369 results about "Radiology studies" 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 relates to the technical field of medical image processing, in particular to a CT image intelligent analysis system for pneumonia auxiliary screening. The method comprises the following steps: firstly, preprocessing a chest CT image and detecting a candidate focus area; secondly, extracting a topological feature, a deep convolution feature and a texture statistical feature based on a persistent coherence theory from each candidate focus, and performing feature fusion through a multi-head self-attention mechanism to generate a unified focus representation vector; mapping the lesion characterization vectors to a pre-constructed radiology knowledge graph, adopting a graph neural network for reasoning, and outputting the pneumonia suspected probability and lesion classification of each lesion; and finally, performing fusion and uncertainty quantification on the analysis results of the plurality of focuses by adopting an evidence theory, and generating a comprehensive screening report. According to the method, complex-form lesions are effectively identified through topological features, accurate identification of lesion types is realized through knowledge graph reasoning, and diagnosis uncertainty quantification is provided through an evidence theory.

Disclosed herein is computer-implemented method for processing at least one image of a location of a body of a subject. The method may comprise obtaining the at least one image, and using a trained algorithm to classify the at least one image or a derivative thereof to a category among a plurality of categories comprising a first category and a second category. The classifying may comprise applying a image processing algorithm. The method may comprise, based at least in part on the classifying, designating the at least one image or derivative thereof as having a first or second priority (e.g., lower priority or urgency than the first priority) for radiological assessment if the at least one image is classified to the first or second category, respectively. The method may comprise generating an electronic assessment of the subject, such as a negative report indicative of the subject not having a health condition.

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 a training method of a cross-modal clustering guide image encoder, which combines comparative learning with a clustering algorithm, and seamlessly utilizes global and local features of a medical image and a radiology text. This approach facilitates more universal and robust medical visual representation learning. Image sub-regions and text sentences are regarded as basic units for global and local comparative learning. The global image and the global text features are respectively obtained by applying attention pooling to the local image and the local text features extracted by the encoder, and then are used for calculating global contrast loss. A cross-modal clustering algorithm utilizes local image-textual features obtained through cross-modal attention to generate additional negative samples, which are then used to calculate local contrast losses.

The invention discloses a medical image diagnosis report generation method and device, equipment, a medium and a product. According to the method, image features of a radiology image are extracted, a medical knowledge graph is encoded as priori knowledge to help identify abnormal areas in the image, and correlation among some disease expressions is considered through modeling of disease keywords. Integrating the extracted image features and the medical knowledge graph features by using a graph attention mechanism to obtain visual fusion features; and inputting the visual fusion features into a pre-trained target prediction model to finally obtain predicted continuous semantic space vectors, and further obtaining a generated radiology medical report through mapping of discrete words and the continuous semantic space vectors, so that medical image features and text disease keyword representations can be accurately aligned. And the accuracy of diagnosis report generation is further improved.

Presented herein are systems, methods, and non-transient computer readable media for determining predicted response scores of subjects. A computing system may identify a first feature set for a first subject to be administered with immunotherapy to address a condition. The first feature set may include one or more of: (i) a first radiological feature identified in a tomogram of a section associated with the condition in the first subject, (ii) a first immunohistochemistry (IHC) feature derived from an image of a sample associated with the first subject, and (iii) a first genomic feature obtained from gene sequencing of the first subject for genes associated with the condition. The computing system may apply the first feature set to a model. The computing system may determine, from applying the first feature set to the model, a predicted score identifying a response to the immunotherapy to be administered to the first subject.

The invention discloses a chest radiology medical report generation method based on a knowledge graph, and the method comprises the steps: obtaining a to-be-processed chest radiology image, inputting the to-be-processed chest radiology image into a trained medical report generation network, and obtaining a chest radiology medical report. The training process of the network comprises the following steps: acquiring a chest radiology medical report set; constructing a chest radiology knowledge graph; extracting text features, image features and knowledge features from the chest radiology medical report, inputting the text features, the image features and the knowledge features into a double-path knowledge enhancement gating module, aligning the text features and the image features, aligning the image features and the knowledge features, and calculating alignment loss; fusing the aligned features to obtain enhanced fusion features; inputting the enhanced fusion features into a classifier, and calculating classification loss; the fusion features are input into a decoder, a chest radiology medical report is generated, and generation loss is calculated; and network parameters are optimized through loss back propagation. According to the invention, the medical report generation quality and accuracy can be obviously improved.

Various methods and systems are provided for automatically recommending one or more radiology protocols based on an imaging examination order which includes both structured and unstructured data. In one example, a method includes receiving an imaging examination order requesting an imaging examination, wherein the imaging examination order comprises structured data and unstructured text, converting the unstructured text into one or more feature vectors, mapping the structured data and the one or more feature vectors to a standardized radiology protocol representation using an imaging examination order classifier, and mapping the standardized radiology protocol representation to a site-specific radiology protocol using a site-specific radiology protocol translator.

The invention provides an automatic focus identification and grading method for a medical image, and the method comprises the steps: carrying out the standardization of an obtained multi-modal original image based on anatomical constraint, and obtaining a standardized image; generating semantic enhancement features through a cross-modal feature compensation network based on the standardized image and associated radiological text description; performing dynamic feature adaptation processing on the semantic enhancement feature to generate a modal adaptive feature; performing context reasoning through a multi-scale feature interaction algorithm based on the modal adaptive features to generate context reasoning features; and lesion identification decoding processing is carried out on the context inference feature map, a lesion segmentation mask is generated, and the lesion segmentation mask is used for extracting lesion area feature parameters to carry out lesion classification. By adopting the method, the adaptability to the missing mode can be enhanced, and the focus identification and grading precision can be improved.

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.

Described herein are prediction models based on the transcriptomic, exomic, and / or radiological analyses on tissue samples to predict the likelihood of the original cancer (such as Hepatocellular carcinoma (HCC)) recurrence into the liver transplant. An example computer implemented method for predicting the likelihood of liver cancer recurrence 5 into a liver transplant includes receiving gene expression data related to a liver tissue sample for a subject having a liver cancer, inputting the gene expression data into a trained machine learning model, and predicting, using the trained machine learning model, a risk of recurrence of the liver cancer in the subject after liver transplantation.

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 present invention relates to a cognitive computing-assisted clinical decision support system designed to enable personalized neurological rehabilitation. The system acquires structured user data across clinical, anatomical, radiological, etiological, pathological, and rehabilitation domains to create individualized profiles. These profiles are mapped against a repository of historical cases using analog matching and similarity scoring to generate stratified, evidence-based rehabilitation recommendations. Real-time monitoring of rehabilitation progress is performed using global recovery and function outcome indicators, allowing for dynamic adjustment of treatment plans. Clinician intervention modules ensure safety, interpretability, and context-aware customization. The system incorporates a continuous feedback mechanism to refine future predictions and recommendations, making it increasingly adaptive over time. The invention improves rehabilitation outcome prediction accuracy, reduces recovery variability, and optimizes functional outcomes by transforming static rehabilitation models into intelligent, responsive, and personalized care pathways.

The invention relates to the technical field of deep learning, in particular to a radiotherapy plan dose distribution verification method based on deep learning, and the method comprises the following steps: collecting historical radiotherapy plan data, generating a physical reference dose field through a Monte Carlo algorithm, unifying the voxel resolution of an anatomical structure to 1 cubic millimeter, and normalizing the dose according to a prescription, data enhancement is carried out only by adopting translation and mirror transformation, trace Gaussian noise is added, and a physical information enhanced three-dimensional training data set is constructed. According to the method, a three-dimensional convolutional network is utilized to automatically learn a dose distribution rule of a historical high-quality plan, a physical constraint module is embedded to ensure that a prediction result accords with a radiology principle, a real-time clinical rule engine is combined to instantly identify and correct a violation hot spot cold region, and an uncertainty quantification technology is assisted to position a high-risk region, so that the accuracy of a prediction result is improved. Finally, minute-level full-automatic verification is achieved, executable optimization suggestions are output, and efficiency is improved by dozens of times while safety is improved.

Described herein are examples of industrial radiography systems that may control, or recommend, certain parameter values of a high resolution, continuous rotation, radiographic imaging process. By controlling, or recommending, the particular parameter values, it may be possible to mitigate certain synchronization issues that occur during the high resolution, continuous rotation, radiographic imaging process. With the synchronization issues mitigated, a user may be able to perform the high resolution, continuous rotation, radiographic imaging process at a high speed, without the loss of detail and / or blur that sometimes occurs due to the synchronization issues.

There is provided a method of identifying likelihood of cancer in a subject, comprising: feeding an input image of a subject depicting a radiological lesion with at least one of: (i) a diameter less than about 10 millimeters (mm), (ii) is visually similar to neighboring tissues, wherein visually similar comprises features extracted from the radiological lesion are statistically similar to features extracted from neighboring tissues surrounding and / or in proximity to the radiological lesion, and (iii) includes benign radiological characteristics, wherein benign radiological characteristics include features extracted from the radiological lesion are more correlated to benign features than to non-benign features, into a machine learning model trained to analyze the radiological lesion, and obtaining an indication of likelihood of cancer or non-cancer for the radiological lesion from the machine learning model.

A real-time artificial intelligence (AI) framework is provided for the automated analysis of radiological images and detection of disease, such as extracapsular extension (ECE) in prostate cancer. The system includes a dual deep learning architecture comprising a first convolutional neural network (CNN) for identifying diagnostically relevant image slices from three-dimensional MRI data, and a second CNN for classifying disease presence based on those slices. A preprocessing pipeline standardizes and harmonizes image input, and cropping algorithms isolate the region of interest for enhanced model performance. This framework enables scalable, high-accuracy diagnosis across various imaging modalities including but not limited to MRI, CT, PET, ultrasound, and diverse disease types, improving clinical decision-making and supporting integration into real-time radiology workflows.

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.

A method for processing data relating to a radiological examination of a patient by way of a determining device, comprises the steps of acquiring doses (Ci, ti) measured at a plurality of times ti, storing these time-stamped measurements of radiation doses, and acquiring at least one DICOM digital file containing information on the examination, wherein the method comprises the following steps: acquiring and storing at least one DICOM digital file delivered by the tomograph during or after a tomography; acquiring and storing time-stamped measurements of the doses detected via a scintillating fiber placed on the table, and time-stamped movements of the table; interpolating the measurements (Ci, ti) with data of the image (DICOM) in a common interpolated space and constructing a table (Ck, DICOMk) in the interpolated space; and determining a table of the average dose levels Tz in each slice T depending on the data (DICOMk, Ck).

A system and method provide artificial intelligence (AI)-based diagnostic guidance for a patient. The system receives captured radiological scan images of the patient, and input components such as results of physical examination of the patient and laboratory data of the patient. The system provides patient imaging information based on the recognized patterns of the received radiological scan images and the three-dimensional reconstructed scans, identifies problems based on predetermined AI criteria and deep-learning based on patient-specific patterns, responses, and the input components, determines risk factors of the patient which cause heath issues, provides probability estimation of diagnoses that predict probability of occurrence of the health issues to the patient based on the risk factors of the patient, prioritize the problems to a hot list based on the probability estimation of diagnoses.

A method for obtaining an irradiation map of a patient during interventional radiology, comprising:a learning phase consisting in submitting to a neural network a learning set comprising associations between a first input tensor comprising data of a first radiology image of a patient's intervention area, and first acquisition parameters of an interventional radiology device, and labels corresponding to an irradiation map obtained by simulation from the said first radiology image and the said first acquisition parameters,a prediction phase on a given patient, comprising the acquisition of a stream of second acquisition parameters of said interventional radiology device, the preparation of a second input tensor comprising data of a second radiology image of said given patient and of said second acquisition parameters, the submission of said second input vector to said neural network and the retrieval of an irradiation map prediction.

The invention provides a cerebral hemorrhage hematoma enlargement prediction system and method based on deep learning, and the system comprises a data obtaining module which is used for obtaining a baseline CT image and clinical data of a patient; the image preprocessing module is used for carrying out standardization processing on the CT image; the feature extraction module is used for extracting image features from the CT image by adopting a deep convolutional neural network; the feature fusion module is used for fusing the image features, the clinical features and the radiology features; and the prediction module is used for predicting the hematoma expansion risk based on the fused features. The system captures 3D space information to the maximum extent by extracting the maximum lesion level, shows high reliability, high interpretability and clinical availability in the aspect of predicting hematoma expansion, is remarkably superior to prediction of clinicians, achieves reasonable consistency of prediction probability and actual probability on multiple data sets, and has good application prospects. The system can be used as a clinical decision support system and has potential to improve patient prognosis.

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.

A system and method for determining a data quality of DICOM data are provided. Information related to a data ingestion of the DICOM data from a radiology device to a database may be received. Respective data qualities of the DICOM data at the set of data ingestion stages of the data ingestion may be determined using a set of data validation rules corresponding to a set of data ingestion stages of the data ingestion of the DICOM data from the radiology device to the database. A user interface that displays information related to the respective data qualities of the DICOM data at the set of data ingestion stages of the data ingestion may be generated.

In some embodiments provided herein is a generative foundation model trained over millions of health system-scale electronic health records along with web-scale medical text corpora to acquire knowledge of both medical practices and theories, and use of the generative model for rare disease diagnosis (including rare ophthalmic, diseases and rare systemic diseases), emergency condition identification (including ophthalmic emergencies and systemic emergencies), complex disease solving ("diagnostic puzzles"), or generating multimodal medical imaging reports (including ophthalmic images and radiology images such as X-rays and CT scans). In some embodiments, the generative model involves the use of language data, for pre-training, language data for supervised finetuning using a instruction tuning approach (e.g., QA pairs), and a human-machine hybrid evaluation strategy. In some embodiments, both the pre-training and supervised finetuning phases involve the use of a particular method of scaling to extend the context window. In some embodiments, the human-machine hybrid evaluation strategy involves language data for automated evaluations, as well as evaluations by generalists and by different specialists (e.g., ophthalmologists and radiologists) of varying levels of experience. In some embodiments, the generative foundation model, MetaGP, is used for unmet clinical needs through integration of medical and multimodal imaging data.

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.