87 results about "Imaging report" patented technology

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 provides a medical image report generation method and related equipment. The method comprises the following steps: acquiring medical image data; and inputting the medical image data into a pre-trained medical image report generation model, and outputting a medical image report corresponding to the medical image data by the medical image report generation model. The accuracy of the generated medical image report can be improved.

The invention relates to the technical field of medical health management, and discloses an endocrine patient health management system based on big data. According to the system, a multi-source data acquisition module is adopted to acquire heterogeneous health data including physiological monitoring indexes, medication record data, medical image reports and the like, and living habit logs can also be included. The physiological feature noise reduction module is used for extracting steady-state physiological features, the multi-modal fusion module is used for generating a focus dynamic map, and the medication feedback analysis module is used for generating a drug response mode evolution sequence. The data are input into a multi-dimensional decision model, and a health management scheme is output by a personalized scheme generation module. And the living habit analysis module analyzes the living habit logs to generate influence factors, and participates in making a health management scheme. The system realizes multi-source data fusion analysis, can provide a personalized health management scheme for an endocrine patient, improves the disease diagnosis accuracy and treatment effect, and improves the life quality of the patient.

The invention belongs to the technical field of medical image intelligent diagnosis, and provides a breast cancer focus benign and malignant discrimination method based on a gated multi-expert mechanism. The method comprises the following steps: firstly, carrying out standardization and semantic preprocessing on a mammary gland X-ray image, a BI-RADS imaging report and structured clinical data, embedding age, mammary gland density and focus position information into a text template in a natural language form, and realizing unified expression of multi-modal input; secondly, extracting image features by utilizing a ResNet network and a simplified CLIP model, obtaining a text semantic vector by adopting a Bio-ClinicalBERT model, and establishing two sub-paths of a lump expert and a calcification expert in a Transform structure; further, an expert weight is dynamically generated through a gating routing mechanism, and soft routing fusion is executed; and finally, outputting benign and malignant results of the breast cancer focus by the binary classification module. According to the method, deep fusion and dynamic collaboration of the mammary gland X-ray image, the BI-RADS text and the clinical information are realized, and the accuracy and interpretability of breast cancer discrimination can be remarkably improved.

The invention provides a medical image report text generation model training method and device, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring a medical image marked with an organ type according to a medical image sequence, and acquiring a report text marked with the organ type according to a medical image report; and training a combined model constructed by an image encoder and a text generation model according to the medical image marked with the organ type and the report text marked with the organ type to obtain a medical image report text generation model. The device executes the method. According to the medical image report text generation model training method and device provided by the embodiment of the invention, text representation is performed based on all medical images of different organs of a human body, and the medical image report is automatically generated according to the medical image report text generation model, so that subsequent medical image report data analysis can be comprehensively and accurately assisted.

The invention provides a text completion processing method and device for a medical image report, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring a medical image and a report text thereof; checking each report text based on a text generation model of a preset medical image report generation model, and if it is determined that the report text is missing, generating text completion task prompt information according to the missing content; and performing feature extraction on each medical image based on the image encoder to obtain an image feature vector, obtaining an existing report text corresponding to the image feature vector based on the text generation model, and complementing the missing report text according to the existing report text and the text complementing task prompt information to obtain a medical image report. The device executes the method. According to the method and the device provided by the embodiment of the invention, the complete and high-quality medical image report can be efficiently and timely generated.

The invention discloses an MRI medical image report automatic generation method and device, and belongs to the technical field of medical image intelligent analysis. The MRI medical image report automatic generation method comprises the following steps: acquiring an MRI image; inputting the MRI image into a target multi-modal large model, and obtaining a target image report output by the target multi-modal large model; wherein the target multi-modal large model is obtained by training based on the following steps: training to obtain an image encoder based on a sample image and a sample text report corresponding to the sample image; based on the trained projection layer, mapping a vector space of an image encoder to a vector space of a large language model to obtain an initial multi-modal large model; and adjusting the initial multi-modal large model to obtain a target multi-modal large model. According to the MRI medical image report automatic generation method, the semantic coherence of the generated image report is improved, the clinical practice requirement can be fully met, and the clinical diagnosis efficiency and accuracy are improved.

The invention relates to the technical field of nursing data management, and particularly discloses a nursing data management optimization method and system for a brain tumor patient, which is used for performing structured conversion and FHIR format standardization processing based on a multi-dimensional rule on vital sign data and electronic medical record data of the brain tumor patient. Therefore, the consistency of data formats is ensured. For medical image data of a patient, a large model technology is particularly introduced to carry out automatic structured description on a medical image, through image feature extraction and semantic segmentation based on deep learning, morphological features and a spatial distribution mode of a focus are identified, a structured image description text is generated, and FHIR format conversion is further completed. Finally, unified access, storage, query and analysis of vital sign data, electronic medical record records and medical image reports are achieved by deploying an integrated server meeting the FHIR standard. According to the method, the integration level of nursing data can be remarkably improved, information islands are broken, and data interoperability is improved.

The invention provides a radiographic image report generation method and system of a CT image, and the method comprises the steps: firstly transmitting an input CT image into a region division module, carrying out the whole-organ segmentation of the image, then distributing a whole-organ segmentation result according to a preset anatomical region with clinical significance, obtaining an anatomical region image with clinical significance, and carrying out the whole-organ segmentation according to a preset anatomical region with clinical significance. An anatomical area image with clinical significance is sent to a report generation module, an area image report text is generated, the report text and an original image are sent to a text-driven focus detection module, the position of a focus in the image is obtained and reported, and a doctor modifies the area image report and then sends the area image report to a report writing module to generate a final report. And meanwhile, the report modified by the doctor and the report automatically generated by the model are sent to an AI report generation quality evaluation module, so that the model carries out immediate update learning.

The invention is suitable for the technical field of medical image processing, and provides a fundus fluorescence radiography report generation method based on a visual large model, and the method comprises the steps: pre-training a fundus fluorescence radiography visual large model, extracting the image features of a fundus fluorescence radiography sequence image through the fundus fluorescence radiography visual large model, and constructing a pseudo time sequence curve; based on the pseudo time sequence curve, key point detection is carried out, a plurality of phase periods are divided, and a phase weight is set for each phase period; calculating a comprehensive score for each frame of the pseudo time sequence curve based on the phase weight in combination with time novelty; based on the comprehensive score, combining a preset target key frame number to obtain a key frame sequence; based on the key frame sequence, a multi-view sequence image report generation model is combined, a Chinese diagnosis report is generated, the time sequence information and phase characteristics of the fundus fluorescence contrast sequence can be fully mined, the diagnosis report conforming to clinical specifications is generated, and the limitation of the prior art is effectively overcome.

The invention discloses an image report process AI quality control system based on data fusion and a dynamic rule engine, and the system comprises a data layer module which is used for integrating multi-source data from an image archiving and communication system, a radiation information system and a hospital information system, and carrying out the preprocessing of the data, and forming a high-quality training data pool; furthermore, the image archiving and communication system obtains historical images in a DICOM format and corresponding report data; the radiation information system collects examination application information and doctor diagnosis information. According to the image report process AI quality control system based on data fusion and the dynamic rule engine, full-dimension and high-efficiency accurate quality control is realized: the system can perform comprehensive automatic screening of grammar, text, logic consistency and image-text relevance on an image report by integrating natural language processing, computer vision and a cross-modal matching technology; error correction response time is shortened to a second level from several minutes of each case of traditional manual auditing, and quality control efficiency is remarkably improved.

The invention provides an image report generation method based on cross-modal retrieval enhancement, and the technical scheme provided by the invention not only extracts visual global features to guarantee global sensory ability, but also extracts multi-scale visual embedding with pathological discrimination ability, so that detailed information required by report generation is obtained in a finer-grained manner; besides, multi-scale visual time sequence features are extracted from multi-scale visual embedding, and details focused on a focus area are optimized through time sequence iteration so as to construct visual local features; the visual global features and the visual joint features are fused to obtain visual features, the visual features contain global knowledge and also contain local knowledge of fine-grained knowledge of different scales, and the generation accuracy of the auxiliary report is improved; before the report is generated, the report text most relevant to the medical image is retrieved from the pre-indexed medical knowledge base to enhance the knowledge of the medical image, so that the autoregression generation model is guided to generate the auxiliary report more accurately.

The application provides a thymus disease risk prediction method and system based on cross-modal feature interaction, which comprises the following steps: respectively preprocessing CT image data, MRI image data and image reports; sequentially performing word segmentation, vector conversion, feature extraction and pooling on the third data, inputting the labeled semantic feature vector into an image generation network to generate a PET-CT image; inputting the first data, the second data and the PET-CT image into the same medical mamba feature extraction network respectively to obtain the first modal feature corresponding to the first data, the second modal feature corresponding to the second data and the third modal feature corresponding to the PET-CT image; performing feature fusion interaction on the first modal feature, the second modal feature and the third modal feature to obtain a fusion feature, and obtaining the thymus disease risk according to the fusion feature. The application can greatly improve the thymus disease prediction accuracy.

The invention relates to the technical field of medical management, in particular to a radiology department follow-up visit management system and method and a medium, and the method comprises a role confirmation module which is used for collecting basic information of a user, and the basic information comprises illness state information; determining role information of the user based on the basic information; the questionnaire management module is used for distributing different user questionnaire survey reports based on the role information and the illness state information of the users and collecting questionnaire survey results; the iconography data management module is used for collecting an iconography report of a user; the evaluation module is used for evaluating the questionnaire grade of the user based on the questionnaire survey result; the diagnosis module is used for generating a corresponding diagnosis result based on the questionnaire grade and the iconography report; and the diagnosis and treatment measure providing module is used for providing corresponding diagnosis and treatment measures based on the diagnosis result, and further performing batch processing on batch volunteers or patients to obtain the corresponding diagnosis and treatment measures, so that the working efficiency of clinical examination of the radiology department is improved.

The embodiment of the invention belongs to the technical field of medical image processing, and relates to a cervical cancer image report automatic generation method and related equipment, and the method comprises the steps: receiving a report generation request which is sent by a user terminal and carries a multi-sequence medical image; inputting the multi-sequence medical images into a multi-sequence three-dimensional MRI feature coding module for feature extraction operation to obtain medical image feature data; inputting the medical image feature data into a Mamb-Transform hybrid multi-mode decoding module to carry out an image report generation operation so as to obtain a medical image report; and outputting the medical image report to the user terminal. According to the method, the limitation of a traditional 2D image or a single-sequence 3D image is broken through, the unstructured, explainable and clinical-value image report is automatically generated on the basis of the three-dimensional multi-sequence MR I for the first time, and the method has wide clinical application value and important social benefits.

The invention discloses a medical image report generation method, system and device and a storage medium, and relates to the technical field of computer vision and natural language processing, and the method comprises the steps: constructing a causal graph model, taking an image as an input variable, taking a final report as an output variable, and taking a region-level pathological state in the image as an intermediary variable; the data imbalance factor is an unobservable hybrid factor; based on a causal graph model, intervening the intermediary variable by using a front door adjustment strategy, and establishing a causal path from visual evidence to report text; based on the intervened intermediary variable, executing a report generation process: a, identifying an abnormal region in the image by using a focus detection model and outputting a corresponding pathological discovery description; and b, inputting the intervened pathological discovery description and the original image into a visual language model to generate a complete clinical report by taking the intervened pathological discovery description and the original image as conditions. And the sensitivity of the model to pathological changes and the clinical reliability of report generation are improved.

The present invention discloses a method, device and computing equipment for pushing image reports based on RPA and AI. The method includes: using a preset lung segmentation model to segment the lung medical image to be detected sent by the RPA robot to obtain a target medical image containing only the lung area, using a preset lung nodule detection model to detect the target medical image to obtain attribute information of each suspected lung nodule, using a preset lung nodule segmentation model to segment the area where each suspected lung nodule is located to obtain three-dimensional contour information of each suspected lung nodule; generating a suspected lung nodule image report based on the attribute information and three-dimensional contour information of each suspected lung nodule and sending it to the hospital platform through the RPA robot. In this way, the lung nodules are detected by AI image analysis technology to obtain a suspected lung nodule image report, and the report is sent to the hospital platform through the RPA robot, which reduces the time doctors spend identifying lung nodules and improves efficiency.

The invention discloses a medical image report generation method and device and a storage medium. The method comprises the following steps: acquiring a target image examination part corresponding to a target examination object and a first image description text; and if it is detected that the first image description text has a writing error, correcting the first image description text based on a reference correction rule matched with the image description text in a correction rule library, and generating a second image description text. And identifying a plurality of medical entity words contained in the second image description text, and classifying the plurality of medical entity words to obtain classification tags corresponding to the plurality of medical entity words. According to the method, multiple pieces of structured data used for representing lesion features are generated based on the classification labels corresponding to the multiple medical entity words, and the multiple pieces of structured data are filled into the medical image report template corresponding to the target image examination part, so that the medical terms in the image description text can be more accurately filled into the template; and a high-quality image report is obtained.

The application discloses an artificial intelligence-based image analysis method and system for degenerative cervical spinal cord disease, relates to the technical field of artificial intelligence, and solves the technical problems of the existing technology, such as non-uniform image report standards, rough image analysis, increased patient anxiety, wasted medical resources, significant observer bias in DCM evaluation, and low efficiency of degenerative cervical spinal cord disease image analysis. A cervical vertebra dissection segmentation model is used to obtain a segmentation label; an intervertebral canal stenosis quantification grading model is used to obtain an intervertebral canal stenosis prediction grade of the segmentation label for the intervertebral canal; a spinal cord compression classification model is used to obtain a spinal cord compression result of the segmentation label for the cervical spinal cord; and a cervical vertebra image analysis model is used to obtain an analysis report of the cervical vertebra nuclear magnetic image. The image report is standardized, improved segmentation models are used, classification models are used in cooperation, and cervical vertebra image analysis models are used for processing, so that the accuracy of analysis is improved, and the efficiency of degenerative cervical spinal cord disease image analysis is improved.

The present application discloses a method and system for generating medical imaging reports based on a large language model, which relates to the field of imaging report generation. It first obtains the patient's original imaging data and clinical background text, and extracts the image embedding vector and background embedding vector respectively. Then, these embedding vectors are used to perform case retrieval based on prior knowledge, and K highly relevant historical case reports are screened out from massive historical data. Subsequently, the image embedding vectors and historical case reports are input into the observation large language model and output in a structured JSON format. Finally, a large language model is written to integrate visual evidence JSON, clinical background text and historical case reports to generate the final medical imaging report. This method effectively solves problems such as incoherent report logic and inaccurate information through a phased and multimodal fusion approach, significantly improving the quality and generation efficiency of the report.

The present application relates to a structured information processing method for imaging report text, a lung disease monitoring method and system. The structured information processing method includes: S11, further dividing the part and morphological features in the imaging medical professional entity into two categories, negative and positive, to obtain eight entity labels, namely, vacancy filler, sentence starter, sentence terminator, part-negative, part-positive, morphology-negative, morphology-positive and disease name, performing named entity recognition on the imaging report text based on the eight entity labels, and extracting the named entity output in BIO format; S12, filtering out redundant information marked as O and entity labels as part-negative and morphology-negative based on the extracted named entities, calculating the sentence vector and storing it in the database. The present application realizes the efficient extraction of symptom information and obtains a more expressive sentence vector, so that a more accurate cluster of similar cases can be obtained for spatiotemporal distribution feature analysis.

The invention discloses a medical image report generation method based on a multi-modal fusion large language model. The method comprises the following steps: fusing local visual features of a medical image with a structured knowledge label through a multi-channel attention mechanism to generate a customized prompt vector; meanwhile, a context dependence matrix is introduced, a cross-graph convolution attention network is constructed, and evolution path modeling is carried out on the multi-time-point image; the prompts and contextual information are input into a large language model to automatically generate a continuous, professional image report. During training, a semantic consistency regular item loss function is combined, so that the report is kept consistent with the image in the semantic level. The system supports seamless connection and private deployment with the multi-mode PACS, and meets different medical environment requirements. According to the method, the quality and the continuity of automatic reports can be remarkably improved, and the clinical requirements on high-quality reports are met.

The invention relates to the technical field of medical image processing and artificial intelligence auxiliary diagnosis, in particular to a medical image report generation informatization planning system, which comprises a multi-modal time sequence difference feature analysis module used for acquiring current medical image data and historical image data of a patient and generating a time sequence difference residual vector; the semantic entropy flow conservation and negentropy injection control module is used for calculating a system net entropy target; the dynamic planning generation module based on residual driving is used for generating a diagnosis report text sequence; the diagnostic specificity and normativity collaborative optimization module is used for constructing a composite loss function containing a cross entropy loss term and a specificity penalty term and carrying out iterative updating on system parameters based on the composite loss function, and the specificity penalty term is used for constraining the description accuracy of the generated text on the dynamic change of the image; the problem that the dynamic change of the disease course cannot be captured only by relying on static image analysis in the prior art is effectively solved.

The invention provides a DICOM file processing method and device, equipment, a medium and a product. The method comprises the steps of obtaining a to-be-processed DICOM file, and performing anomaly detection and preprocessing on the to-be-processed DICOM file; according to the preprocessed non-abnormal DICOM file, analyzing the non-abnormal DICOM file to obtain analyzed multi-modal image data and metadata, and carrying out persistence processing on the analyzed multi-modal image data and metadata; identifying an image type of the multi-modal image data, and calling a data display template corresponding to the image type; and screening target metadata associated with the image type from the metadata after persistence processing, and filling the target metadata and the analyzed multi-modal image data into a data display template to form an image display report. The metadata of the DICOM file can be quickly obtained, and the overall efficiency of medical image data processing, image reading convenience and the standardization level of image report generation are improved.

The application discloses a kind of based on multi-feature enhancement medical image report generation method and system, method includes: using superpixel segmentation technology is segmented to medical image, obtain region of interest;Using pre-trained DenseNet is extracted to local feature to region of interest, and local feature is as graph node to construct relation graph;The relation graph is input into graph network GAT to learn graph feature, and the graph feature is input into encoder;The feature obtained by each layer of the encoder is output to the corresponding decoder layer in the form of U-type connection, the decoder predicts the next word based on the image feature obtained in the encoder and the generated text sequence feature, until all words are generated, finally obtain complete image report.Through the technical scheme of the application, the recognition of local lesions is enhanced, the interaction between text features and image features is strengthened, the semantic details and feature relationships of medical images are strengthened, and the accuracy of the generated report is improved.

The application discloses a disease evolution perception-based medical image report generation method and device based on analogy reasoning, and relates to the technical field of medical image analysis. The method comprises the following steps: based on an adaptive alignment mechanism, aligning the features of two images in a key lesion area; based on a visual evolution consistency loss function, constraining the learning of the evolution relationship between images; according to a current medical image report and a historical medical image report, extracting features by using a text encoder; based on a text evolution consistency loss function, constraining the learning of the evolution relationship between reports; and generating a medical image report based on an optimized visual encoder, an optimized adaptive region alignment module, an optimized evolution relationship learner, a text encoder, an optimized relationship mapper and an optimized large language model. The application is an accurate and efficient medical image report generation method based on cross-time disease evolution modeling.

The invention provides a focus identification method based on a standard template and an image report generation method. The method specifically comprises the following steps: receiving a medical image of a patient; determining a part to which the medical image of the patient belongs, and selecting a standard template corresponding to the part, the standard template comprising an anatomical marker structure; extracting a key internal structure contour in the medical image of the patient, and performing feature matching on the key internal structure contour and a corresponding anatomical marker structure in the standard template; under the condition of feature matching registration, voxel-by-voxel difference calculation is carried out on the medical image of the patient and a standard template in various transform domains, and a multi-channel residual image is generated; generating an exception enhancement graph according to the multi-channel residual image to screen an exception region; and automatically generating a focus description statement based on the abnormal region. According to the invention, the workload of initial writing of an image report by a radiologist can be obviously reduced, and the clinical working efficiency and the image interpretation consistency can be improved.

The invention provides an image report generation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the detection information of a user, and the detection information comprises a medical image and clinical information; obtaining abnormal sign information of each region based on the medical image and the clinical information, and obtaining description information of each focus based on the medical image; under the condition that the historical examination information of the user does not exist, screening description information of a key focus based on the description information of each focus and the clinical information; the image report is generated based on the clinical information, the abnormal sign information and the description information of the key focus, the abnormal sign information and the description information of the focus in each area are obtained by integrating the medical image and the clinical information, key details of the physical condition of a patient can be comprehensively and accurately captured, omission or misjudgment caused by a single information source is avoided, and the accuracy of the patient is improved. And the content of the image report is more complete and accurate.

Some embodiments of the present disclosure provide a framework for using unsupervised artificial intelligence to automatically abstract and align clinical facets. The approach of the present application may be shown to reduce human involvement and, accordingly, enhance privacy compliance. Aspects of the present application relate to a process of self-learning from the data available. Accordingly, aspects of the present application may be shown to be resilient to the appearance of new concepts and facets in future data. Additionally, aspects of the present application may be shown to adapt well when presented with different languages, different styles of documentation and different clinical domains. Aspects of the present application relate to processing unstructured, non-fielded data, such as clinical notes, admission and discharge summaries, surgical notes, lab reports and imaging reports. These notes may be considered to contain hidden insights in the clinical domain. Additionally, these notes may be considered to contain data that may not be captured elsewhere in a readily usable way. Aspects of the present application may be shown to support analysis of large size populations at a relatively low incremental cost.

This invention belongs to the field of medical image data processing technology, specifically disclosing a multi-agent medical image report generation method and system based on fine-grained organ perception. The method includes the following steps: constructing an organ-level medical image dataset and an organ-level report dataset; inputting the organ-level medical image dataset and organ-level report dataset into a multi-agent system based on fine-grained organ perception to extract features, performing multimodal fusion and decoding to obtain an organ-level predicted report; serializing the organ-level predicted report and then sending it to the agent semantic interaction module for unified semantic modeling to generate the final medical image report. This technical solution utilizes a fine-grained organ perception multi-agent architecture to achieve organ-level fine-grained cross-modal modeling and organ-level report prediction, and improves the consistency and completeness of multi-organ information through the agent semantic interaction module, thereby enhancing the accuracy and reliability of the generated medical image report.