304 results about "Radiomics" patented 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 multi-task lung cancer brain metastasis lifetime prediction method based on multi-modal data fusion, and relates to the field of medical image processing, and the method comprises the steps: obtaining non-small cell lung cancer CT image data, gene data and clinical data; performing focus segmentation on the non-small cell lung cancer CT image data; feature extraction is carried out based on the focus segmentation result, and radiomics features and depth image features are obtained; performing feature extraction on the gene data to obtain gene features; missing value deletion and numeralization preprocessing are carried out on the clinical data to obtain clinical features; local cross-modal attention fusion is carried out on the radiomics features and the depth image features; performing global cross-modal attention fusion on the preliminary mixed image features, the gene features and the clinical features to obtain original mixed features; and performing conditional guidance diffusion based on the original mixed features to obtain a lung cancer brain metastasis classification judgment result and a lifetime prediction risk. According to the invention, the lifetime prediction accuracy and stability can be improved.

The invention relates to the technical field of medical diagnosis, and discloses a DCM early noninvasive analysis method based on multi-radiomics and serum markers. Collecting image data through a multi-modal medical imaging device, and collecting serum marker data through a blood detection device; respectively generating a radiomics feature set and a serum marker time sequence feature set by using a multi-scale feature extraction algorithm and a time sequence analysis model; fusing the features by adopting a dynamic weighted fusion strategy to generate a joint feature matrix; inputting the model into a pre-trained multi-task deep learning model to predict a DCM risk probability; and finally, based on a genetic algorithm, optimizing the diagnosis decision tree and outputting an early DCM diagnosis result. The method is noninvasive and accurate, and can effectively improve the early diagnosis accuracy of DCM.

A method of automatically determining a calcium score for at least one coronary artery is disclosed. The method comprises receiving cardiac non-contrast CT data indicative of a cardiac non-contrast CT scan carried out on a patient, analysing the cardiac non-contrast CT data in a calcified components identifier to detect candidate coronary artery calcified components, and analysing cardiac non-contrast CT data associated with the candidate coronary artery calcified components using a radiomics analyser to determine radiomic characteristics of the candidate coronary artery calcified components. The method also comprises applying machine learning to the determined radiomic characteristics associated with each candidate coronary artery calcified component to identify any calcifications that are located on a coronary artery, analysing the cardiac non-contrast CT data to identify at least one body component in the cardiac non-contrast CT data not associated with a coronary artery of the patient, and using the identified at least one body component in the cardiac non-contrast CT data to remove or avoid misclassification of calcifications on a coronary artery that are located on the at least one identified body component.

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 image analysis, and discloses an HER2 positive breast cancer auxiliary prediction method based on multi-modal data fusion. According to the method, preoperative MRI, clinical pathology data and RNA-seq data meeting quality control standards are obtained through dynamic case screening, traditional image features, multi-scale Hessian matrix blood vessel-structure features and topological persistent coherent curvature features are extracted through multi-plane segmentation, and key features are obtained through three-level screening. A multi-factor logistic model fusing radiomics, clinical features and immune scoring is further constructed, a pCR probability value and a feature contribution heat map are output, and MRI data quality control, real-time analysis and interactive report generation are achieved through a matched computer system. The method breaks through the limitation of single modal analysis, dynamically associates the image features with the molecular mechanism, and provides interpretable decision support for precision medical treatment.

The invention relates to the technical field of image recognition, and discloses a carotid plaque stability and stroke risk prediction system fusing multiple modes, and the system comprises a data collection and fusion module, a feature extraction and association module, a risk assessment and layering module, an intervention decision module, and a report generation and feedback module. When carotid plaque stability and cerebral apoplexy risk prediction is carried out, plasma proteomics, radiomics and clinical data are synergistically integrated by establishing a multi-modal data fusion analysis framework, so that the limitation that a traditional method depends on a single data source is overcome; the plaque risk can be evaluated from multiple dimensions of biological activity and morphological features, the comprehensiveness and accuracy of risk prediction are improved, a more reliable diagnosis basis is provided for clinic, interaction and sensitivity influence among different modal features can be adaptively quantified by introducing dynamic feature correlation modeling and a real-time weight calibration mechanism, and the risk prediction accuracy is improved. And objectivity and consistency of risk assessment results are ensured.

The invention discloses a colorectal cancer metastasis risk prediction method and system fusing radiomics and clinical characteristics, and the method comprises the steps: S1, collecting image data and clinical risk factor data of a patient, carrying out the preprocessing, and carrying out the feature extraction; s2, performing data expansion, and fusing expanded features and original features; s3, constructing and training a multi-modal large model, and outputting a first prediction result of the colorectal cancer metastasis risk of the patient; s4, layering the patients according to the first prediction result, and performing dynamic prediction; s5, performing interpretable analysis on a dynamic prediction result and generating a report; and S6, verifying the multi-modal large model. According to the method, the aspects of data fusion, prediction performance, dynamic assessment, clinical interpretation and the like are remarkably improved, key problems in the prior art are solved, an efficient, accurate and easy-to-use transfer risk assessment tool is provided for clinicians, and the method has important academic value and wide clinical application prospects.

The invention discloses a system and a method for predicting postoperative recurrence risk after triple negative breast cancer neoadjuvant therapy based on multi-modal time sequence medical image data, and belongs to the field of medical image analysis. The system comprises a data processing module used for constructing a multi-modal data set; the multi-modal feature extraction and screening module is used for extracting deep learning, radiomics and tumor habitat features from the region and carrying out feature screening; the model training module is used for constructing a time sequence model based on a Transform architecture and carrying out training through a multi-task learning strategy integrated with time consistency constraint and gene association auxiliary loss; and the recurrence risk prediction module is used for loading the trained model and outputting a recurrence probability and a risk level. According to the method, the multi-modal time sequence image and gene information are fused, so that the recurrence risk of the triple negative breast cancer patient is dynamically and accurately quantified, and support is provided for clinical individualized treatment decision.

The invention discloses a tumor prognosis prediction method and a tumor prognosis prediction system, which are used for constructing a multi-modal fusion model based on image-pathology to improve the prognosis prediction efficiency of tumors, especially pancreatic cancer, and providing reference information for clinical decision-making. According to the technical scheme, the method comprises the following steps: S1, preprocessing an original tumor enhanced CT image, segmenting a tumor region, extracting radiomics features and depth image features of the tumor region, and establishing a CT image feature set; s2, after feature preprocessing is carried out on the tumor clinical data, clinical features with statistical significance are screened out, and a clinical feature set is established; s3, carrying out Hamp; e, preprocessing the pathological image, segmenting a tissue region, extracting spatial relation features, and generating a pathological spatial feature set; and S4, based on a feature interaction method, carrying out multi-modal fusion on the CT image features, the clinical features and the pathological spatial features, inputting a full-connection neural network, constructing a tumor survival risk prediction model, and outputting a tumor survival risk probability through the tumor survival risk prediction model.

The invention belongs to the technical field of medical image processing, and particularly relates to an esophageal cancer treatment effect and survival combined prediction method and system based on multiple modalities, and the method comprises the steps: obtaining a preoperative CT image and Hamp of an esophageal squamous cell carcinoma patient; e, the dyed digital pathological image, transcriptome data and clinical diagnosis and treatment information are preprocessed and subjected to feature extraction, and radiomics embedding representation, pathomics embedding representation and pipeline branch embedding representation are obtained respectively; the radiomics embedded representation, the pathomics embedded representation and the pipeline branch embedded representation are aligned and input into a multi-modal fusion module based on a multi-head self-attention mechanism for feature fusion, and fusion feature representation is generated; and based on the fusion feature representation, synchronously outputting a curative effect prediction result and a survival prediction result by using a multi-task output module, and evaluating model prediction performance by using a curative effect evaluation index and a survival analysis index.

The invention relates to a glioma radiotherapy effect prediction method based on habitat analysis imageomics, and belongs to the technical field of medical image artificial intelligence and tumor radiotherapy, and the method comprises the steps: collecting a multi-mode MRI image, a radiotherapy plan computed tomography CT image, a dose image and a local recurrence label before postoperative radiotherapy, and converting the images into a standard format; sequentially performing craniocerebral boning processing, N4 bias field correction, isotropic resampling, gray level normalization processing and image registration on the acquired image to obtain a preprocessed image; based on the planned target volume PTV, dividing the interior of the PTV into three types of physiological structure habitat areas and three types of physiological habitat areas; image omics features of the multi-mode MRI images are extracted in the PTV and all habitat areas, and an optimal feature subset is screened out; taking the screened optimal feature subset as an input, taking the local recurrence label as an output label, and selecting an optimal model; and generating an output result by using the optimal model.

The invention discloses a heart failure combined left beam branch conduction block classification method based on feature fusion, and the method comprises the steps: extracting radiomics features and deep learning features through a heart magnetic resonance image, carrying out the feature fusion and selection, constructing a classification model through a machine learning algorithm, and carrying out the classification of the heart failure combined left beam branch conduction block. According to the method, the advantages of deep learning in the aspect of image processing and the high efficiency of machine learning in a classification task are combined, and complex information of a heart image can be reflected more comprehensively and accurately by combining radiomics features and deep learning features; an automatic and intelligent feature extraction and classification method is utilized, a large amount of medical image data can be rapidly processed and analyzed, accurate classification of the heart failure combined left bundle branch conduction block is achieved, the method has higher classification accuracy and higher robustness, and in the field of medical image classification, the method has the advantages that the classification accuracy is improved, and the classification efficiency is improved. And the efficiency and the quality of medical services can be improved.

The invention provides a breast cancer recurrence risk prediction method, system and device based on an ultrasonic image, and relates to the field of intelligent medical treatment, the method uses a deep convolutional neural network to perform deep network feature extraction on a breast ultrasonic image, and uses a deep learning semantic segmentation algorithm to perform accurate positioning and automatic segmentation on a breast tumor region of interest, thereby improving the accuracy of breast cancer recurrence risk prediction. Meanwhile, habitat analysis is carried out on the ultrasonic images to extract tumor heterogeneity features, multi-level and multi-mode features such as deep learning features, radiomics features and habitat analysis features are fused, a breast cancer recurrence risk prediction model is constructed, breast cancer recurrence risk prediction is carried out, and a breast cancer recurrence risk assessment result is output. And a quantitative basis is provided for clinical treatment decisions. The accuracy and robustness of recurrence risk prediction are remarkably improved through multi-feature fusion, and standardization and objectification of breast cancer prognosis evaluation are achieved. In addition, the method further has the advantages of being easy and convenient to operate, low in cost, noninvasive, nonradiative, good in repeatability and the like.

The invention discloses a meniscus injury prediction method and system based on a neural network, and the method comprises the steps: obtaining a knee MRI image, carrying out the automatic segmentation of the knee MRI image based on a convolutional neural network, extracting a plurality of image features based on the segmented image, and obtaining a meniscus injury prediction result. And constructing a meniscus damage prediction model based on the SNN network model, and carrying out model training and verification. The meniscus injury diagnosis accuracy and diagnosis efficiency are improved, future injury risk prediction is achieved, valuable prediction information is provided for clinicians, prevention and treatment schemes are helped to be formulated, meanwhile, radiomics feature and load structure feature heat maps and a mixed attention mechanism are introduced, the clinical interpretability is enhanced, and the diagnosis accuracy and efficiency of meniscus injury are improved. And a full-automatic diagnosis process is realized.

The invention provides a nerve invasion prediction system and method based on a hybrid model, and electronic equipment. The method comprises the following steps: acquiring initial medical information; preprocessing the initial medical information to obtain target medical information; based on the target medical information, acquiring deep learning image features through multi-scale convolution and an attention mechanism of a deep learning model, investigating a tumor region and a peritumor region through a radiomics model, and acquiring radiomics image features; carrying out feature splicing on the radiomics image features and the deep learning image features to obtain classification model input information; and inputting the classification model input information into the classification network to obtain a category prediction result of the initial medical information. According to the feature extraction method combining deep learning and radiomics, the features of the tumor can be captured from different angles, the accuracy of nerve infringement prediction is improved, and the precision requirement of nerve infringement prediction of the prostate part is met.

The invention discloses a method for constructing a machine learning model based on cerebellar subregion multi-modal radiomics. According to the method, [18F] FDG PET metabolic features and 3DT1 W MRI structural features of a cerebellar subregion are extracted, a random forest classification model is constructed after feature selection, and high-precision identification of the Alzheimer's disease (AD) and a cognitive normal (CN) is realized in combination with SHAP analysis. According to the method, the multi-modal radiomics characteristics of the cerebellar subregion are integrated for the first time, the accuracy and interpretability of AD early diagnosis are improved, and a non-invasive and efficient diagnosis tool is provided for clinic.

The invention discloses a postoperative portal vein pressure prediction system, which relates to the technical field of portal vein pressure prediction, and is characterized in that three-dimensional structure regions of interest of the liver and the spleen are segmented based on preoperative and postoperative abdomen enhanced CT vein phase image data of a patient; image omics features including first-order statistical features, texture features and shape features are extracted from the segmented regions of interest of the three-dimensional structures of the liver and the spleen, and core feature screening is carried out on the extracted features; integrating the screened core features with clinical hemodynamic parameters and surgical parameters to construct a multi-modal prediction model of the portal vein pressure gradient; and predicting and outputting a portal vein pressure gradient predicted value by using the multi-modal prediction model of the portal vein pressure gradient, and determining a postoperative portal vein pressure gradient risk grade of the patient. The portal vein pressure gradient prediction method solves the problems of insufficient dynamic evaluation capability, multi-modal information integration and risk layering application in the prior art, and realizes non-invasive and accurate prediction of the portal vein pressure gradient.

The invention discloses a breast tumor accurate segmentation method based on a space-time fusion network, which adopts the space-time fusion network fusing convolution, LSTM and PK feature fusion to capture space and time features at the same time so as to improve segmentation precision and enhance medical interpretability, and introduces LSTM into each layer of an encoder of the space-time fusion network to capture sequence dependence, so that the segmentation precision is improved. And the model can learn the mode in the whole dynamic enhancement process. In addition, parameters of the classic PK model are fused into network input and training processes, so that the medical interpretability of the model is improved. By comprehensively utilizing a deep learning framework of DCE-MRI spatio-temporal dynamic information and radiomics characteristics, high-precision segmentation of breast tumors is realized, and medically interpretable tumor characteristics are provided, so that doctors can make more accurate diagnosis and treatment plans based on segmentation results.

The invention relates to the technical field of medical risk assessment, in particular to a thyroid cancer risk assessment system and device based on multiple modes. The system comprises a data preprocessing module used for unifying formats and scales of radiomics images, transcriptomics data and clinical medicine texts; the multi-modal feature extraction module is used for extracting image data features by adopting 3D ResNet-34 as a bottom feature extractor and combining a deformable convolution technology; performing feature selection on the exosome miRNA data through a machine learning model, and performing importance sorting on features through an SHAP method; performing feature extraction on the clinical medicine text data through a multi-granularity semantic fusion network architecture; the cross-modal dynamic fusion module is used for aligning and fusing features; and the decision and interpretability module is used for generating a diagnosis result and providing interpretability analysis. According to the technical scheme, the accuracy and efficiency of thyroid cancer risk assessment can be improved.

The invention relates to the technical field of bioinformatics analysis, in particular to an enhanced CT image-based hepatocellular carcinoma postoperative early recurrence multi-modal prediction model and a construction method thereof, and the construction method comprises the following steps: S1, data acquisition: collecting clinical data and liver enhanced CT images of a hepatocellular carcinoma surgical patient, and evaluating the recurrence condition of the HCC patient; s2, image preprocessing: sketching a region of interest in the image, and extracting deep learning features; s3, establishing a model: establishing a plurality of image omics models, constructing a plurality of deep learning models, and constructing a multi-modal model; s4, evaluating and verifying the model: evaluating the prediction accuracy of the multi-modal model by adopting an ROC curve and an AUC value, and evaluating the prognosis prediction capability of the patient by using a Kaplan-Meier curve; according to the method, the characteristic expression ability and the recurrence risk discrimination performance are improved, a scientific basis can be provided for follow-up visit and treatment of the hepatocellular carcinoma postoperative patient, and the lifetime of the patient is prolonged.

The invention relates to the technical field of machine learning, and discloses a cancer treatment effect prediction method and device, equipment and a storage medium. The method comprises the following steps: carrying out radiomics feature extraction on a CT scanning image to obtain radiomics features; performing interested pathological feature extraction based on an attention mechanism and local pathological feature extraction based on an interested pathological feature extraction result on the pathological image to obtain pathological omics features; performing differential expression gene analysis on the transcriptomics data, and performing differential expression gene screening based on a differential expression gene analysis result to obtain transcriptomics characteristics; and inputting a fusion feature obtained by fusing the radiomics feature, the pathological omics feature, the transcriptomics feature and the clinical data into a cancer treatment effect prediction model to obtain a cancer treatment effect prediction result. According to the embodiment of the invention, the cancer treatment effect is predicted by combining the radiomics characteristics, the pathomics characteristics, the transcriptomics characteristics and the clinical characteristics, and the prediction accuracy can be improved.

The invention discloses a Crohn disease focus automatic segmentation and activity evaluation system based on deep learning, which belongs to the field of medical artificial intelligence and comprises a data preprocessing unit, a focus automatic segmentation unit, a radiomics feature extraction unit, a feature screening and dimension reduction unit and an activity classification unit. According to the method, an nnU-Net deep learning segmentation model is combined with image omics feature extraction, multi-stage feature screening and machine learning classification technologies, so that full-process automation from CTE image preprocessing, focus automatic segmentation, feature extraction and screening to activity classification is realized. The system can efficiently and accurately segment the focus of Crohn's disease, automatically assesses the disease activity based on the screened key radiomics characteristics, significantly improves the consistency, objectivity and efficiency of diagnosis, and is suitable for clinical auxiliary diagnosis and scientific research analysis.

The invention relates to the technical field of computer medical detection, in particular to a thyroid nodule benign and malignant classification method based on clinical information, radiomics and gene detection. According to the method, pathological diagnosis of thyroid nodules is used as a dependent variable, detection data including radiomics characteristics and molecular omics are used as continuous variables, and the detection data of at least three genes CLDN10, HMGA2 and LANM3 are selected as the data of the molecular omics; meanwhile, in combination with part of clinical pathological characteristics including gender, age and BRAF V600E mutation condition factors, a thyroid nodule preoperative diagnosis prediction model based on radiomics and molecular omics is constructed through an SVM modeling method. Gene detection, radiomics and clinical basic information are combined, and a thyroid nodule diagnosis model based on the combination is established on the basis of Chinese population. The method is used for assisting clinicians in distinguishing benign and malignant thyroid nodules and guiding clinical decisions

The invention provides a myocardial amyloidosis classification prediction method and system based on cardiac magnetic resonance multi-sequence radiomics, and is applied to the technical field of data processing. The method comprises the following steps: preprocessing cardiac magnetic resonance multi-sequence image information to generate a standardized multi-sequence image data set; processing the standardized multi-sequence image data set to generate a target image omics feature vector; processing the gene detection information to generate pathogenic gene characteristics and mutation type characteristics; processing the target radiomics feature vectors and the cardiac magnetic resonance parameters based on medical knowledge graph data to generate an AL type myocardial feature associated feature set and an ATTR type myocardial feature associated feature set; processing the blood detection index information and the intestinal microflora parameter information to generate myocardial amyloidosis influence factors; and processing the data based on the target myocardial amyloidosis classification prediction model to generate a myocardial amyloidosis classification prediction result.

The invention relates to a CSF1R prediction method in brain glioma, a storage medium and a device, and belongs to the technical field of medical information analysis. The method comprises the following steps: preprocessing magnetic resonance image data; extracting a radiomics feature CR of the magnetic resonance image data; performing multi-angle and multi-slice 2D image analysis and processing on the magnetic resonance image data, inputting the processed data into a convolutional neural network for training, and extracting a deep learning feature DL of the last convolutional layer; fusing the radiomics feature CR and the deep learning feature DL to obtain a feature S; and inputting the feature S into a pre-trained machine learning ensemble learning strategy, performing CSF1R label prediction, and finally obtaining a CSF1R prediction result. According to the method, the end-to-end automatic process is realized, the advantages of multi-modal data, radiomics and deep learning are combined, the prediction accuracy and efficiency are remarkably improved, and powerful support is provided for diagnosis and treatment of brain glioma.

The invention discloses a bladder cancer muscular layer infiltration and prognosis prediction system based on deep learning imageomics, relates to the technical field of medical treatment and belongs to auxiliary diagnosis, and adopts an imageomics method and a deep learning model to extract imageomics features and deep learning features of CT images respectively; compared with the prior art, potential meaningful features in the CT image are extracted more comprehensively, the prediction accuracy can be further improved, meanwhile, the features are screened more strictly by using technologies such as intra-class correlation coefficient (ICC) analysis, t inspection, Pearson correlation coefficient, minimum absolute contraction and selection operator (LASSO), PCA dimension reduction and Cox regression, and verification is performed in a plurality of external medical centers. On one hand, the over-fitting risk of the model can be greatly reduced, on the other hand, the excellent generalization of the model is also proved, the use of the model is not excessively influenced by the medical environment and the CT acquisition instrument, and the error generated by the multi-center effect can be effectively solved.

An ultrasound-metabolic synergistic system for diagnosing SLN metastasis of breast cancer. A deep learning model based on grayscale ultrasound is constructed to predict SLN metastasis of breast cancer, and 9 grayscale deep features and 15 grayscale manual features associated with SLN metastasis are obtained. A matrix material based on two-dimensional material MXene / multi-walled carbon nanotubes improves the detection of small metabolic molecules in blood, and metabolomics analysis results show that this has certain potential in detecting SLN metastasis of breast cancer, while revealing metabolic pathways closely associated with metastasis, such as the glucose-alanine cycle and glycolysis, suggesting that alterations in relevant molecular pathways may underlie heterogeneous features in ultrasound radiomics. The combination of ultrasound omics and metabolomics further improves the diagnostic performance for SLN metastasis of breast cancer. Therefore, organic integration of the two serves the diagnosis of SLN metastasis of breast cancer, providing a new strategy and theoretical basis for the accurate diagnosis of SLN metastasis of breast cancer, and possesses practical clinical application values.

The invention relates to the technical field of medical image analysis, and discloses a breast cancer treatment response prediction system based on multi-time-sequence images. According to the method, preoperative MRI, clinical pathology data and RNA-seq data meeting quality control standards are obtained through dynamic case screening, traditional image features, multi-scale Hessian matrix blood vessel-structure features and topological persistent coherent curvature features are extracted through multi-plane segmentation, and key features are obtained through three-level screening. A multi-factor logistic model fusing radiomics, clinical features and immune scoring is further constructed, a pCR probability value and a feature contribution heat map are output, and MRI data quality control, real-time analysis and interactive report generation are achieved through a matched computer system. The method breaks through the limitation of single modal analysis, dynamically associates the image features with the molecular mechanism, and provides interpretable decision support for precision medical treatment.

A method for determining a recurrence of a disease in a patient includes generating a medical image of an organ of the patient and then extracting an invasive edge around an area of interest in the medical image. A plurality of radiomics features is obtained from the invasive edge and the recurrence of the disease is determined based on the plurality of radiomics features.