feature vector set; obtaining a thyroid focus detection list through thyroid focus benign and malignant discrimination branches; and generating thyroid focus structured diagnosis data records based on the thyroid focus detection list, and writing the thyroid focus structured diagnosis data records into a computer-aided diagnosis system. According to the method, deep learning and multi-modal blood flow features are fused, intelligent diagnosis of the thyroid focus is achieved, and the method has the advantages of being high in precision, high in interference resistance and structured in result.

The embodiment of the invention discloses a malignant load identification method, device and equipment, a medium and a program product, and relates to the technical field of power load monitoring. The method comprises the following steps: performing modal decomposition on an original power utilization sequential sequence to obtain a plurality of intrinsic mode components, and reconstructing intrinsic mode components which do not belong to noise components to obtain a target power utilization sequential sequence; performing feature extraction on the target power consumption time sequence to obtain target power consumption features, and inputting the target power consumption features into a pre-trained malignant load identification model for identification to obtain an identification result; the malignant load identification model is obtained by updating model parameters of a weak learner based on a natural gradient descent method and performing training optimization. The lightweight malignant load learning model obtained through training in the scheme can be deployed and operated on the intelligent electric meter, high-quality input features are obtained through multi-mode decomposition and reconstruction, the accuracy of malignant load recognition is improved, and accurate recognition of the malignant load based on the lightweight model is achieved.

The invention provides a tumor recognition method based on multi-modal feature fusion, relates to the technical field of tumor recognition, and is used for solving the problems of missing multi-modal feature calibration, insufficient semantic association, poor generalization and insufficient clinical interpretability in the prior art. The method comprises the following steps: firstly, synchronously acquiring visual structure and electrical impedance bimodal data through an event-driven sensor array, and completing preprocessing through pulse coding, STDP rule optimization and quality verification; then, respectively extracting structured feature vectors of a tumor visual structure class and characteristic feature vectors of a numerical attribute class by a heterogeneous twin coding engine, and unifying dimensions and distribution through processes such as multi-scale dynamic perception and cross-modal calibration; then based on an attention mechanism, InfoNCE contrast loss and minority class weight gain, dynamic weighted fusion and semantic association enhancement are performed on the bimodal features, and a comprehensive feature vector is generated; and finally, through clinical logic adaptation and multi-center deviation correction, outputting a tumor benign and malignant identification result through a full-connection classifier, and synchronously generating a clinical interpretable report containing key features and weights. According to the method, the complementary advantages of bimodal information are effectively integrated, the problems of heterogeneous multi-center equipment, unbalanced samples and the like are solved, the tumor recognition accuracy and the early-stage tiny tumor detection rate are improved, the computing power consumption is reduced, edge medical equipment deployment is adapted, and the requirements for low misjudgment and traceability of clinical diagnosis are met.

The invention relates to the technical field of image recognition, in particular to a urinary system CT image tumor benign and malignant identification method based on deep learning, which comprises the following steps: acquiring a urinary system CT image, constructing a map band sequence and extracting gray level distribution, identifying a heterogeneous edge and a texture mutation region, aggregating perturbation map blocks to form an abnormal structure, and identifying the benign and malignant tumors. And fusing multiple types of image layers to complete label integration, and generating a feature recognition image layer. According to the method, the extension recognition capability of the tumor edge external expansion region is enhanced by combining a graph band gray scale aggregation and sequence construction mode, the judgment precision of local heterogeneous change is improved by fusing gray scale kurtosis and migration analysis, and the texture disturbance trend is extracted based on direction vector included angle change. The block gray level fluctuation and gradient relationship supports abnormal structure aggregation identification, spatial coincidence and boundary difference combined screening realizes multi-feature region unified coverage, abnormal region expression definition and structure positioning accuracy are enhanced, and image layer consistency and identification stability are improved.

The invention relates to the technical field of biological medicine, in particular to a nano-vesicle for targeted brain glioma metabolic immune remodeling and a preparation method and application thereof. According to the nano-vesicle for targeted brain glioma metabolism immune remodeling, double-target metabolism and cascade responsive cleavage site modified biological vesicles are integrated, and effective penetration of a drug blood brain barrier and precise targeting of brain glioma can be achieved. The ROS responsive nanoparticles II are prepared by selecting BSA as a drug carrier, applying a nanoprecipitation method and utilizing SLC1A5, LDHA antagonists and responsive linkers, so that the metabolic double-target inhibitor only responds to specific ROS of tumor cells, and specific release in the tumor cells is realized. The cascade response polypeptide provides a simple and convenient delivery scheme with low invasiveness and application prospects for treatment of brain glioma, and is expected to realize precise and individualized treatment in the field of treatment of malignant glioma.

Chimeric antigen receptors targeted to IL-13Ra2 are described. The targeting domain is a IL13 variant having increased specificity for IL-13Ra2 relative to IL-13Ra1.

The invention belongs to the technical field of medicinal chemistry, and particularly relates to a 3-sulfamoyl benzamide compound as well as a preparation method and application thereof. The 3-sulfamoyl benzamide compound has a structure as shown in a formula I. In the formula I, X is chlorine or trifluoromethyl; r1 and R2 are independently C1-4 alkyl or R1, R2 and N atoms connected with R1 and R2 jointly form a heterocyclic group, and the heterocyclic group is a substituted or unsubstituted five-membered or six-membered heterocyclic group; and when the heterocyclic group is a substituted heterocyclic group, the substituent group in the substituted heterocyclic group is one or more of C1-4 alkyl, phenyl and acetyl. The 3-sulfamoyl benzamide compound disclosed by the invention has a remarkable proliferation inhibition effect on HCT116 colon cancer cells, HeLa human cervical cancer cells, MDA-MB-231 human breast cancer cells and A375 human malignant melanoma cells.

The invention discloses a pulmonary nodule malignancy probability prediction method based on radiomics and clinical features, and the method comprises the following steps: collecting and processing original lung image data, and extracting pulmonary nodule image blocks; collecting and processing clinical data of a target patient, and constructing a structured clinical feature vector; inputting the pulmonary nodule image block and the structured feature vector into a dual-channel adaptive fusion network, and extracting an image feature vector and a structured embedded feature vector; a modal sensing module judges that a modal is missing, a modal compensation module is started when the modal is missing, a pseudo-dual-channel output feature pair is generated, and otherwise, a cross attention fusion feature vector is generated; a dynamic fusion module carries out dynamic fusion on the cross attention fusion feature vector or the pseudo dual-channel feature pair, and outputs a dynamic fusion feature vector; and inputting the dynamic fusion feature vector into a classifier, and outputting a malignant probability value of the pulmonary nodule. According to the invention, a dual-channel adaptive fusion network is adopted to realize the intelligent prediction of the malignant probability of pulmonary nodules.

The invention relates to the technical field of medical images, in particular to a multi-modal medical image registration and fusion analysis method, which comprises the following steps of: eliminating image noise and artifacts based on a modal adaptive filtering strategy; constructing a pyramid type feature extraction network to realize multi-scale feature extraction, calculating feature matching degrees among different modal images, and dynamically adjusting matching weights by combining feature differences among modals; a focus area attention mask is constructed, targeted enhancement of registration image features is realized, and a hierarchical fusion strategy is adopted to evaluate the quality of a fused image; and constructing a multi-task deep learning model to complete focus automatic detection, segmentation and benign and malignant preliminary judgment on the fused image. According to the multi-modal medical image registration and fusion analysis method, a three-layer feature pyramid is constructed, a multi-feature fusion matching cost function is introduced, and cross-modal feature matching is optimized through an adaptive weight iteration nearest point ICP algorithm, so that the information richness, marginal definition and focus discrimination of a fused image reach the standard.

The invention discloses an intelligent classification and recognition method for ear-nose-throat lesion images, and relates to the technical field of image recognition, and the method comprises the steps: synchronously collecting a nasopharynx white light image and an i-Scan image, screening an image meeting a quality standard, carrying out the correlation of pathology and grading labeling, and constructing a data set containing a multi-center case; the white light image and the i-Scan image are subjected to denoising processing, key mark points are extracted, image registration is completed, a mucous membrane surface area, a submucous membrane blood vessel area and a lymphatic tissue enrichment area are divided through semantic segmentation, and feature enhancement is carried out on different areas; extracting morphological features and textural features from the enhanced image, calculating feature importance weights and fusing the feature importance weights into low-dimensional feature vectors; the benign and malignant dichotomy model and the pathological type multi-classification model are respectively constructed, and classification results are output in combination with a doctor diagnosis rule base and verification indexes, so that the accuracy and clinical practicability of ear-nose-throat lesion diagnosis are remarkably improved.

The invention discloses a pulmonary nodule benign and malignant identification and prediction system based on multi-modal feature fusion, and belongs to the technical field of data processing, and the system comprises a data collection module which is used for collecting pulmonary nodule multi-modal data; the feature fusion module is used for carrying out preprocessing and feature fusion on the pulmonary nodule multi-modal data, and generating pulmonary nodule multi-modal fusion data by integrating features of different modals; and the identification and prediction module is used for constructing a pulmonary nodule benign and malignant identification and prediction model, analyzing and identifying the pulmonary nodule multi-modal fusion data according to the pulmonary nodule benign and malignant identification and prediction model, and determining a pulmonary nodule benign and malignant identification result. According to the method, the problems that effective pulmonary nodule benign and malignant identification and prediction cannot be carried out based on multi-modal feature fusion in the prior art, and the accuracy and efficiency of pulmonary nodule benign and malignant identification are reduced are solved. Effective pulmonary nodule benign and malignant identification and prediction can be carried out based on multi-modal feature fusion, and the accuracy and efficiency of pulmonary nodule benign and malignant identification can be improved.

Provided is an intelligent grading method and system for pulmonary nodules based on multi-modal feature fusion, including: obtaining ROI and VOI of pulmonary nodules based on chest CT examination images and examination reports by utilizing clinical multi-modal data from physical examination population, designing a multi-task feature extraction network based on attention mechanism, to obtain radiomics features and deep image features from the ROI and VOI; designing a cross-modal feature fusion method based on graph representation learning, designing a multi-modal information extraction method, obtaining specific feature representations and graph structures of modalities, and then fusing the feature representations and the graph structures; and proposing an optimization and clinical verification method of pulmonary nodule grading GCN model based on self-supervised learning, to realize fine grading of pulmonary nodule malignancy with slight differences, thereby providing a new approach to design of fine-grained classification algorithms.

The invention discloses a thyroid tumor evaluation system based on combination of an ultrasonic image and pathological data, and relates to the technical field of auxiliary decision making. Comprising the steps that an image acquisition module preprocesses a thyroid ultrasound image; the pathology acquisition module is used for extracting structured data containing pathology malignancy scores and molecular marker expression parameters; the feature extraction module generates feature vectors from the images and the pathological data respectively; the region screening module dynamically generates an image region sensitivity threshold value based on the molecular marker and the pathological malignant score; a weighted extraction module recognizes an image suspicious region and generates weighted features according to the image suspicious region; the multi-modal fusion module fuses bimodal features through a weight distribution rule; and the evaluation decision module outputs benign and malignant prediction results and confidence scores. Dynamic sensitivity regulation and control of deep coupling of pathological semantics and image features are realized, and image suspicious region identification precision and cross-modal decision reliability precision are improved.

The invention discloses a breast cancer classification method based on multi-modal data hierarchical fusion, which belongs to the field of medical information processing, and comprises the following steps: firstly, learning clinical semantic information by using a one-dimensional convolutional neural network, and extracting clinical semantic features; secondly, extracting B-ultrasonic and Doppler image features through a semantic enhancement residual network, performing hierarchical fusion on the clinical semantic features and the B-ultrasonic and Doppler image features in the image feature extraction process to obtain preliminarily fused image features, and thirdly, splicing the preliminarily fused image features and the clinical semantic features to obtain a final image feature; sending the data to a linear random feature attention module for cross-modal interaction fusion; and finally, performing full-connection network prediction on a cross-modal interaction fusion result to generate a classification result. According to the method, the application of the multi-modal data hierarchical fusion model in the ultrasonic image and clinical semantic features is realized, the accuracy and robustness of benign and malignant breast cancer classification can be improved, and the method has clinical popularization and application values.

The invention relates to the technical field of novel biological nanomaterials, in particular to a lung-targeted exosome complex as well as a preparation method and application thereof. The lung targeting type exosome complex is prepared from an NK cell exosome, lipid nanoparticles and an entrapped anti-tumor nucleic acid drug, the NK cell exosome is obtained by extracting a natural killer cell NK-92MI of a human malignant non-Hodgkin lymphoma patient through a differential centrifugation method. The anti-tumor nucleic acid drug is a specific nucleic acid drug targeting a key canceration driving gene in non-small cell lung cancer. The lung targeting type exosome complex prepared by the invention is an excellent and stable drug delivery carrier, has a good lung tissue targeting function, enhances the tumor targeting effect of the exosome, also exerts the tumor cell killing function of the exosome, and has important significance for the treatment of non-small cell lung cancer.

Chimeric antigen receptors for use in treating malignant melanoma and other cancers expressing CS1 are described.

The invention relates to a method and apparatus for high resolution total body photography (TBP). The inventive method uses a multi-stage keypoints focused pipeline that begins with blob detection to rapidly and coarsely localize potential lesions within high-resolution TBP images. Once these regions of interest are identified, a deep learning classifier evaluates them for malignancy risk. Acknowledging that wide-field imaging can compromise classification precision due to variability in resolution and appearance, the process is further refined by integrating ugly duckling analysis and t-SNE clustering. The ugly duckling detection process groups suspicious regions across all images, effectively highlighting clusters of high-risk candidates for further clinical review utilizing the method of the invention.

The invention relates to a lung elastography and AI-assisted pulmonary nodule benign and malignant identification system and a medium, and solves the problem of limited identification accuracy of a traditional iconography examination method. The system comprises at least one processor which is configured to realize the lung elastography and AI-assisted pulmonary nodule benign and malignant identification method when executing a computer program, and the specific steps are as follows: inputting obtained comprehensive consistency indexes, individual characteristics of patients and key information in multi-source data into a pre-trained correlation model; outputting the prediction probability that the case pulmonary nodule is malignant; based on the prediction probability and the initial confidence coefficient, adopting a preset confidence coefficient adjustment algorithm to calculate the adjusted confidence coefficient; and displaying the pulmonary nodule benign and malignant judgment result, the confidence coefficient, the multi-physical field simulation result and the comprehensive consistency index on a display terminal in a multi-modal mode. The method has the advantages that the accuracy of identifying benign and malignant pulmonary nodules is improved, and the limitation of a traditional method is made up.

The invention relates to a kidney lump benign and malignant analysis method and device based on a laparoscopic ultrasound image, and belongs to the technical field of medical image processing.The method comprises the steps that radiomics characteristics of the laparoscopic ultrasound image are obtained, and radiomics scores of lump malignant risks are calculated according to the radiomics characteristics; determining an independent risk factor corresponding to the patient clinical variable, and constructing a clinical prediction model according to a mapping relationship between the patient clinical variable and the independent risk factor; and according to the radiomics score and the clinical prediction model, constructing a kidney lump benign and malignant analysis model. The technical problem that in the prior art, massive quantitative image features cannot be efficiently and accurately mined from medical images, and the most valuable iconography features cannot be screened out for analyzing clinical information is solved.

The invention discloses a construction method of a malignant pleural effusion prediction model, a marker combination and application, equipment and a medium. The construction method comprises the following steps: S1, obtaining characteristics of a biomarker combination; the biomarker combination is composed of three biomarkers of NGAL, CEA and CA50; s2, based on the characteristics of the biomarker combination, adopting Logistic regression analysis to respectively construct regression models corresponding to the three biomarkers, and respectively outputting parameters alpha NGAL, beta NGAL, alpha CEA, beta CEA, alpha CA50 and beta CA50; and S3, based on the output parameters in the step S2, adopting Bayesian analysis to construct a malignant pleural effusion prediction model. The method can be used for predicting the MPE probability in the pleural effusion patient, the identification and prediction accuracy is high, and compared with pleural biopsy and thoracoscopic sampling biopsy, the method has the advantages of being rapid, minimally invasive and the like.

The invention discloses a medical image recognition method and system based on artificial intelligence, and the method comprises the steps: obtaining CT image data, pathological section data and PET metabolic activity data, introducing a GNN graph neural network to model the semantic association between multi-modal features, and outputting a fusion feature vector; inputting the fusion feature vector into a DSRN double-flow space-time recursive network, extracting 3D lesion morphological features of a single image by using a spatial flow, analyzing lesion growth kinetic parameters of a historical image sequence through a time flow, and generating a lesion malignancy probability index based on a gating fusion unit; based on the focus malignancy probability index, Monte Carlo Dropout sampling is utilized to generate a confidence interval, a clinically interpretable credibility score is output, and when the confidence is smaller than a threshold value, a low-confidence area is displayed for a doctor to check. The deep fusion of multi-source information is realized, the one-sidedness of a single mode is avoided, and the recognition accuracy and efficiency are improved.

Disclosed are methods of treating cancer in a subject comprising (i) administering a pharmaceutical composition comprising a therapeutically effective amount of a mutagenized IL 13 moiety (mIL 13); and then (ii) delivering a radiation therapy.

This disclosure provides: (i) a method of using an anticeramide antibody or an antigen-binding fragment thereof to improve hematopoietic stem cell transplantation for the treatment of various non-malignant and malignant diseases; and (ii) a composition for such treatment.

Aspects of the present disclosure provide methods for determining the eligibility of a subject having a malignancy for treatment with an anti-PD therapeutic agent based on a Combined Positive Score (CPS) for a tumor tissue sample from the subject. Compositions and kits or performing the disclosed methods are also provided.

The invention discloses an application of NF-kappa B and IRF4 in prognosis evaluation of diffuse large B-cell lymphoma, relates to the technical field of disease prognosis and molecular biology, provides a marker for prognosis evaluation of diffuse large B-cell lymphoma, and relates to the application of NF-kappa B and IRF4 in prognosis evaluation of diffuse large B-cell lymphoma. The invention relates to application of a substance for detecting the marker in claim 1 in preparation of a product for prognosis evaluation of diffuse large B-cell lymphoma, and provides a medicine for treating diffuse large B-cell lymphoma. By detecting the marker, the malignancy degree of the DLBCL can be more accurately analyzed, a reliable prognosis evaluation basis is provided for clinicians, and accurate prognosis evaluation is carried out; through targeting NF-kappa B and MUM1 dual-blocking therapeutic drugs, the prognosis of patients is improved, the life quality and the survival rate of the patients are improved, and novel therapeutic drugs are developed; a new breakthrough is brought to diagnosis, treatment and prognosis evaluation of the DLBCL, and potential clinical application value is achieved.

The presently disclosed subject matter provides methods and kits for treating solid tumors in a subject by using a combination of anti-CTLA-4 and / or anti-PD-1 antibodies with at least one member of the group consisting of a bacterium, bacterial product, and an immunoregulatory entity. In particular embodiments, the bacteria are toxin-depleted, anaerobic bacteria, such as Clostridium novyi-NT.

The invention discloses application of Bombyx mori neuropeptide BommoOKAtype4 in preparation of a medicine for inhibiting melanoma cell growth, and belongs to the technical field of medical biology. Aiming at the technical problems that melanoma is high in malignancy degree, easy to transfer and limited in treatment means, and whether the bombyx mori neuropeptide BommoOKAtype4 can directly inhibit the growth of melanoma cells is not clear yet when the bombyx mori neuropeptide BommoOKAtype4 is mainly used for skin whitening before, the invention provides the application of the bombyx mori neuropeptide in preparing the medicine for inhibiting the melanoma cells. According to the silkworm neuropeptide, gene expression and protein functions of melanin synthesis key enzymes TYR, TRP1 and TRP2 are comprehensively inhibited by down-regulating expression of a core transcription factor MITF, so that melanoma cell proliferation is directly inhibited while melanin synthesis is inhibited, and therefore, the silkworm neuropeptide can be used for preparing drugs for treating melanoma and has a wide application prospect. A novel peptide candidate substance is provided for treatment of melanoma.

The present invention provides ROR1 specific chimeric antigen receptors (CAR) and their therapeutic use. The CAR comprises a signal peptide, a ROR1 antigen binding domain, a hinge, a transmembrane domain, a co-stimulatory domain and an intracellular signaling domain. The modified immune cells endowed with such CARs are suitable for treating malignancies such as cancer, chronic lymphocyte leukemia (CLL), and acute lymphocytic leukemia (ALL).

The disclosure provides methods of treating a malignancy comprising administering an effective dose of a chimeric antigen receptor genetically modified T cell immunotherapy and methods for manufacturing such immunotherapy. Some aspects of the disclosure relate to methods of determining objective response of a patient to a T cell immunotherapy based on the levels of attributes prior to and after administration of the immunotherapy to the patient.