8944 results about "Imaging Procedures" patented technology

The invention discloses a medical diagnosis intelligent decision-making system based on multi-modal data fusion, and the system comprises the steps: obtaining a medical image, biological features and electronic medical record data of a patient through a patient information collection unit, and extracting feature vectors from different modal data through a feature processing unit; the core of the system is a modal fusion engine, and the module combines a deep neural network and a Bayesian inference engine to realize deep learning and probability-dependent modeling of an association relationship between features; carrying out weight optimization on different modal features by adopting an attention mechanism through a self-adaptive feature optimizer, and generating optimized feature representation; the intelligent diagnosis analysis unit calculates the diagnosis probability based on the optimized features, performs auxiliary diagnosis in combination with a case knowledge base when the confidence coefficient is insufficient, and outputs a diagnosis report containing diagnosis categories, basis and suggestions; according to the method, multi-source heterogeneous data are fused, deep learning and probabilistic reasoning are combined, intelligent medical auxiliary diagnosis is achieved, and diagnosis accuracy and interpretability are improved.

The invention discloses a pulmonary infectious disease prediction system based on multi-modal data fusion, and aims to improve the accuracy and reliability of disease prediction through comprehensive analysis of multi-dimensional data. The system comprises a data acquisition module, a feature extraction module, a dynamic fusion module and a prediction modeling module. The data acquisition module is used for acquiring clinical and medical images and environmental factor data of a patient in real time; the feature extraction module extracts feature vectors of all data sources; the dynamic fusion module performs adaptive weight distribution on the multi-modal features and fuses the multi-modal features into multi-modal feature representation; the prediction modeling module is used for carrying out dynamic change trend modeling on the multi-modal feature representation through time sequence analysis so as to generate a probability prediction value of suffering from pulmonary infectious diseases and a disease trend prediction result of the patient; the invention provides a comprehensive and efficient disease prediction method by utilizing the collaborative advantage of multi-modal data and combining an attention mechanism and time sequence analysis, and can be widely applied to the fields of medical diagnosis and public health management.

The invention discloses a multi-modal medical image data intelligent processing system, relates to the field of medical image analysis, and is applied to multi-modal medical image whole-process analysis of CT, MRI, PET, ultrasound and the like. According to the system, different modal image features are extracted and fused through a cross-modal manifold fusion network; a semantic guidance dynamic registration engine optimizes registration parameters to ensure that the registration error is less than or equal to 1.5 mm; the multi-task collaborative diagnosis network realizes multiple tasks such as disease classification; the clinical knowledge embedding and interpretable module generates a structured report and is in butt joint with an HIS system. Meanwhile, the model is optimized through a federated learning architecture, the adaptability of newly added data is improved by more than or equal to 20%, and intelligent processing and analysis of multi-modal medical images are realized.

The invention relates to the technical field of medical data processing, in particular to an advanced nasopharynx cancer treatment effect prediction system based on deep learning. According to the invention, a data acquisition module acquires multi-modal medical images and clinical data of a patient from a hospital information system; the data preprocessing module is used for cleaning, registering, normalizing and standardizing the data; the feature extraction module extracts image features by using a convolutional neural network and fuses the image features with clinical features to form a comprehensive feature vector; the model construction and training module constructs a deep learning model, and is adjusted and optimized through training; the treatment prediction module inputs the comprehensive feature vector to obtain a treatment effect prediction result; and the result evaluation and feedback module compares prediction and actual results, calculates indexes and collects and feeds back the improved model. The system comprehensively integrates data, accurately mines features, constructs an effective model, provides personalized prediction to assist clinical decision, continuously improves the model performance, and has important significance in improving the prediction accuracy of the treatment effect of advanced nasopharynx cancer, promoting personalized medical treatment and the like.

The invention discloses a medical image enhancement method and system based on multi-modal fusion, and the method comprises the steps: obtaining medical image data which comprises a CT image, an MRI image, a PET image and an ultrasonic image; calculating a mutual information value of each semantic tag in the cross-modal feature vocabulary, and generating an inter-modal feature correlation matrix; performing non-rigid space registration on the unified dimension image set to generate a geometrically consistent multi-modal image set; extracting skeleton region features, soft tissue region features and high metabolism region features from the multi-modal image set, and generating a core feature set; a deep learning algorithm is adopted to train the core feature set, and a segmented image set containing a segmentation mask is generated; and dynamically adjusting the fusion weight according to the regional features of the segmented image set, and generating a fusion enhanced image. According to the invention, through feature extraction, spatial registration and deep learning fusion of the multi-modal medical image, effective integration of different-modal medical image information is realized.

The invention relates to the technical field of medical image diagnosis, and discloses a medical image automatic diagnosis method and system based on deep learning. According to the method, multi-modal medical image data of a target object is acquired and standardized, a two-channel convolutional neural network is utilized to extract features, the features are processed through cross-modal feature fusion, adaptive attention weight distribution and other technologies, a cascaded two-way long-short-term memory network is adopted for modeling, abnormity is detected based on a probabilistic graph model, and the target object is identified. And the nidus is segmented by a multi-scale context information enhancement module, and finally a diagnosis suggestion is generated by a diagnosis inference engine driven by a knowledge graph. The system comprises a multi-modal image acquisition interface module, a distributed feature calculation cluster, a visual interaction terminal and a security audit module. According to the method, the accuracy and efficiency of medical image diagnosis can be improved, comprehensive diagnosis reference is provided for doctors, and meanwhile data safety and privacy are guaranteed.

The invention provides a tumor area double-view detection method and system based on image modalities and views, and relates to the field of medical image analys.The method comprises the steps that multi-modal image data are collected from the body of a patient, and image data of different view angles at the same position are obtained according to time frames; extracting multi-modal features from the image data of different modalities and different view angles, and performing weighted fusion on the extracted multi-modal features to form a fused feature map; inputting the fused feature map to train and predict the 3D-CNN network so as to identify and position a tumor region, and marking the tumor region by using a mask output by the segmentation model; calculating the pixel number of the tumor area in each layer of image according to the marked mask, and multiplying the pixel number by the volume of each pixel to obtain the volume of the tumor area; and the segmentation results of each layer of image are superposed, and a three-dimensional model of a tumor region is generated by using 3D reconstruction, so that the limitation of single-mode and single-view image data in tumor detection is overcome.

The invention relates to a multi-modal large model dynamic compression and reasoning optimization method based on a MoE architecture. The method comprises the following steps: establishing an edge computing system conforming to medical equipment specifications, constructing a medical image analysis network based on an improved hybrid expert MoE architecture, and adopting a three-layer cascade structure of a feature coding layer, a dynamic routing layer and an expert execution layer; executing expert module dynamic loading and video memory optimization; executing knowledge graph compensation and domain knowledge injection; executing hardware instruction level optimization and calculation acceleration; executing multi-expert feature fusion and decision weighting; performing diagnosis result generation and confidence evaluation; performing real-time data return and model iterative optimization; executing multi-device cooperation and load balancing; executing system security monitoring and exception handling; and generating a structured diagnostic report. The problem that the precision loss of a multi-modal large model is difficult to meet actual requirements is solved, and medical feature adaptive dynamic compression, medical hardware collaborative energy efficiency optimization and cross-modal compensation of medical knowledge enhancement are realized.

The invention relates to the technical field of image processing, and discloses an AR technology-based precise positioning method and system for minimally invasive surgery of hepatobiliary surgery. The method comprises the steps that blood vessel network data are extracted through a medical image sequence, a three-dimensional structure diagram is generated, operation target points are marked, and a coordinate system is established; collecting dynamic data for mapping matching, and establishing a real-time tracking model; constructing an augmented reality scene and implanting a navigation path; acquiring an instrument position calculation track, and comparing with a navigation map to generate an instruction; constructing a projection interface to display the key area; recording the whole process and evaluating the operation quality. By establishing the real-time tissue deformation tracking model, a multi-source data fusion accurate positioning mechanism and an intelligent operation quality evaluation system, the accuracy and reliability of operation navigation are effectively improved.

The invention relates to a medical image processing technology, and discloses a medical image segmentation and labeling method and system based on multi-modal information fusion, and the method comprises the steps: constructing a model structure of a medical image segmentation and labeling model based on multi-modal information fusion; preprocessing the data set, constructing the data set by using a random sampling method, and dividing the data set into a training set, a verification set and a test set according to a proportion; training and convergence of the model: training the model through a loss function and an optimizer until the model is converged; and S3, cutting an organ to be segmented from the original image, inputting the cut organ to the model trained in the step 3, inputting a text prompt word for semantic segmentation, and taking a mask predicted by the model as a model segmentation labeling result. According to the method, the model structure is constructed, then the medical image data set is subjected to necessary preprocessing, and then the medical image data is segmented in combination with the guiding text semantic information, so that the efficiency of segmenting and marking the medical image from 0 to 1 is effectively improved.

The invention relates to the field of image analysis, in particular to a temporal bone disease classification method and system based on a multi-modal medical image fusion technology. The method comprises the following steps: acquiring a multi-modal image of a patient, performing adaptive distortion correction, and generating a standardized image set; performing layer-by-layer anatomical structure semantic segmentation and multi-modal image fusion on the standardized image set to construct an image fusion framework; according to the image fusion framework, performing intelligent recognition on the fine structure of the temporal bone, and constructing a personalized temporal bone anatomical structure chart; performing tissue function state analysis and digital pathology dynamic simulation based on the personalized temporal bone anatomical structure chart, and constructing a digital pathology model; and performing intelligent pathological feature classification based on the digital pathological model to obtain an intelligent classification report. According to the method, rapid, efficient and accurate temporal bone disease classification is realized.

The invention discloses a whole course follow-up visit management system and method for gynaecology and obstetrics patients, and relates to the field of medical information management.The method comprises the steps that when a patient sees a doctor for the first time, basic information, medical related information, medical image data, gene detection data, sensor data and patient self-report data of the patient are input; preprocessing the input patient data, and integrating the preprocessed patient data into a personal health record; establishing a health assessment model of the patient based on the integrated personal health record, outputting a current health assessment score of the patient, and generating a follow-up visit plan according to the current health assessment score of the patient; and establishing an obstetrical complication prediction model based on the personal health record, and outputting a patient complication prediction value. Potential obstetrical complication risks are recognized in advance, especially when the health condition of the pregnant and lying-in woman changes, the prediction result can be dynamically updated, real-time monitoring and early warning of complication are achieved, and safety management of the lying-in woman is optimized.

The invention discloses a multi-modal medical image fusion diagnosis system based on artificial intelligence, and the system comprises the following steps: extracting shared features of CT and MRI images through a convolutional neural network, and mapping the shared features to the same feature space; a two-way step-by-step alignment strategy is adopted, a three-dimensional deformation field matrix is generated, and cross-modal image anatomical structure alignment is achieved; calculating modal feature weights and eliminating distribution differences through an attention mechanism and an adversarial domain adaptation layer; constructing a CT-MRI image block contrast learning task, and optimizing a shared feature encoder; a conditional generative adversarial network is used for generating a false image of a missing mode according to the semantic segmentation map, and data distribution is constrained through a Wasserstein distance; uniform feature extraction of multi-modal medical images is realized through a shared feature encoder, the cross-modal image alignment accuracy is improved in combination with a bidirectional deformation field prediction module, and the comprehensiveness and accuracy of fusion features are enhanced by using a multi-modal feature fusion module.

The invention relates to the technical field of region segmentation, in particular to a tumor medical image processing method and system of a tumor ablation treatment system. According to the method, a CT image of a disease site is primarily segmented to obtain a tissue region, an initial abnormal region is screened out, and a final abnormal region is screened out according to the difference between the shape change degrees of the initial abnormal region and an analysis region thereof and the difference between the image numbers of the CT image of the disease site where the initial abnormal region and the analysis region thereof are located. Then, mistakenly segmented areas are screened; and according to the position distribution difference of the edge pixel points of the mistakenly segmented region and the analysis region thereof, adjusting the gradient value of the edge pixel points of the mistakenly segmented region, and then performing region segmentation on the CT image of the diseased part again by using the adjusted gradient value. According to the method, the accuracy of region segmentation of the CT image of the diseased part is effectively improved.

A medical image disease diagnostic system that is included in a clinical medical diagnosis workflow. In one embodiment, a user interface is configured to assess a medical imaging application that provides for viewing, analyzing, and annotating of medical images and medical non-image data and medical training data. A processor includes a pre-trained subsystem configured to generate AI pre-trained models based on the medical image and medical non-image data, a training data generation subsystem configured to generate the medical training data, and a medical image model training subsystem configured to transform the medical image and medical non-image data into the medical training data. The medical image model training subsystem is further configured to train or generate the pre-trained AI models based on the medical training data, and a model drift subsystem is configured to determine accuracy of the pre-trained AI models. A diagnostic reporting subsystem configured to generate clinical diagnostic reports.

The invention relates to the technical field of medical treatment, in particular to an AI-based medical image automatic segmentation and labeling system, which comprises an image data acquisition module, a data preprocessing module, a feature extraction module, an automatic segmentation module and an intelligent labeling module, the data preprocessing module improves image quality through noise suppression, artifact removal and modal alignment, the feature extraction module extracts a feature map of a potential lesion area, and the automatic segmentation module combines a full convolutional network, a bidirectional long-short-term memory network and an attention mechanism to realize accurate segmentation of an organ area and the lesion area. The intelligent labeling module is used for labeling anatomical names, lesion types and pathological features of the segmentation results by using a medical knowledge graph and a semantic embedding technology; the method has the advantages of being high in segmentation precision, comprehensive in marking and high in expansibility, and the automation and intelligence level of medical image analysis can be improved.

The invention relates to the technical field of medical image processing, in particular to a medical image automatic analysis system and method, and the system comprises a time sequence dynamic monitoring module, a lesion area weighted fusion module, a boundary energy optimization module, a multi-channel filtering processing module, and a time sequence feature construction module. According to the method, time sequence tracking analysis of medical images is realized through time sequence dynamic monitoring, change salient region screening and precise lesion evolution trend positioning, lesion data at different moments are integrated by means of weighted fusion processing, the accuracy of time sequence analysis is improved, data fluctuation errors are reduced, lesion boundaries are optimized in combination with gradient analysis and gray scale comparison, and the accuracy of medical image analysis is improved. Noise interference is eliminated, lesion contours are clearer, different frequency features of images are screened through multichannel filtering, lesion differentiation information is highlighted, irrelevant interference is reduced, feature expression is enhanced, a complete lesion time sequence analysis framework is constructed, and a comprehensive image analysis basis is provided for precise medical treatment.

The invention discloses a mild traumatic brain injury image evaluation system based on deep learning, and relates to the technical field of medical imaging, and the system comprises a data source module which is used for obtaining head magnetic resonance imaging data as an input data source; the preprocessing module is used for carrying out de-noising processing and image registration on the skull magnetic resonance imaging data to realize standardized preprocessing; and the feature extraction module is used for extracting a local feature map from the multi-dimensional image data by constructing a multilayer convolutional neural network, and identifying a subcortical structure change and damage suspicious region. According to the mild traumatic brain injury image evaluation system based on deep learning provided by the invention, a standardized evaluation report automatically generated by the system not only integrates clinical information of a patient, but also provides detailed injury types, severity and follow-up visit suggestions, so that the efficiency and reliability of clinical decision making are greatly improved; and powerful support is provided for formulating an individualized diagnosis and treatment scheme.

In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution. Combinations of one or more of uncertainty, use of synthetic training data, and therapy prediction may be provided.

The invention discloses a medical image report generation method and system based on a large language model, and relates to the field of image report generation, and the method comprises the steps: firstly obtaining original image data and a clinical background text of a patient, and respectively extracting an image embedding vector and a background embedding vector; then, case retrieval based on priori knowledge is carried out by utilizing the embedded vectors, and K highly related historical case reports are screened out from massive historical data; and inputting the image embedding vector and the historical case report into an observation large language model, and outputting the image embedding vector and the historical case report in a structured JSON (JavaScript Object Notation) format. And finally, a large language model is written to integrate the visual evidence JSON, the clinical background text and the historical case report, and a final medical image report is generated. According to the mode, through a staged and multi-modal fusion mode, the problems of incoherent report logic, inaccurate information and the like are effectively solved, and the report quality and the generation efficiency are remarkably improved.

The invention relates to the technical field of medical image report error correction, in particular to a medical image report automatic quality control error correction system and method. The method comprises the steps of importing medical image data, analyzing the medical image data by using an open source tool, performing standardization processing and local contrast enhancement on an image, performing automatic segmentation of a target area through a UNet model, and calculating density features of a key area to obtain feature vectors; meanwhile, the report text data is subjected to word segmentation, standardization processing and keyword extraction steps, the word importance is evaluated by utilizing a BERT model, a semantic vector of the report is generated through a text model, finally, the consistency of the image and the report is detected by calculating the similarity between a feature vector and the semantic vector, and an automatic error correction program is started when the image and the report are inconsistent. And error types are analyzed and corrected. According to the method, medical image processing, natural language processing and deep learning technologies are combined, so that human errors are reduced, and the diagnosis efficiency and accuracy are improved.

The invention relates to an image processing method and a puncture guiding system. The method comprises the following steps: acquiring a medical image sequence image and an ultrasonic sequence image of a target object; performing registration based on the ultrasonic sequence image and the medical image sequence image to obtain a registration transformation matrix and a three-dimensional image after registration fusion; displaying a cross section image in the registered and fused three-dimensional image in a first display area, and projecting a puncture point on the puncture plate on a mapping point of the cross section image and an imaging area of a sagittal plane; responding to rotation operation of the ultrasonic probe, and updating imaging areas of the cross section image, the mapping points and the sagittal plane in the first display area in real time according to the rotation operation; when any target mapping point is located in the imaging area of the sagittal plane in the cross section image, the relative position of the target mapping point located in the imaging area of the sagittal plane in the sagittal plane image is displayed in the second display area. Therefore, the same target mapping point can be displayed in the cross section image and the sagittal plane image at the same time.

A system to provide endovascular support for patients. The system includes a processor configured to receive, from a medical imaging device, a pre-treatment vessel roadmap and medical images acquired in response to an endovascular therapy performed on a patient, and obtain endovascular treatment parameters, including image-based endovascular treatment parameters based on features of the medical images. Such endovascular treatment parameters include a geometry and location of an endovascular therapy device within a vessel of the patient. The processor is further configured to predict tissue characteristics of a first lesion based on the endovascular treatment parameters and generate a map of the first lesion based on the pre-treatment vessel roadmap and the predicted tissue characteristics of the first lesion.

Systems and method can be provided to transform input data (e.g., CT imaging data) into structured representations to create interpretable models. Another aspect of the current invention can be generating labels synthetically to apply to real data according to a biologically-based labelling technique to guide the model training with a priori mechanistic knowledge.

The invention relates to a lung focus medical image segmentation method based on graphics and text information and knowledge embedding. The method comprises the following steps: acquiring a lung medical image of a patient and a corresponding clinical diagnosis report; preprocessing the lung medical image to obtain an enhanced image; inputting the lung medical image and the clinical diagnosis report into the medical visual language model to obtain a focus prompt embedding vector; and inputting the lung medical image, the enhanced image and the focus prompt embedding vector into the medical image segmentation model to obtain a lung focus region segmentation image. By adopting the method, the lung focus can be quickly positioned by the segmentation model through the focus prompt embedding vector, the interference of a non-target area is reduced, and the segmentation accuracy and the target concentration are improved.

The invention relates to the field of artificial intelligence, in particular to a medical image computer-aided analysis method based on deep learning, and aims to solve the problems that an existing medical image analysis method is low in high-resolution image processing efficiency, insufficient in tiny focus recognition precision, weak in model generalization ability and insufficient in multi-modal image fusion. According to the method, a lightweight multi-scale feature extraction network is constructed to improve the high-resolution image processing efficiency, a fine-grained lesion recognition module is introduced to improve the detection precision of a tiny lesion, and a self-adaptive regularization strategy is adopted to enhance the model generalization ability. And a multi-modal deep fusion mechanism is designed to make full use of complementary information of different modal images. According to the invention, medical image analysis which is more efficient, more accurate, higher in generalization ability and capable of effectively fusing multi-modal information can be realized, so that clinical application of deep learning in the field of medical images is promoted.

The invention provides an acute cerebral apoplexy early-stage intelligent screening and early warning method and system based on deep learning, and relates to the technical field of medical imaging and artificial intelligence, and the method comprises the steps: obtaining a multi-temporal angiography image of the brain of a patient through a pulse-type dynamic imaging scheme, extracting hemodynamic features through a double-flow feature extraction network, and obtaining a multi-temporal angiography image of the brain of the patient; and constructing a vasculopathy risk distribution diagram by combining a vascular structure analysis model, and marking a potential embolism risk area. Furthermore, in combination with tissue perfusion parameters obtained by multispectral imaging and brain tissue function parameters obtained by electroencephalogram signals, an ischemic penumbra region is identified by using a self-adaptive threshold segmentation model. And finally, based on the ischemic penumbra area, the tissue perfusion parameters and the brain tissue function parameters, calculating a risk level by using a hierarchical decision model with a space-time attention mechanism, and generating a corresponding clinical intervention scheme. According to the invention, the early accurate screening and risk early warning of acute cerebral apoplexy can be realized, and the clinical intervention efficiency is improved.

The invention discloses an intelligent detection method and device for fusing a medical image learning image, and relates to the technical field of medical image processing. The method comprises the following steps: acquiring and preprocessing a bimodal medical image, and extracting a feature map through multi-scale decomposition; constructing a cross-modal correlation model, and setting a modal attention mechanism (embedding anatomical structure prior guidance feature complementation) and a morphological attention mechanism (setting lesion morphological constraint weight); the method comprises the following steps: collecting multiple types of image samples, pairing according to a focus form and an imaging mode to construct a bimodal joint data set, and correlating and labeling to generate a training data set with modal attributes; after a multi-stage iteration training model, inputting the preprocessed image to carry out feature fusion so as to obtain a fused image; and generating a lesion probability graph according to the fused image, positioning a lesion area through multi-threshold segmentation, and outputting a detection result. The system comprises a data acquisition module, a preprocessing module and the like. The method improves the accuracy and reliability of medical image detection, and is suitable for clinical multi-modal image analysis.

The invention relates to the technical field of intelligent recognition, in particular to an intelligent tumor recognition system and method based on big data. The system comprises a tumor big data processing module, a tumor identification model construction module, a tumor multi-source feature fusion module and a tumor classification identification mapping module, and can acquire corresponding tumor multi-modal medical image data and patient pathological clinical data through butt joint and perform image registration and filtering cleaning processing. Tumor multi-mode standard image data and patient clinical standard data are obtained; constructing a corresponding tumor recognition model; performing feature analysis and big data fusion splicing on the tumor multi-modal standard image data and the clinical standard data of the patient to obtain a multi-source fusion feature vector of the tumor of the patient; and inputting the multi-source fusion feature vector of the tumor of the patient into a constructed tumor identification model to carry out tumor classification and identification, thereby outputting a medical image tumor identification result. According to the invention, accurate recognition of tumors can be realized.

The invention discloses a neural tumor recognition system based on artificial intelligence in the technical field of medical image analysis. The system comprises a network architecture search module, a diagnosis path optimization module, a data self-supervised learning module, a continuous learning matching module, a dynamic tumor tracking module, an iterative image processing module, a result fusion feedback module and a system evaluation improvement module. According to the invention, network architecture search is carried out through the genetic algorithm and the deep Q network, the system can more accurately identify neural tumors, the diagnosis accuracy is improved, the diagnosis process is more transparent due to the application of the SHAP algorithm and the model local interpretable method, doctors can conveniently understand and judge a trust model, and use of a contrast learning algorithm and a clustering algorithm is facilitated; the method has higher efficiency and precision in the aspect of processing multi-source medical image data, and particularly provides powerful technical support for dynamic tracking of tumors through combination of an adaptive learning model and an optical flow algorithm in the aspect of construction of a heterogeneous data feature set.