181 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 discloses an image report generation method, system and device based on multi-modal learning optimization, and the method comprises an image retrieval module which is used for carrying out the image feature extraction of an input medical image, and obtaining similar medical images and reports; the simulation generation module is used for guiding an image report generation model to generate an initial draft report by utilizing the medical image and the report; the personalized diagnosis module is used for carrying out personalized adjustment on the report generation model according to the historical report preference of the doctor or the historical medical data of the patient; the reinforcement learning optimization module is used for dynamically adjusting a text generation mode based on a reinforcement learning report optimization strategy and optimizing an image report generation model in combination with doctor scoring, automatic evaluation and clinical feedback; and the iterative refining module is used for performing text retrieval by using the initial draft report as a query, acquiring text fragments which are semantically similar to the initial draft report, and performing neural symbol reasoning optimization to generate a final report.

The invention discloses a medical image report generation method based on multiple agents. The method comprises the following steps: step 1, constructing an image segmentation agent, a cue word retrieval agent, a report generation agent and an image database; 2, inputting the medical image into an image segmentation agent, and generating a focus label through the image segmentation agent; 3, inputting the focus label into a cue word retrieval intelligent agent, and obtaining an example report related to the focus label by the cue word retrieval intelligent agent; and 4, taking the focus label and the example report as input of a report generation agent, outputting a preliminary image report by the report generation agent, and then introducing a terminology condition constraint and reinforcement mechanism to correct the preliminary image report to generate a final image report. According to the invention, the workload of doctors can be reduced while the image analysis report which is accurate and meets the medical standard is generated.

The invention discloses an image report generation method, system and device based on multi-granularity knowledge fusion, and the method comprises an image report pre-generation unit which is used for receiving a medical image as input, and processing the medical image through an image report generation model to obtain an initial image report text; the knowledge retrieval unit is used for retrieving related medical entities and definition knowledge thereof from a public medical knowledge base, a local self-built medical knowledge base and a real-time data source based on the initial image report text to obtain multi-source medical knowledge; the knowledge fusion unit is used for fusing the multi-source medical knowledge to obtain a fused knowledge text; and the final report generation unit is used for combining the medical image, the initial image report text and the fused knowledge text, and regenerating an image report through an image report generation model to obtain a final image report. According to the method, diagnosis errors can be remarkably reduced, the working efficiency of doctors is improved, and meanwhile reports easier to understand are provided for patients.

The invention relates to a multi-modal medical image report generation method and system based on a medical thinking chain. The method comprises the following steps: acquiring an existing original disordered unstructured medical image report data set; preprocessing the image reports in the data set to obtain a high-quality data set; processing the medical image report in the high-quality data set based on a large language model, and extracting hierarchical medical attribute question and answer pairs; the hierarchical medical mathematical question and answer pairs are connected in series and reconstructed into chain reasoning question and answer pairs; and performing fine adjustment on an existing medical image report generation model by utilizing the chain type reasoning question and answer pair data, and generating a medical image report by utilizing the medical image report generation model after fine adjustment. Compared with the prior art, the method has the advantages that the utilization rate of the multi-modal features and the recognition capability of intention understanding are explicitly enhanced, the generalization, reliability and accuracy of the reasoning capability are improved, and the reliability of the generated medical image report is higher.

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 discloses a medical image report structuring method and a related device, and relates to the technical field of natural language processing, and the medical image report structuring method comprises the steps: obtaining a target medical image report; based on a pre-obtained medical image report structured model, structured processing is conducted on the target medical image report, structured information of the target medical image report is obtained, and the medical image report structured model is obtained by conducting instruction fine adjustment on a pre-trained large language model through training data; the training data at least comprises a first training medical image report marked with structured information. The medical image report structuring method disclosed by the invention is an end-to-end structuring method based on a large language model, the key information contained in the medical image report can be accurately, efficiently and normatively extracted by the method, and the method is simple in implementation process and relatively low in manual annotation cost.

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 provides a knowledge-driven medical image report structured processing method and device, and the method is based on an image knowledge base constructed through the combination of various modes such as medical knowledge, large language model or rule matching, manual verification and the like, and the image knowledge base comprises complete medical image knowledge. Furthermore, the accuracy of the medical entity in the image report identified based on the medical knowledge in the image knowledge base is higher. And carrying out hierarchical combination on the medical entities with the cascade relationship in the same clause to realize the planning output of the structured information. And finally, for the medical entity information of all clauses in the image report, removing repeated and redundant entity information existing among different clauses to obtain structured information corresponding to the image report, and further improving the accuracy of the structured information in the image report.

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 invention belongs to the technical field of medical image auxiliary diagnosis, and particularly relates to a medical image report generation method and system of a dual graph convolutional network. The method comprises the following steps: acquiring a medical image picture set and a medical image report set corresponding to the medical image picture set; wherein the medical image report set serves as a label corresponding to the medical image picture set; for each medical image picture in the medical image picture set, extracting global-level features and local-level features from the medical image pictures by using a dual graph convolutional network, and performing feature fusion according to the global-level features and the local-level features to obtain aggregation features of the medical image pictures; and training a coding and decoding model according to all the aggregation features of the medical image picture set and the corresponding labels, so that the trained coding and decoding model can output a corresponding medical image report based on the input medical image picture.

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 provides a method for automatically generating a modification type knowledge base based on text report analysis. The method comprises the following steps: acquiring modification type data before and after a preset number of image reports which are manually labeled are modified; defining a database formed by modification type data before and after the preset number of image reports are modified as a modification type basic library; obtaining a single disease instance library; each single disease instance library comprises cue words and answers; and automatically generating a modification type knowledge base according to a preset rule based on the cue word of the large language model, the modification type basic library and the single disease instance library. The invention further discloses a system for automatically generating the modification type knowledge base based on text report analysis. According to the method, a standardized knowledge base can be continuously integrated and expanded for standardizing writing and teaching of reports, and intelligent teaching and performance evaluation can be designed based on report content extraction and type modification.

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 present application discloses a method, device and storage medium for quantifying the workload of an imaging report, including: obtaining a target imaging report corresponding to a patient, and extracting multiple first key features in the text content of the target imaging report using an NLP model; encoding each first key feature using a pre-set encoding rule, and determining multiple feature codes corresponding to each first key feature and multiple encoding values ​​corresponding to each feature code; generating a corresponding first data set based on multiple feature codes and multiple encoding values; and inputting the first data set into a pre-set workload prediction model, and determining the workload corresponding to the target imaging report. The technical effect of truly reflecting the workload of doctors when filling out diagnostic reports based on target imaging reports and enhancing the work enthusiasm of doctors is achieved.

The invention discloses a three-dimensional CT (Computed Tomography) image report generation method, system and equipment and a medium. The method comprises the following steps: acquiring a target three-dimensional CT image and a target text prompt corresponding to the target three-dimensional CT image; and inputting the target three-dimensional CT image and the target text prompt into a preset report generation model to obtain a target image report of the target three-dimensional CT image output by the report generation model, so that automatic generation from the three-dimensional CT image to the image report can be realized, the accuracy and efficiency of image report generation are improved, and the user experience is improved. And the clinical adaptability is further improved.

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 purpose of the present invention is to disclose a probability statistical method, device and storage medium for illegal words in medical imaging reports, comprising the following steps: segmenting the i-th historical medical imaging report to obtain a word set C i ; Set a first large data word set C, wherein the first large data word set C includes C1, C2...C i ; Segment the j-th medical imaging examination item and patient gender information to obtain the word set K j ; Set a second data word set K, wherein the second data word set K includes K1, K2...K i ; Calculate the probability P1 = P(M x |N y ); According to the naive Bayes formula, calculate the probability P2 = P(N y |M x )=(P(N y )P(M x |N y )) / (P(M x )), when P(N y |M x ) is 0, then the vocabulary M is determined x Relative word set N y It is a mutually exclusive relationship, and the beneficial effect is: through the naive Bayes formula, the probability of a specific word appearing in a specific word set K is counted as 0, and the mutually exclusive relationship between the specific word and the specific word set K is obtained and stored, which is used as a prompt when a new medical imaging report is compiled later.

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.