86 results about "Radiology report" patented technology

The invention discloses a radiology report generation method based on semantic alignment, and belongs to the technical field of radiology report generation. The radiology report generation network constructed by the invention comprises a multi-scale feature extraction module, a cross-modal semantic alignment module and a Transform report generation module which are connected in sequence; the multi-scale feature extraction module comprises a convolutional neural network and a multi-scale local sparse attention module which are connected in sequence; the cross-modal semantic alignment module comprises an embedded layer, a multi-head attention module and a gating fusion module, wherein the multi-head attention module and the gating fusion module are connected with the embedded layer. The input end of the multi-head attention module and the input end of the gating fusion module are connected with the output end of the multi-scale local sparse attention module. The predicted radiology report generated by the radiology report generation method is good in quality.

Provided in the present invention are a radiology report generation method and apparatus, and a terminal and a storage medium. The method comprises: inputting a radiology image to be processed into a pre-trained dynamics priori network model, wherein the dynamics priori network model comprises a dynamic knowledge graph network, a prior knowledge network and a decoder; in the dynamic knowledge graph network, obtaining a dynamic knowledge graph on the basis of the radiology image, and obtaining a visual representation on the basis of the dynamic knowledge graph; acquiring prior knowledge information corresponding to the radiology image, and on the basis of the prior knowledge information and the visual representation, using the prior knowledge network to obtain a prior representation vector; and inputting the visual representation and the prior representation vector into the decoder to generate a radiology report corresponding to the radiology image. In the present invention, the radiology report is generated by combining the dynamic knowledge graph with the prior knowledge information, such that visual and textual biases caused by limited data availability can be better handled, thereby improving the report generation quality.

A neural network system is trained to generate textual reports (that is, medical reports, such as radiology reports) from one or more medical images, by fine-tuning a pre-trained neural network system (a visual language model, "VLM") operative, upon receiving an input comprising at least one image and a textual input, to generate a value indicative of a predicted likelihood of one or more candidate text continuations of the textual input. The fine-tuning of the neural network system is performed to reduce the value of a cost function which includes a first prediction cost term based on a first training database including first training datasets of at least one medical image and an associated text report, the first training datasets corresponding to first individuals. The first prediction cost term further includes a cost value for each individual, inversely dependent on a likelihood value of the associated textual report, conditioned on the at least one medical image, and created by the neural network system.

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 lung image segmentation method and apparatus, and a storage medium. The method comprises the steps of obtaining a lung image and radiology report information; preprocessing the lung image to obtain an initial image spatial feature and an initial image channel feature; preprocessing the radiology report information to obtain an initial space level text feature and an initial channel level text feature; performing multi-modal feature alignment processing by using the target cosine loss function to obtain a target alignment feature; constructing a space level multi-modal feature; multi-modal features of the channel level are constructed; according to a dual cross-modal self-attention mechanism, constructing a target multi-modal feature; and according to a preset semantic level number, performing adaptive gating fusion processing on the target multi-modal features on different semantic levels to obtain a lung image segmentation result. According to the method, lung image segmentation is realized, the accuracy and applicability are improved, and the calculation complexity is reduced. The method can be widely applied to the technical field of image processing.

The invention relates to the field of radiology department medicine and computers, and discloses a radiology report automatic generation method and system based on radiology priori knowledge, which comprises the following steps: constructing a radiology priori knowledge performing document dicing on the priori knowledge of a radiology report through a sliding window so as to construct the radiology priori knowledge base; data set reconstruction and fine tuning: constructing text data of a radiation report, and performing instruction fine tuning on the text data of the radiation report by using a large language model; construction of a comprehensive retrieval vectorization module: constructing the comprehensive retrieval vectorization module in combination with semantic similarity retrieval and full-text retrieval technologies; and automatically generating a radiation report by combining the matched radiation priori knowledge after passing through the comprehensive retrieval vectorization module. According to the system, the radiation prior knowledge base, the comprehensive retrieval vectorization module and the report generation module are integrated in advance, so that a doctor can generate a high-quality radiation image report according with a standard format only by inputting radiation diagnosis information.

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.

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 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.

Machine training is used to learn to generate findings in radiology reports. Rather than merely learning to output findings from an input, the machine training uses loss based on impression derived from findings to machine train the model to generate the findings. Once trained, the machine-learned model generates findings but the findings are more accurate or complete due to having used impression loss in the training.

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.

A method (100) of analyzing a medical report (34) presenting clinical content determined from one or more images (38) includes: extracting a text embedding (54) from the medical report: extracting an image embedding (52) from the one or more images; determining one or more content feature vectors from the text embedding and the image embedding; determining one or more style feature vectors from the text embedding; and at least one of: extracting one or more clinical findings contained in the medical report using the one or more content feature vectors; scoring the style of the medical report using the one or more style feature vectors; and / or converting the medical report to a target style using the one or more content feature vectors and one or more target style feature vectors different from the determined one or more style feature vectors.

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.

Systems and methods for identifying and presenting prior findings data in a radiology review workflow, based on natural language processing (NLP) of unstructured radiology report text, are disclosed. In an example, prior findings are generated with operations including: receiving text from a prior radiology report prepared for a patient in a prior radiology study; processing the text with a trained NLP engine to classify discrete prior findings in the prior radiology study that identify observed condition(s) and characteristics of the observed condition(s); and outputting or presenting the prior findings, such as in a radiology review user interface. In an example, the radiology review user interface displays the observed condition(s) and the characteristic(s) of the observed condition(s) to a user (e.g., a radiologist), and can apply sorting, filtering, or grouping based on pathology phrases or attribute words associated with the observed conditions or the characteristics of such observed conditions.

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.

Machine training is used to learn to generate findings in radiology reports. Rather than merely learning to output findings from an input, the machine training uses loss based on impression derived from findings to machine train the model to generate the findings. Once trained, the machine-learned model generates findings but the findings are more accurate or complete due to having used impression loss in the training.

In one embodiment, a method is disclosed that includes accessing radiographic images of an animal, wherein one or more first radiographic images of the radiographic images each depict the animal from one or more views and one or more second radiographic images of the radiographic images each depict one or more body parts of the animal; identifying a disease classification for the animal based on analysis of the radiographic images with a machine learning model; generating a diagnostic report for the animal based on the machine learning model, the diagnostic report including the disease classification and a natural language text radiology report; and sending instructions to a user device to present the diagnostic report.

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 is suitable for the technical field of medical image analysis, and provides a radiology report generation method based on a hierarchical knowledge integration framework. Through hierarchical knowledge injection and a cross-modal fusion module, the difference between image perception and text cognition is effectively reduced, and the consistency between the image and the text is ensured. Rich medical priori knowledge is utilized, and the accuracy and correlation of the report are enhanced. The method not only provides a new scheme for radiology report generation, but also shows wide potential in the field of medical image analysis. The method can accurately align images and texts, reflect key clinical information, and enhance the practicability and reliability of reports in combination with image features and medical knowledge. Besides, complex anatomical structures and pathological features can be processed, the problem of generalization caused by detail omission in the report is reduced, the integrity, continuity and real-time performance of the report are ensured, and the method has remarkable theoretical innovation significance and practical application value.

The present invention relates to the combination of imaging and reporting in medical imaging. Since the ultimate output of medical imaging is a radiology report, the quality of which depends to a large extent on the radiologist, there is a need for an integrated system for both medical imaging and reporting. Combine imaging and radiology reporting. Combine image acquisition, image reading, and reporting, thereby allowing feedback on the reading to control the acquisition, such that the final report is more comprehensive. Clinical findings that are normally associated with the report can be automatically used for feedback for further or continuous acquisition without the radiologist. Clinical identification can be used to determine what image processing to perform for reading, and / or to provide raw (i.e., non-reconstructed) scan data from the imaging system for image processing integrated with report generation.

Systems and methods for using a differentiable multi-agent Actor-Critic (DiMAC) for multi-step radiology report summarization. The tasks of extracting salient sentences and phrases are divided across two collaborating agents that are trained end-to-end using reinforcement learning (RL).

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.