1752 results about "Malignancy" patented technology

An intelligent pulmonary nodule grading method and system based on multi-modality feature fusion. The method comprises: using clinical multi-modality data of a physical examination crowd to acquire an ROI and a VOI of a pulmonary nodule on the basis of a chest CT examination image and an examination report, designing a multi-task feature extraction network based on an attention mechanism, and acquiring radiomics features and deep-layer image features from the ROI and the VOI; designing a cross-modality feature fusion method based on graph representation learning, designing a multi-modality information extraction method, acquiring a specific feature representation and graph structure of each modality, and then performing feature representation and graph structure fusion; and providing a pulmonary nodule grading GCN model optimization and clinical verification method based on self-supervised learning, so as to implement fine grading of pulmonary nodule malignancy having only slight differences. A new way is provided for the design of a fine-grained classification algorithm; and in the aspect of solving a clinical problem, provided is a classification algorithm optimization method and clinical verification scheme in which generative self-supervised learning and comparative self-supervised learning are organically combined.

The invention provides an esophageal squamous carcinoma detection method and system based on combination of multi-modal information fusion and artificial intelligence, and relates to the technical field of detection. And inputting the fusion feature map corresponding to the multi-modal image information into a pre-trained esophageal squamous carcinoma detection model to perform focus area detection and segmentation on the fusion feature map, and based on a detection result, performing benign and malignant classification and staging prediction on a detected focus by using an integrated learning method in combination with a classification algorithm to obtain the esophageal squamous carcinoma. Outputting a classification result and a visual heat map; converting the extracted focus area information into three-dimensional point cloud data by using an improved point cloud reconstruction algorithm; performing space division and noise reduction processing on the three-dimensional point cloud data by adopting a self-adaptive octree structure; and generating a smooth lesion three-dimensional model by using an improved Poisson surface reconstruction algorithm in combination with a curved surface subdivision technology.

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 benign and malignant nodule grading evaluation system based on large model fusion ultrasonic imaging and thyroid gene markers, which can organically fuse non-invasive examination and serological detection, can simulate and diagnose multi-grade risk probability information provided by a gold standard, realizes similar risk grading estimation in a non-invasive mode, and has a wide application prospect. The thyroid nodule risk assessment method can provide visual explanation conforming to clinical logic based on comprehensive information of iconography and molecular biology, can significantly improve the accuracy of thyroid nodule risk assessment, can also effectively improve clinical decision-making efficiency and patient credibility, and has important clinical application prospects. The system comprises a data acquisition module, an ultrasonic image feature extraction module, a gene marker feature extraction module, a multi-modal fusion and hierarchical reasoning module and a generation module.

The invention relates to the technical field of sound wave measurement, in particular to an ultrasonic image diagnosis system and method.According to the ultrasonic image diagnosis system and method, matching between reflection characteristics and tissue characteristics is made to have the self-adaptive learning ability through a deep neural network, the targeted recognition effect is improved in the aspect of signal classification, and based on the judgment result of the reflection characteristics, the diagnosis accuracy is improved. When boundary partitioning is carried out on a tissue area, the edge structure is judged by using the combination of three parameters of gray range, gradient direction and texture continuity, contour fuzziness caused by single index judgment is avoided, and the boundary partitioning accuracy is improved by extracting a gray distribution center, an amplitude change track and an edge area continuous change sequence. And the region positioning result is input into a support vector machine, accurate recognition of lesion properties is realized according to boundary classification comparison of morphological structure quantitative features and historical benign and malignant feature data, secondary verification of the structural form is performed after the image is formed, and the diagnosis integrity and the judgment confidence are effectively improved.

Disclosed is a system for detecting whether the breast cancer is benign or malignant based on multiple modalities, which comprises: an ultrasonomics model configured to extract breast tumor position information and breast tumor morphological features on the basis of an acquired grayscale breast ultrasound image, perform global interactive fusion on the breast tumor position information and breast tumor morphological feature information, and convert a Transformer encoder-based feature output into an output vector of the corresponding classification dimensions by means of a fully connected layer; a metabonomics model configured to obtain a metabolic fingerprint spectrogram and analyze the metabolic fingerprint spectrogram on the basis of a metabolic database and a predetermined significantly differential metabolite to obtain abnormal metabolic pathways, signal transduction pathways, and related biochemical reactions associated with breast cancer malignancy; and a detection module configured to perform joint modeling, based on the breast tumor position information, the breast tumor morphological features, and a peak value of the significantly differential metabolite of the breast cancer malignancy, to output a prediction result about whether the breast cancer is benign or malignant.

The invention discloses a multi-mode breast ultrasonic diagnosis system based on a dynamic feature fusion network, belongs to the field of medical image processing, and particularly relates to the multi-mode breast ultrasonic diagnosis system based on the dynamic feature fusion network. The invention aims to solve the problem that the existing method is low in breast ultrasonic recognition accuracy. The system comprises a multi-modal data preprocessing main module used for preprocessing a collected multi-modal mammary gland ultrasound image to obtain a preprocessed multi-modal mammary gland ultrasound image; the multi-modal convolutional neural network main module is used for building a dynamic feature fusion network model; the model training main module is used for training an established dynamic feature fusion network model by using the preprocessed multi-modal breast ultrasound image to obtain a trained dynamic feature fusion network model; and the model test main module is used for loading the trained dynamic feature fusion network model, classifying a to-be-diagnosed multi-modal breast ultrasound image, and identifying benign and malignant breast tumors in the multi-modal ultrasound image.

The invention relates to the technical field of medical image processing, in particular to a multimodal-based digestive tract tumor lesion image segmentation method and segmentation system. The method comprises the following steps: acquiring an alimentary canal tumor lesion image, and extracting an alimentary canal tumor ultrasonic image; evaluating the tumor invasion depth based on the digestive tract tumor ultrasonic image; performing focus three-dimensional visual modeling according to the tumor invasion depth to obtain a digestive tract tumor focus model; extracting a tumor tissue pathological image according to the digestive tract tumor lesion image; performing tumor region segmentation based on the tumor tissue pathological image to obtain digestive tract tumor region data; performing tissue arrangement anomaly detection based on the digestive tract tumor region data to obtain tissue arrangement anomaly data; and calculating a tissue arrangement disorder index according to the tissue arrangement abnormal data and the digestive tract tumor area data. According to the invention, the tumor identification accuracy and the malignant region segmentation precision are improved based on the medical image processing technology.

The invention relates to the technical field of tumor risk assessment, and discloses a multi-modal deep learning-based ovarian adnexal tumor risk grading system, which comprises a multi-modal data acquisition module for acquiring ultrasonic image data, CA125 serum concentration data and gold standard data of an ovarian adnexal tumor patient; the ultrasonic image segmentation module is used for carrying out automatic positioning and structured segmentation on a focus area of the ultrasonic image by adopting self-adaptive bidirectional displacement; the multi-modal feature extraction module is used for extracting an O-RADS vector and a CEUS vector according to the focus area mask of the segmented image, and carrying out numerical value normalization processing on the CA125 serum concentration data to obtain a standardized feature vector; the multi-modal feature fusion module carries out feature fusion to obtain OCC-US vector representation; and the risk grading decision module is used for classifying the OCC-US vector representation and outputting an OCC-US score and a benign and malignant risk grade. According to the invention, the diagnosis precision and reliability of the ovarian adnexal tumor are improved.

The invention discloses a multi-omics pathological analysis system and method for colorectal cancer liver metastasis risk prediction, and relates to the technical field of biomedicine and cancer diagnosis, the system comprises a data acquisition module, a data preprocessing module, a cell analysis module, a score construction module, a model construction and training module and a prediction and evaluation module; according to the method, multiple omics data including the single cell data set, the space transcriptome data, the batch RNA-seq data, the clinical data and the full slice image WSIs are integrated, comprehensive and accurate prediction of the colorectal cancer liver metastasis risk is achieved, cross-dimensional data fusion and analysis improve the prediction accuracy and reliability, and the risk of the colorectal cancer liver metastasis is reduced. The invention also provides a powerful tool for deeply exploring the molecular mechanism of tumorigenesis, development and metastasis, and particularly in a cell analysis module, cell subtypes and molecular characteristics closely related to liver metastasis can be disclosed by identifying and analyzing malignant cell subgroups LMTMCs triggering liver metastasis, so that the liver metastasis can be discovered. And a scientific basis is provided for formulating a personalized treatment scheme.

The invention discloses a thyroid tumor diagnosis method and system based on ultrasonic and cytological image conjoint analysis. The method comprises the following steps: converting a thyroid B ultrasonic image of a patient to be diagnosed into an image feature vector FUS; performing structured feature extraction on the thyroid cytological image of the patient to be diagnosed to obtain a structured feature vector FCYTO; the image feature vector FUS and the structured feature vector FCYTO are converted into a fusion Token sequence; and inputting the Token into a multi-modal feature fusion and prediction network based on a Transform architecture, and carrying out feature fusion and classification prediction so as to obtain the probability that the thyroid tumor of the patient to be diagnosed is malignant. The thyroid tumor diagnosis based on multi-modal fusion is carried out on the basis of ultrasonic and cytological images, so that the diagnosis accuracy is improved.

The invention discloses an ovarian tumor cross malignancy classification system based on a hybrid radiomics model. On one hand, a backbone network in the Med3D network is used as a depth feature extractor to construct a target network, and multi-modal 3D magnetic resonance image data is used to carry out fine tuning training on the target network to obtain a corresponding ovarian tumor classification network for obtaining depth features and tumor classification results. On the other hand, omics characteristics of the tumor region of interest are extracted, and a tumor classification result corresponding to the omics prediction model is obtained. In addition, the depth features and the omics features are fused, and a corresponding classification result is obtained through a KNN classification algorithm. And finally, obtaining a final ovarian tumor cross malignancy classification result according to weighted average of the three classification results. According to the invention, by integrating various models and algorithms, more comprehensive feature mining and analysis are carried out on the multi-modal MRI image data of the ovarian tumor patient, so that the final ovarian tumor classification effect is improved.

The invention relates to the technical field of medical artificial intelligence, in particular to a pulmonary nodule malignant risk dynamic prediction method and system based on space-time attention and multi-modal data guide fusion. Clinical features are obtained based on clinical data, and a lung CT slice image is obtained based on a lung CT image; the method comprises the following steps: extracting general medical visual features in an image by using an open-source medical large model, extracting image features in a lung CT slice image through a visual Transform model, and performing feature guide optimization on the image features based on clinical features and the general medical visual features to obtain patient image specific features; performing joint mapping on the specific features of the patient image by using a space-time attention mechanism to obtain space-time enhancement features of the patient image; and performing weighted fusion on the clinical features, the general medical visual features and the space-time enhancement features of the patient by using a gating mechanism, and obtaining the risk prediction probability of the malignant pulmonary nodules of the patient by using a classifier. The method can predict the malignant probability of the pulmonary nodule, and facilitates the realization of auxiliary diagnosis such as pulmonary nodule screening.

In accordance with at least one aspect of this disclosure, there is provided an imaging agent, a including dye compound conjugated to an antigen specific targeting vector which can be particularly advantageous because their behavior in vivo can contribute to superior optical imaging properties, for example, by significantly increasing the target-to-background ratio of imaged tissues, leading to higher resolution imaging, and ultimately providing for better recognition of malignant tissue for resection and margin assessment and improved visualization during minimal invasive laparoscopic surgery, for example. A method of imaging tumor cells in a subject, can include, administering an imaging effective amount of an imaging agent according at least one embodiment of the invention; 2-4 hours after administration of the imaging agent, irradiating a region in the subject in which tumor cells are expected to be found with at a wavelength absorbed by the imaging agent; and detecting a signal from the imaging agent, thereby imaging the tumor cells, wherein the target-to-background ratio 2-4 hours after administration is from about 2.75 to about 15.

The invention discloses a breast tumor benign and malignant identification system based on a double-branch attention distillation network, belongs to the field of medical image processing, and particularly relates to the breast tumor benign and malignant identification system based on the double-branch attention distillation network. The invention aims to solve the problem that the existing method based on deep learning is insufficient in feature extraction capability and cannot accurately focus a lesion area. The system comprises an ultrasonic image preprocessing main module used for preprocessing collected breast ultrasonic images, obtaining ultrasonic imaging areas, unifying the image size of the obtained ultrasonic imaging areas, obtaining preprocessed breast ultrasonic images, and taking the preprocessed breast ultrasonic images as a training set; the double-branch convolutional neural network main module is used for building a double-branch attention distillation network model; the model training main module is used for obtaining a trained double-branch attention distillation network model; and the model test main module is used for loading the trained double-branch attention distillation network model and classifying breast ultrasound images to be diagnosed.

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 relates to the technical field of medical image processing, in particular to a lung tumor CT image 3D segmentation method and system based on multi-modal image fusion. The method comprises the following steps: acquiring a lung tumor CT image; determining a first texture feature based on the lung tumor CT image; identifying a lung tumor boundary by using the first texture feature; detecting a nodule protrusion area in the boundary of the lung tumor; obtaining tissue infiltration data from the nodule protrusion area; determining a second texture feature according to the tissue infiltration data; determining a tumor heterogeneity feature according to the first texture feature and the second texture feature; evaluating the potential malignancy degree by utilizing tumor heterogeneity characteristics; and dividing a tumor risk area of the lung tumor CT image based on the potential malignancy degree. According to the invention, accurate heterogeneity identification and risk region division of the lung tumor CT image are realized based on a medical image processing technology, and the accuracy of lung tumor 3D segmentation is improved.

The invention discloses a lysosome targeted chimera based on Sortilin and application thereof. The lysosome targeted chimera comprises a ligand molecule of a lysosome targeted receptor and a ligand molecule of a targeted protein to be degraded, wherein the ligand molecule and the ligand molecule are connected through a linker; the lysosome targeting receptor is Sortilin, and the ligand molecule of the lysosome targeting receptor is a polypeptide targeting the Sortilin; the protein to be degraded is membrane protein or extracellular protein. Furthermore, the polypeptide of the targeted Sortilin is connected with an enzyme response sequence and an integrin receptor targeting sequence. According to the invention, the Sortilin-combined ligand neurotensin is functionally coupled with the neutralizing antibody or polypeptide of overexpression protein in targeted tumor or inflammatory diseases, and the Sortilin-combined ligand neurotensin has obvious treatment gain in malignant solid tumor and psoriasis models. The discovery promotes the transformation potential of the lysosome participating in the biological agent, and provides a dual strategy for realizing accurate degradation through receptor recruitment and microenvironment induction.

The embodiment of the invention provides a Grignson grading method and device for a prostate cancer pathological image, equipment and a medium. The method comprises the following steps: acquiring a prostate cancer pathological image which is a full-view digital slice image; dividing a non-blank area of the prostatic cancer pathological image into a plurality of non-overlapping examples; converting the instance into a high-dimensional feature vector; the high-dimensional feature vector is input to a pre-trained multi-branch hybrid supervised network model, slice-level representation is obtained, the multi-branch hybrid supervised network model comprises an attention backbone network, instance-level branches and slice-level branches, the slice-level branches comprise slice-level benign branches and slice-level malignant branches, and slice-level representation is obtained; the slice-level malignant branch comprises an instance compression module and a component extraction module; and inputting the slice level representation into a classifier to obtain a Grignson classification result of the prostatic cancer pathological image. Based on this, the embodiment of the invention can improve the accuracy of Gleason grading of the prostatic cancer pathological image.

Disclosed are approaches to non-invasively characterize a tumor or other lesion in a region of interest (ROI) based on various analyses of magnetic resonance imaging (MRI) data. The MRI data may correspond to ultrafast dynamic contrast enhanced MRI (DCE-MRI) and high spatial resolution DCE-MRI scans, and diffusion-weighted MRI (DW-MRI) scans of the ROI. Vasculature metrics may be determined, and tumor-associated blood flow velocity and / or tumor interstitial pressure may be obtained using the vasculature metrics as inputs to a computational fluid dynamics model. A combination of morphological vascular metrics and functional vascular metrics may be used to characterize the tumor. Malignancy, aggressiveness, treatment response, and other features of tumors or other lesions, in the breast or other regions of a patient, may be characterized through disclosed analyses of MRI data.

The invention discloses a thyroid cancer pathological image classification method and system based on multi-modal feature fusion and Bayesian optimization, and relates to the technical field of medical image processing, and the method comprises the steps: collecting a thyroid ultrasound image data set, and carrying out the preprocessing operation; extracting an improved local binary pattern feature, a Haralick texture feature and a VGG16 depth feature, and constructing a mixed feature space; and splicing the features in the mixed feature space into a 4119-dimensional mixed feature vector, and carrying out feature importance screening by utilizing ExtraTres. Improved local binary pattern features, Haralick texture features and VGG16 depth features are fused, a mixed feature space is constructed, feature importance screening and PCA dimension reduction are performed by using ExtraTrees, multi-level features of the image are effectively extracted, the accuracy of benign and malignant thyroid nodule classification is remarkably improved, meanwhile, network hyper-parameters are dynamically adjusted through a Bayesian optimization algorithm, and the classification accuracy of benign and malignant thyroid nodules is improved. And model convergence is accelerated in combination with a cosine annealing strategy, so that the generalization ability of the model is enhanced, and the model can be excellently expressed on different data sets.

An improved method for MRI-guided sonosensitized focused ultrasound treatment of malignant tissue is disclosed herein.

The invention provides a minimally invasive method for determining whether pulmonary nodules are malignant or not by measuring methylation levels of different gene combinations such as CDO1, PTGER4, HOXA9, SHOX2, SP9 and ZNF781. The disclosed methods also enable the detection of lung cancer. The invention also discloses a polynucleotide and a kit which can be used for measuring the methylation level of CDO1, PTGER4, HOXA9, SHOX2, SP9 and ZNF781.

This disclosure provides novel bispecific antibodies that specifically bind to CD47 and Programmed Death-Ligand 1 (PD-L1). The disclosure further relates to methods of making the bispecific antibodies and nucleic acids encoding the antibodies. The disclosure further relates to therapeutic methods for use of the bispecific antibodies in the treatment of a condition associated with malignant cells expressing CD47 and / or PD-L1.

Provided herein are novel anti-CD20 x anti-CD28 antibodies and methods of using such antibodies for the treatment of B-cell malignancies. Subject anti-CD20 x anti-CD28 antibodies are capable of agonistically binding to CD28 costimulatory molecules on T cells and CD20 on tumor cells. Thus, such antibodies enhance anti-tumor activity at tumor sites. The subject antibodies provided herein are particularly useful in combination with other anti-cancer therapies (e.g., anti-CD3 x anti-CD20 x anti-CD79b antibodies) for the treatment of B-cell malignancies.

The invention relates to Chinese pulsatilla root and stem exosome-like vesicles as well as a preparation method and application thereof. The preparation method comprises the following steps: juicing Chinese pulsatilla roots, filtering, and collecting filtrate; and centrifuging the filtrate, collecting the precipitate, re-suspending, and filtering to obtain a solution containing the exosome-like vesicles of the Chinese pulsatilla roots. Compared with the prior art, the Chinese pulsatilla root and stem exosome-like vesicles extracted by the method can inhibit malignant progression, including proliferation and migration, of lung cancer, and cell uptake and animal living imaging prove that the Chinese pulsatilla root and stem exosome-like vesicles can be taken by cells and can be enriched in tumor tissues at the same time.

The invention discloses a pulmonary nodule risk prediction method, system and device based on a large model, and a medium, belongs to the technical field of artificial intelligence, and aims to solve the technical problems of insufficient generalization ability of a network model and poor pulmonary nodule multi-task prediction effect in the prior art. The method comprises the following steps: acquiring sample data of multiple modalities including medical images, gene detection and demographic data; single-mode features are obtained through an exclusive feature extraction module, and then the single-mode features are input into a plurality of expert networks to obtain single-mode experts; obtaining a multi-modal feature after the single-modal feature passes through a cross-modal attention module, and inputting the multi-modal feature into a plurality of expert networks to obtain a plurality of multi-modal experts; the three task MoE modules are all provided with task gating networks in a matched mode, and the task MoE modules select the most appropriate expert set according to the calculated probability that each expert is activated; acquiring to-be-predicted data, inputting the to-be-predicted data into the selected expert set, and outputting malignant, growth and wettability risk prediction results of the pulmonary nodules by the task model.

Disclosed herein is a clear cell renal cell carcinoma (ccRCC) biomarker set. Also disclosed herein is a detection system using the biomarker set disclosed herein, methods of determining whether a subject has or shows recurrence of clear cell renal cell carcinoma, method of determining whether a renal mass sample is benign or malignant, method of detecting response of a subject to systemic treatment, and a kit for carrying out the same.

The invention discloses a method and a system for visually identifying hemodynamic spatial-temporal heterogeneity in breast tumors, and belongs to the technical field of medical image acquisition and auxiliary detection. The method for visually recognizing the hemodynamic spatial-temporal heterogeneity in the breast tumor comprises the steps that S1, DCE-MRI images of breast benign and malignant tumors are obtained, and region-of-interest sketching is carried out; s2, carrying out TIC automatic classification and coding on each voxel in the tumor by adopting 19 types of TIC curves to obtain a TIC color coding graph; s3, calculating the TIC type and proportion of each focus, and extracting a TIC color coding map to reflect radiomics characteristics of tumor internal heterogeneity; s4, screening the image omics characteristics of the TIC color coding image, and establishing three models including 19 types of TIC curve fusion models; according to the method, the difference of hemodynamics time and space heterogeneity in the breast tumor of the patient can be visually judged and compared, and benign and malignant and biological behaviors of the tumor of the patient are judged.

The invention belongs to the technical field of benign and malignant diagnosis model systems for solid pulmonary nodules, and particularly relates to a benign and malignant diagnosis deep learning model system for solid pulmonary nodules. Comprising an overall architecture system of the model, a data set introduction system, a data set preprocessing system, a lung CT image segmentation system, a lung nodule detection system, a lung nodule category system, an experiment parameter setting system, an evaluation index system and an experiment setting and result system. A lower half branch of the network is mainly designed, the lower half branch network extracts multi-scale feature information of a lung CT image through an Inception module, and convolutional attention modules such as ECA and CBAM are introduced to enhance the extraction effect of the lower half branch network on position and shape features of the lung CT image.