55 results about "Radiology report" patented technology

The invention relates to the field of radiology reports, and discloses a radiology report generation method fusing disease perception comparison and cross-modal alignment, and the method comprises the following steps: respectively extracting visual features of a medical image and text features of a radiology report through a visual encoder and a text encoder; utilizing a disease perception contrast learning module DACL to map the visual features and the text features to a unified semantic space; self-adaptive feature matching is carried out by using a dynamically updated memory matrix through a cross-modal memory alignment module CMA; a three-stage training strategy optimization model is adopted, and pre-training, reinforcement learning fine adjustment and knowledge distillation are included. Incremental knowledge is dynamically updated through text features, cross-modal feature alignment tasks are focused, coupling with feature representation is avoided, alignment efficiency is improved, a three-stage training strategy is adopted, if student model performance is better, weights are migrated, and clinical accuracy and generalization ability of the model are further enhanced.

The invention discloses a radiology report generation method and system based on visual collaborative enhancement and a cross-modal fusion network, and belongs to the technical field of natural language processing. According to the invention, a visual collaborative enhancement module is designed for modeling visual features from global and local perspectives to enhance the recognition of abnormal lesions in a radiology image, so that the attention deviation of an abnormal region caused by unbalanced data distribution is relieved. Meanwhile, a cross-modal information fusion device is provided, the module utilizes a novel double cross-modal communication component to promote multi-level fusion of visual and text information, the problem of modal isomerism is solved, and semantic-level feature alignment and refinement are achieved. According to the method, the problem that a model cannot capture key focus features due to unbalanced data distribution in a radiology image is solved, and the problem that effective alignment and fusion are difficult due to the fact that feature spaces of different modal information of image and text information are different is solved.

The invention discloses a cross-modal gating fusion radiology report generation method combined with clinical semantic enhancement, and relates to the technical field of medical report generation. A semantic enhancement decoding unit is used for performing deep fusion on image sequence representation, historical text embedding and clinical semantic representation to obtain final enhancement decoding representation of multi-modal semantic enhancement; and performing report generation on the final enhanced decoding representation output by the semantic enhanced decoding unit to obtain a predicted radiology report. The predicted radiology report generated by the radiology report generation method is closer to a radiology report sample text in the aspects of sentence structure and coherence.

The invention discloses a radiology report generation method and system based on global dependency learning, and belongs to the technical field of natural language processing. According to the method, a global dependency learning module is designed, and the module integrates rotation position coding to enhance original Mama, so that long-sequence visual feature dependency is effectively modeled, and the model calculation efficiency is improved. According to the method, the problems that the visual long-term dependence modeling and calculation efficiency in the radiology image are difficult to effectively balance, and the heterogeneity of the characteristics of different modal information of the image and the text is difficult to effectively align and fuse are solved; the method not only can effectively capture the long-term dependency relationship on the key visual features in the radiology image, but also can improve the model calculation efficiency, and can enhance the alignment and fusion capability among the multi-modal heterogeneous information.

The present invention relates to an AI-based system and method for generating enhanced radiology reports. The system comprises a database for storing multimodal patient data, a natural language processing (NLP) module for extracting clinical information, and a machine learning module for correlating the clinical information with radiology images to identify diagnostic insights. An AI-based report generation module analyzes the images and clinical information to generate a preliminary report, which is refined based on radiologist input. The generated report is then integrated into the patient's electronic health record. The system employs techniques such as multimodal deep learning, active learning, explainable AI, and federated learning to enhance diagnostic accuracy, capture expert feedback, provide transparency, and enable multi-institutional collaboration. The invention aims to improve the accuracy, efficiency, and value of radiology reporting in patient care.

The invention discloses a chest image diagnosis method and system based on multi-modal sign collection, and relates to the technical field of medical image.The method comprises the steps that a chest image of a patient is obtained, electrocardiosignals and blood oxygen saturation data are synchronously collected, and an associated radiology report is obtained; the image lesion features and the frequency domain rhythm template are combined for processing, motion artifacts are eliminated through a frequency domain decoupling equation, and refined image features are output; inputting the refined image features and the text pathological semantic features into a bidirectional attention mechanism to generate fusion features, and splicing the oxyhemoglobin saturation data and the text pathological semantic features into a sign-text vector; and inputting the fusion feature and the sign-text joint vector into a multi-task loss function, and outputting a structured diagnosis report. According to the method, accurate elimination of motion artifacts is achieved through a frequency domain decoupling equation, and coupling calculation is conducted on an electrocardio rhythm template and image lesion features in a frequency domain space.

A computer-implemented method for assisting radiologists in efficiently preparing radiology reports from diagnostic images is disclosed. The method includes processing radiology images using artificial intelligence to automatically detect anatomical structures and pathologies, and generating positional and descriptive data for each detected feature. An initial radiology report, fully populated with the detected features, is automatically generated prior to user interaction and displayed through a user interface comprising synchronized image and text panels. The radiologist reviews this initial report by selectively adding, modifying, or deleting features through a user interface input that identifies each feature and an associated action. The report is updated immediately based on these inputs, ensuring continued synchronization between image annotations and their descriptive narratives. This approach reduces reporting turnaround times, decreases cognitive workload, and minimizes diagnostic errors.

The invention discloses a learnable radiology report generation method based on visual text alignment and fusion of retrieval enhancement. The method comprises the steps of collecting and respectively constructing a model training data set and an auxiliary data set, then constructing a visual text alignment and fusion model based on retrieval enhancement, and inputting the model training data set and the auxiliary data set into the visual text alignment and fusion model based on retrieval enhancement together for training. Constructing an inference model according to the trained visual text alignment and fusion model based on retrieval enhancement; and inputting the to-be-detected medical image and the auxiliary data set into the reasoning model for processing to obtain a radiology report corresponding to the to-be-detected medical image. According to the method, retrieval correlation is enhanced, meanwhile, a fine-grained vision-text alignment and fusion method is adopted to align and fuse features, and the problem that fine-grained region-sentence alignment is difficult due to weak supervision of an image report level in the medical report generation process is solved.

The invention discloses a radiodiagnosis report intelligent distribution system and method based on a multi-dimensional rule engine, and belongs to the technical field of medical image information processing. In order to solve the problems that existing radiology department reports depend on manual distribution, the efficiency is low, emergency reports are prone to missing detection, and resource distribution is uneven, a data input module is in butt joint with a PACS / RIS system to collect data and preprocess the data; the dynamic rule engine integrates multi-dimensional parameters such as patient information and examination types, and dynamically calculates distribution priorities according to a weight algorithm; the intelligent matching module is combined with NLP keyword extraction, load balancing and historical efficiency prediction to realize accurate matching of a report and a doctor; the exception handling module provides a three-level rollback strategy; and the security convergence block ensures data security. The system can dynamically adapt to hospital requirements, improves report distribution efficiency and accuracy, meets medical compliance requirements, and is suitable for hospitals and third-party image centers.

The invention discloses a radiology report generation method based on hierarchical interactive fusion, and relates to the technical field of medical image intelligent processing, and the method comprises the steps: collecting original image data, carrying out the intensity normalization, obtaining normalized image data, carrying out the multi-level visual feature extraction and feature dimension unification of the normalized image data, and carrying out the multi-level visual feature extraction and feature dimension unification of the normalized image data; forming a hierarchical visual feature set; coding processing is carried out on the hierarchical visual feature set, cross-layer attention relations between a shallow layer and a deep layer and between a middle layer and the deep layer are constructed in the coding process, a shallow layer two-dimensional biased field and a middle layer two-dimensional biased field are formed, and a migration smoothness index and a consistency index are obtained after nonlinear resampling; and in the decoding stage, a multi-path cross attention structure is constructed based on the coded hierarchical visual feature set, an abnormal priori graph is constructed according to the shallow two-dimensional biased field and the middle two-dimensional biased field, attention bias is formed, and a cross-hierarchical cross attention aggregation vector is generated. According to the invention, association expression of multi-level visual features is realized.

The application belongs to the technical field of image processing, and discloses a personalized CT image reconstruction system based on double physical driving, which comprises an encoder, an anatomical information capturing module, a scanning information capturing module, a personalized modulation module and a decoder; the encoder is used for feature extraction of a scanned image to obtain imaging features; the anatomical information capturing module is used for extracting anatomical modulation parameters containing anatomical information according to a radiology report; the scanning information capturing module is used for capturing potential relationships between scanning protocols and noise distributions to obtain scanning modulation parameters containing physical information; the personalized modulation module is used for modulating the imaging features obtained by the encoder according to the anatomical modulation parameters and the scanning modulation parameters to obtain personalized imaging features; and the decoder is used for generating a personalized CT image according to the personalized imaging features. The application also discloses a federated learning method suitable for personalized CT image reconstruction. Through double physical driving based on scanning parameters and anatomical information, the application can effectively realize personalized CT imaging.

A radiology report automatic generation method, system, device and medium based on patient specific priori knowledge uses a special token to represent missing patient clinical context information, so that a text encoder can process complete and incomplete clinical context input in a unified manner, and then robust clinical context features are extracted; the method comprises the following steps: constructing a space-time fusion network STF, integrating previous medical images of a patient, establishing a difference mapping relation between a current image and a historical image for modeling an evolutionary process of a disease, and extracting space-time visual features with time dependence; establishing an attention-enhanced hierarchical fusion network for fusing multiple layers of hidden states in a visual encoder so as to extract multiple layers of hierarchical visual features with rich semantics; a prior perception progressive fusion network is introduced, and patient specific prior knowledge and hierarchical visual features are gradually fused in a coarse-to-fine mode to generate multi-modal features facing radiology report generation; the method comprises the following steps: designing a two-stage training strategy: in the first stage, aiming at image-text alignment, improving the accuracy of medical image-text retrieval; and in the second stage, the generation of the radiology report is taken as a target, a text decoder is optimized, and the performance of the generated radiology report in the aspects of clinical semantic accuracy and language expression quality is improved.

This specification provides a cardiac ultrasound coronary artery stenosis diagnostic system and method based on a visual language transfer model. The system includes: a database construction module for standardizing multi-view cardiac ultrasound video sequences and radiological reports; a cardiac region of interest (ROI) standardization and labeling module for segmenting myocardial segments, obtaining ROI data, and generating a coronary artery-specific perfusion region mask as the ROI for vascular-level stenosis prediction; a visual language transfer pre-training module for semantic alignment of video data and report text using a visual language transfer model; and a downstream task prediction module for utilizing the enhanced attention mechanism of the coronary artery-specific perfusion region mask, based on the global visual features output by the visual language transfer pre-training module, to output patient-level classification results for obstructive coronary artery disease and vascular-level stenosis prediction results for LAD, LCX, and RCA.

The invention belongs to the technical field of multi-modal natural language generation, and provides a radiology report generation method and system based on feature fusion and medical guidance, and the method comprises the steps: carrying out the feature extraction of medical image data and text report data, obtaining the text features with enhanced medical knowledge, and carrying out the feature extraction of the text features; obtaining image space-time characteristics reflecting the evolution information of the focus; injecting the medical knowledge enhanced text features into image space-time features by using a cross attention mechanism to generate multi-modal space-time fusion features; and according to the multi-modal space-time fusion features and a preset decoder, generating a radiology department medical text report, and by integrating historical and current multi-temporal and multi-view image data of a patient, combining anatomical structure constraints and lesion evolution rule modeling, on the premise of guaranteeing term normalization and logic coherence, establishing a medical text report of the radiology department. Longitudinal lesion evolution information and multi-view complementary information can be fully utilized, and the professionality and accuracy of reports are improved.

In one embodiment, a method comprising accessing radiographic images of an animal, wherein one or more first radiographic images of the radiographic images depict the animal from one or more views, respectively, and wherein one or more second radiographic images of the radiographic images depict one or more body parts of the animal, respectively, determining disease classifications associated with the animal based on analyzing the radiographic images by a machine learning model, generating a diagnostic report associated with the animal based on the machine learning model, wherein the diagnostic report includes the disease classifications and a natural-language textual radiology report, and sending instructions for presenting the diagnostic report to a user device.

A radiology report generation system is configured to obtain an analysis result for a target medical image using an artificial intelligence analysis model, extract at least one similar image to the target medical image from a catalog set comprising medical image-radiology report pairs; determine at least one radiology report paired with the at least one similar image as a reference image, and generate a radiology report for the target medical image based on the analysis result, using the reference report as a guideline.

The invention provides a system and method for quality control in radiology reports. It collects data from reports, medical scan images, and questionnaires within a Safety, Quality, Efficiency, and Productivity (SQEP) framework. Scores for safety, quality, productivity, and efficiency are computed for radiologists and departments using weighted parameters, peer reviews, and audit outcomes. A consolidated SQEP score evaluates overall performance. The system identifies deviations in report quality through trend analysis and correlations, providing corrective feedback and training recommendations to radiologists. By addressing errors proactively, the system enhances diagnostic accuracy, operational efficiency, and compliance with quality standards. This comprehensive approach ensures improved performance and reliability in radiology departments while maintaining high-quality patient care.

The invention discloses a radiology report generation method and system based on focus guide mask and knowledge graph enhancement, and belongs to the technical field of medical artificial intelligence. The method comprises the following steps: preprocessing a chest medical image and extracting local features; constructing a lesion knowledge graph containing an organ-lesion-disease category triple; obtaining a candidate organ and a mask image thereof through a pre-trained focus detection model; retrieving related knowledge from the knowledge graph based on the candidate organs to generate focus enhanced knowledge features, and inputting the organ mask and the original image into a mask image attention layer together to obtain mask enhanced image features; using a multi-branch gating cross-modal fusion module to carry out adaptive weighted fusion on the two types of features to obtain knowledge mask enhanced fusion representation; and finally, a radiology report is generated through a Transform encoder-decoder. According to the method, image-text alignment of organ granularity can be realized, language illusion in a generated report is effectively reduced, and clinical consistency and interpretability of the report are improved.

Methods and systems described enable automatic generation of a significant portion of or all of a clinical report (e.g., radiology report), using multimodal models trained on image and language data. Methods described can transform unstructured language and image information into findings, as well as an accurate and comprehensive clinical report, in a designated style (e.g., writing style). The methods and systems described thus significantly improve performance in generation and processing of clinical reports, in relation to time saved per clinical shift, dictation effort, medical billing, and other performance factors.

The invention provides a chest radiograph report generation method and system based on cross-modal alignment and a significant semantic region, and is applied to the technical field of data processing. The method comprises the following steps: processing chest X-ray chest radiograph image information of a target patient and corresponding initial radiology report text information on the basis of a network model for identifying a saliency region of medical semantic information, and generating the saliency region and a saliency map of the medical semantic information; processing the chest X-ray chest radiograph image of the target patient and the salient region image of the medical semantic information based on a salient region guided mask image modeling module, and generating image block features after mask image reconstruction; processing the saliency map and the features of the image blocks after mask image reconstruction based on a language generation model guided by the saliency map, and generating target radiology report information; and processing the target radiology report information to generate an evaluation result generated by the chest radiography report.

The invention discloses a training method and device for a multi-modal representation learning model. The method comprises the following steps: classifying medical image data by a representation learning model to obtain a first category vector, and classifying an iconography report text to obtain a second category vector; calculating the similarity between each first category vector and all second category vectors to generate a first similarity matrix, and normalizing the first similarity matrix to obtain a first probability distribution matrix; calculating the similarity between each second category vector and all the first category vectors to generate a second similarity matrix, and normalizing the second similarity matrix to obtain a second probability distribution matrix; and performing parameter adjustment on the representation learning model according to the first probability distribution matrix and the second probability distribution matrix to obtain a multi-modal representation learning model. According to the invention, through a two-way alignment mechanism of label similarity and feature similarity, a high-precision multi-modal representation learning model is obtained through training under limited medical annotation data, and the medical assistance value of the model is improved.

A system and method for generating radiology reports. The systems and methods display an image of a region of interest on a display, determine image characteristics of the region of interest, determine, via a processor, a recommendation based on the image characteristics, and generate, via the processor, a report including the recommendation.

The application discloses a kind of gradient optimization method and system of multitask radiology report generation, it is related to medical information technology field, including, obtaining medical training data set containing image, label and report text, construct and include visual encoder, clinical constraint auxiliary task branch and text decoder Multi-task report generation model;Firstly, the failure mechanism of linear scalarization is studied from the perspective of gradient dynamics, and it is formalized as the "double dilemma" of drift term bias and diffusion term attenuation using the SDE framework, thereby revealing the geometric root cause of suboptimality in RRG multitask optimization, and proposing CAME-Grad, a gradient optimization algorithm designed specifically for multitask RRG. As an optimizer independent of the backbone network, it can be integrated into various model architectures in a plug-and-play manner. On the MIMIC-CXR and IU X-Ray datasets, CAME-Grad was extensively evaluated across eight representative RRG methods, and the results showed that its average clinical performance improved by 2.3% and 1.9%, respectively.

A method for radiology reporting includes any or all of: determining a set of inputs, determining a template, generating a radiology report, processing the radiology report, adjusting the radiology report, and / or any other suitable steps. A system for radiology reporting includes and / or interfaces with any or all of: a set of models, a computing system, a set of databases, a user interface, user devices, and / or any other suitable system components.

The embodiment of the invention discloses a data analysis method based on multi-modal data fusion and related equipment, and relates to the technical field of machine learning and data analysis, and the method comprises the steps: obtaining the chief complaint data of a target person through a natural language processing technology according to the voice information of the target person; analyzing the medical image information of the target person through a preset deep learning model to obtain a radiology report of the target person; inputting the chief complaint data and the radiology report into a primary diagnosis model based on a single-layer multi-head attention mechanism to obtain a primary diagnosis result; acquiring historical doctor seeing data of the target person, and constructing a historical case knowledge graph based on the historical doctor seeing data; performing embedded learning on the preliminary diagnosis result and the historical case knowledge graph by using a diagnosis model to obtain a diagnosis suggestion; and inputting the diagnosis suggestions into a domain expert knowledge base so as to verify the diagnosis suggestions.

The present disclosure relates to a radiology report generation system and method. This radiology report generation system is configured to: obtain an analysis result for a target medical image by using an artificial intelligence analysis model; extract at least one similar image to the target medical image from a catalog set composed of pairs of medical images and radiology reports; determine at least one radiology report corresponding to the at least one similar image as a reference report; and generate a radiology report from the analysis result for the target medical image by using the reference report as a guideline.

This invention discloses a method for generating radiological image reports based on explicit visual evidence. The specific implementation steps include: constructing a training set for a clinical diagnostic inference chain based on anatomical priors; fine-tuning a pre-trained multimodal large model using a ternary collaborative loss function; extracting high-resolution grid features during the inference stage, locking key lesion features through an active retrieval mechanism triggered by interleaved tokens, and embedding these features into a contextual sequence after processing by a perceptual resampling module as explicit visual evidence, driving the model to generate a radiological report supported by the image. This invention achieves a paradigm shift from static full-image injection to dynamic evidence-based inference, overcoming the problems of opacity and easy neglect of small lesions in the inference process of existing radiological image reports, while improving the diagnostic reliability and computational efficiency of the model while preserving high-resolution details.

A weakly supervised intracranial hemorrhage (ICH) detection workflow includes training a deep learning (DL) model including a coupled convolutional neural network and recurrent neural network on a large dataset of CT scans with expert-labeled slices indicating presence or absence of ICH. Transfer learning (TL) is used to further train the DL model using a second large dataset of CT scans with only scan labels extracted from radiology reports using natural language processing (NLP). The DL model weights each slice of the scan against the final ICH diagnosis using an attention-based bi-directional long-short term memory network, where the attention weights represent slice-level ICH predictions. Model-generated heatmaps highlight significant regions of the CT scans that lead to the provided ICH predictions.

The present application provides a radiology report generation method fusing clinical semantic modulation and hyperbolic prototype classification, which comprises: acquiring chest X-ray images and their associated clinical context knowledge, extracting visual features of the images using a visual encoder, extracting clinical semantic embedding using a medical language encoder, and retrieving relevant report features from a reference report database; constructing and utilizing a clinical semantic modulation module to generate modulated visual representation; constructing and utilizing a hyperbolic prototype classification module to generate diagnosis awareness prompts; inputting the modulated visual features and diagnosis awareness prompts into a decoder to complete the autoregressive generation of the radiology report. The present application can fully utilize the cross-modal fusion of clinical knowledge and visual features, and utilize the exponential expansion embedding capacity of hyperbolic space to improve the disease detection ability under the condition of class imbalance, and improve the clinical accuracy of radiology report generation.

The invention relates to the technical field of computer vision and image processing, in particular to a radiology report generation method based on comparative learning and adaptive knowledge integration, and the method comprises the steps: obtaining a target medical image and a radiology report text; the target medical image and the radiology report text are input into a preset information integration model, a radiology report with the unified medical image and knowledge is output, the information integration model extracts features in the target medical image and the radiology report text, the features are aligned through comparative learning and then subjected to adaptive fusion, and the target medical image and the radiology report text are obtained. And outputting a final feature. According to the invention, high-reliability full-automatic radiology report generation can be realized, the workload of radiologists is significantly reduced, and the standardization degree and clinical application value of the report are improved.