230 results about "Image diagnosis" patented technology

The invention discloses a multi-modal X-ray image diagnosis report generation method based on reinforcement learning optimization. The method comprises the following steps: acquiring a multi-source public data set and a private data set in a specific range; performing format standardization processing on the X-ray image, performing normalization processing on text information, and constructing a structured public training data pair and a private enhanced data pair; training an end-to-end multi-modal neural network by using the public training data pair in a supervised fine tuning mode; carrying out reinforcement learning on the first-stage end-to-end multi-mode neural network by adopting a strategy gradient algorithm optimization mode of GRPO and using candidate reports and weighted rewards to obtain a second-stage end-to-end multi-mode neural network; and generating a diagnosis report by using the two-stage end-to-end multi-modal neural network. The method is suitable for personalized application scenes of X-ray image diagnosis, can quickly adapt to specific clinical requirements on the basis of limited professional data, and has good practical value and popularization prospect.

The invention discloses a brain tumor image diagnosis large model pre-training method, diagnosis method and system, and the method comprises the steps: obtaining the image data of a brain tumor patient and a corresponding diagnosis text, and the image data comprises a plurality of sequences; constructing a visual unified model, carrying out complete sequence standard training and missing sequence distillation training on the visual unified model by utilizing the image data, learning unified visual representation of the image data of any sequence combination, and taking characteristics of the complete sequence image data as teacher characteristics in the missing sequence distillation training; the features of the missing sequence image data serve as student features, and distribution alignment of the student features and the teacher features in the feature space is restrained; constructing a visual language model, taking unified visual representation output by the trained visual unified model as input based on a multi-task target, and training the visual language model in combination with the diagnosis text; the trained model can effectively process the sequence missing condition, and the accuracy and robustness of diagnosis are improved.

The invention relates to the technical field of medical image processing, in particular to a neurosurgery image diagnosis method and system based on image processing, and the method comprises the following steps: obtaining gray matter edge nodes of a triaxial section, constructing a symmetric path unit, collecting an edge direction vector, and generating a direction trajectory diagram; and extracting continuous slices, constructing a rotation track sequence, identifying an abnormal region, filling gaps, combining path voxels, and dividing spatial levels to generate an image structure chart. According to the method, the grey matter edge nodes in the three-axis tangent plane are obtained, and the node paths with the symmetrical characteristics are screened out according to the space projection trend, so that the continuous region of the structure can be accurately recognized, the direction vectors in the continuous slices are extracted, the direction mutation region in the track is recognized, and the jump and fracture performance of the structure can be timely captured; and the abnormal region is accurately labeled, so that higher-dimensional expression and finer-grained recognition of the neural structure are realized, and spatial modeling and visual analysis of complex neuropathy are effectively supported.

The invention discloses a diabetic foot early-stage detection method combining infrared and visible light imaging, and relates to the technical field of diabetic foot medical imaging diagnos.The diabetic foot early-stage detection method comprises the steps that multi-mode image collection and standardization processing are conducted, infrared images and visible light images are synchronously collected, and standardization processing such as size normalization is conducted; image registration and space alignment are carried out, marking points are set based on foot anatomical features, and feature extraction, mismatching point elimination and transformation matrix calculation are carried out; extracting and screening multi-dimensional features, extracting temperature and structural features, and screening by using a Relief-F algorithm; feature lesion recognition and classification are fused, and lesion probability is output through a double-branch convolutional neural network; carrying out detection result verification and feedback optimization, and comparing a clinical diagnosis optimization model; and generating a detection report and storing data, and generating a report containing the fused image. The early lesion detection precision is improved through multi-modal fusion, individual and environment differences are adjusted and adapted in a personalized mode, and reliable technical support is provided for clinic.

The invention relates to the technical field of cloud computing analysis, in particular to a multi-modal data fusion AI cloud computing analysis system. Comprising an ethical conflict resolution module, an antagonistic modal purification module, a phase change critical energy consumption optimization module, a multi-modal time sequence calibration module, a federal modal distillation module, a metadata differential algebraic management module, a quantum-classical hybrid fusion module and a dynamic unloading strategy execution module. And the ethical conflict resolution module is used for ethical analysis and dynamic liability chain tracing. Through cross-modal deep interaction, dynamic ethical conflict resolution, antagonistic noise suppression, energy consumption-precision optimization and other technologies, the utilization rate, the system robustness and the calculation efficiency of multi-modal data are remarkably improved, the ethical and safety problems are solved, a comprehensive solution is provided for multi-modal AI application, and the multi-modal AI algorithm has good application prospects. The method can be flexibly applied to the fields of automatic driving, e-commerce customer service systems, medical image diagnosis, industrial Internet of Things, smart agriculture and the like.

On the basis of determination by image inspection that an image defect is generated and a predetermined condition being satisfied, an instruction unit for issuing an image diagnosis instruction is displayed on a screen of an inspection result, and image diagnosis is executed in response to an operation on the instruction unit.

The invention discloses a thyroid ultrasound image diagnosis method based on deep learning, and the method comprises the following steps: collecting a thyroid ultrasound original image set, and carrying out the preprocessing; performing focus segmentation on the standardized thyroid ultrasound image set; performing morphological constraint and boundary refinement; calculating the blood flow direction, blood flow velocity and blood flow power of each thyroid focus area and neighborhood; generating a preliminary fusion feature map based on a feature adaptive deep learning network, and fusing the preliminary fusion feature map with the thyroid focus blood flow feature vector set; obtaining a thyroid focus detection list through thyroid focus benign and malignant discrimination branches; and generating thyroid focus structured diagnosis data records based on the thyroid focus detection list, and writing the thyroid focus structured diagnosis data records into a computer-aided diagnosis system. According to the method, deep learning and multi-modal blood flow features are fused, intelligent diagnosis of the thyroid focus is achieved, and the method has the advantages of being high in precision, high in interference resistance and structured in result.

The invention discloses a medical analysis method and system based on a multi-modal large language model and chain reasoning, and belongs to the crossing field of artificial intelligence and medical image analysis. The method comprises the following steps: firstly, carrying out preprocessing and modal alignment on medical multi-modal data to obtain image visual features and text embedding vectors of the same vector spatial dimension; capturing visual evidence in a structured pathological feature form through a self-excitation mechanism; on the premise of visual evidence, a thinking chain text containing causal logic is generated through evidence anchoring chain type reasoning; and finally, performing vision and text dual consistency verification on the thinking chain text, and outputting an analysis conclusion according to a result, or triggering a negative feedback correction mechanism to regenerate the thinking chain text. According to the method, medical reasoning full-process logic visualization is realized, medical illusion is eliminated, the diagnosis accuracy of complex cases is improved, and the method is suitable for medical scenes such as medical visual questions and answers and image diagnosis report generation.

Systems and methods are provided herein for developing and deploying active-prompt-tuned, domain-specific image diagnosis and categorization applications. Processes of the present disclosure may control and provide for human-confirmable diagnostics from images, based on controlled instructions provided to vision-language models. The controlled instructions may be developed by systems provided herein, which generate system prompts and example prompt sets using active prompt tuning approaches.

The invention provides a liver tumor early diagnosis method and system based on artificial intelligence, and relates to the technical field of biomedical engineering, and the method comprises the steps: obtaining original information metadata related to the liver, the original information metadata comprises image data original information metadata and biomarker original information metadata, the image data comprises liver CT (computed tomography) and MRI (magnetic resonance imaging) image data, the biomarkers comprise serum tumor marker levels, and the physiological and environmental factors of the patients comprise age, gender, dietary habits and genetic backgrounds of the patients. According to the method, the problems that a tumor in a small and complex area is easily missed or misdiagnosed in the prior art are solved, the path density is predicted through denoising, segmentation and feature extraction of image data preprocessing in combination with a path simulation model based on a graph theory and a Monte Carlo simulation method, and a potential tumor area can be accurately judged.

The invention relates to the technical field of medical image intelligent diagnosis, and provides an interpretable lung CT image intelligent diagnosis method and device, and the device comprises an image calling module, an image processing module, an image analysis module and a result display module. According to the method, identity information of a patient is read to automatically verify and authorize, and an electronic CT image is called and stored in a data storage card through an encryption transmission channel. And the image processing module performs denoising, enhancement and interpretability analysis on the original image to generate a thermodynamic diagram to assist in decision making. The image analysis module completes lesion detection and classification, verifies the consistency between a model decision and doctor experience through a thermodynamic diagram, and optimizes network parameters in combination with feedback data. And the result display module displays a diagnosis result and a thermodynamic diagram in real time, and supports multi-terminal cooperation and data archiving. The device supports data sharing and remote consultation, the efficiency and convenience of diagnosis are improved, and the wide application prospect of artificial intelligence in the field of medical diagnosis is shown.

The invention relates to the technical field of medical image diagnosis, and discloses a tooth and fracture line recognition treatment method based on the combination of an AI technology and a CBCT image, and the method comprises the steps: obtaining and preprocessing the CBCT image, and carrying out the multi-scale analysis and recognition of a tooth structure, a microcrack and a fracture line through a first AI model. And the second AI model combines the identification result and the patient characteristics, and generates a personalized treatment scheme through multi-objective optimization. Clinical feedback is used for continuously iteratively optimizing double models, and the diagnosis and treatment precision and effect are improved. The system comprises an image data acquisition unit, an image data preprocessing unit, a tooth and fracture line identification unit, a personalized treatment scheme generation unit and a feedback and optimization unit. Through AI and CBCT image fusion, accurate identification of teeth and fracture lines is realized, a personalized treatment scheme is recommended in combination with individual features of a patient and a multi-objective optimization algorithm, rapid response is realized, a closed-loop feedback mechanism continuous optimization model is established, and diagnosis and treatment precision, efficiency and individualization level are remarkably improved.

The invention provides an imaging department doctor training method and system based on generative artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: calling a predetermined case parameterization strategy to carry out the parameterization processing of a real-time case, and obtaining a real-time parameter feature; extracting a first case in the image database, and obtaining a first parameter feature of the first case; generating a virtual case in combination with the real-time parameter features and the first parameter features, and introducing medical knowledge graph analysis to obtain a case reasonable coefficient of the virtual case; if the case reasonable coefficient is in a preset coefficient threshold value, constructing a virtual clinical environment of the virtual case; in a virtual clinical environment, image training is carried out on a target doctor in combination with a virtual case, and the problems that an existing image department doctor training system depends on fixed textbooks and standardized training cases and lacks a dynamic adjustment mechanism based on a real-time case, so that dynamic deduction and immersive interactive learning of doctors are difficult to realize, and the training efficiency is poor are solved. And the technical problem that the image diagnosis skill is difficult to improve efficiently is solved.

The invention provides a comprehensive solution for a medical ultrasonic image diagnosis task, which comprises two innovative parts, namely a CAPT parameter efficient fine tuning method based on a convolution adapter and embedded prompt tuning and a model lightweight method based on evolution pruning. Aiming at double challenges of scarcity of data and difficulty in model adaptation in the field of medical ultrasonic images, on one hand, the CAPT method introduces a convolution module into a traditional adapter to enhance the fine-grained feature extraction capability by fusing adapter tuning and prompt tuning mechanisms, and dynamically generates a prompt vector embedded with attention calculation by using adapter learning context information; the adaptability and the small sample generalization ability of the Transform model in an ultrasonic image classification task are obviously improved; on the other hand, aiming at a model edge deployment requirement, designing an evolution pruning algorithm, taking DenseNet-121 as a basic framework, inheriting parent strong neuron connection by adopting an asexual reproduction mechanism, and through a channel importance evaluation method for coupling a BN layer scaling factor and a weight L1 norm and in combination with a sparse evolution strategy guided by environmental constraint, constructing a model edge deployment model. And the classification precision is maintained, and meanwhile, the model parameters and the calculated amount are greatly compressed. The two technologies respectively break through the technical bottlenecks of medical image diagnosis from two dimensions of parameter efficient fine tuning and model structured pruning, the former improves the adaptability of the model field through feature extraction optimization, and the latter balances network lightweight and performance stability by means of an evolutionary algorithm. And an innovative technical path is provided for efficient deployment of an ultrasonic image auxiliary diagnosis system in a data limited scene and an edge computing environment.

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.

The invention discloses a medical image privacy protection-oriented structure perception adaptive steganography method, which comprises the following steps of: in a training stage, generating a diagnosis related region mask based on a pre-trained IMIS-Net, extracting an embeddable suppression mask, and obtaining an initial embedding probability under the minimum distortion constraint; and the self-adaptive adjustment of structure perception is carried out on the pixel-level embedding probability in combination with a reinforcement learning normal form, so that the ROI is protected preferentially and a high-risk region is inhibited on the premise of meeting the set capacity, and the steganalysis resistance and perception quality are improved. And after the training is completed, in a reasoning stage, the masks and probabilities are firstly generated or quoted, then action probabilities are obtained through a trained strategy network, then probability consistency is mapped into STC cost, real embedding is completed by the STC, and a secret-containing medical image is obtained. According to the invention, on the premise that the availability of image diagnosis is not reduced, safe embedding and hidden transmission of sensitive information are realized.

The invention relates to the technical field of computer vision and industrial equipment fault diagnosis, in particular to a fault image diagnosis system for a remote controller of a bolter miner driven by a CMC chip. Comprising a data sensing module for synchronously acquiring a CMC chip internal logic state vector and a real-time observation image, and preprocessing to obtain target visual data; the generative reconstruction module maps the logic state vector to a high-dimensional space to generate an ideal feature map; the visual analysis module is used for extracting an observation feature map through a convolutional neural network, generating an interference distribution mask based on frequency domain texture statistics, and identifying an unstructured environmental noise region; the difference analysis module is used for calculating a semantic residual image by combining the ideal feature map, the observation feature map and the interference distribution mask, and generating an image difference analysis result; according to the method, ideal features are generated by using the internal logic state vector of the CMC, and the residual calculation of the coal dust noise area is shielded by the interference mask, so that the false alarm caused by the environmental noise is effectively eliminated, and the closed-loop verification that what you see is what you control is realized.

The invention provides a pneumonia CT image diagnosis model training method, diagnosis method and equipment, and the training method comprises the steps: inputting a 3D chest CT image into a multi-task deep learning model, enabling a shared encoder in the model to extract multi-scale feature data, and enabling a connection module and a decoder to obtain pneumonia focus region prediction result data according to the multi-scale feature data, the classification head obtains pneumonia diagnosis type prediction result data according to the multi-scale feature data; determining the joint loss of the model in the current iteration round and updating model parameters; and if the current multi-task deep learning model satisfies a training termination condition, outputting the current model as a pneumonia CT image diagnosis model. According to the method, the problems of low model feature utilization rate and low pneumonia diagnosis process efficiency caused by incapability of simultaneously completing focus segmentation and type classification due to task simplification of an existing pneumonia diagnosis model can be solved.

The present invention discloses an image target detection method, apparatus, device, and medium based on adaptive enhancement. The method comprises: obtaining input images captured by cameras with different viewing angles, and performing image diagnosis using a camera degradation fingerprint library to obtain an image diagnosis result; based on the image diagnosis result, dynamically selecting an image processing combination using a rule engine, and performing image enhancement processing on the input image using the image processing combination to obtain an enhanced image; performing multi-view feature fusion on the enhanced image using a Transformer-based feature fusion architecture to obtain a fused image; performing target detection on the fused image to obtain a target detection result, and constructing an image target detection model based on the result; lightweight deployment of the image target detection model using model compression and dynamic jump inference technology, and performing target detection using the deployed image target detection model. The present invention can solve the problems of poor imaging quality and low target detection accuracy of old cameras.

The invention provides an adversarial sample repairing method based on fusion of a diffusion model and a generative adversarial network, and belongs to the technical field of artificial intelligence security. By constructing a dual-model collaborative optimization framework, the progressive denoising capability of a diffusion model and the discriminant supervision advantage of a generative adversarial network (GAN) are integrated, and efficient removal of disturbance in adversarial samples and accurate reservation of original semantic information are realized. According to the invention, PGD, Camp; the method has the advantages that the repairing accuracy of high-intensity adversarial attacks such as GAN and W is obviously better than that of a repairing method independently using a GAN or a diffusion model, meanwhile, the method has universal repairing capacity, repairing of various adversarial samples can be achieved through self-adaptive weight adjustment, the limitation of a single scene is broken through, repairing of multi-modal data such as images (RGB / gray), texts (adversarial sentences) and video frames is supported, and the method is suitable for large-scale popularization and application. The method can be widely applied to safe and sensitive scenes such as automatic driving, medical image diagnosis and face recognition.

The invention relates to the technical field of medical image artificial intelligence, in particular to a multi-prototype cross-modal contrast learning fetal heart ultrasound image intelligent diagnosis system which comprises the steps that a to-be-detected fetal heart ultrasound image is obtained through an obtaining module, and a processing module inputs the to-be-detected fetal heart ultrasound image into a pre-trained image diagnosis model; the method comprises the following steps: performing feature extraction on a to-be-detected fetal heart ultrasound image, performing cross-modal multi-prototype comparison according to a feature extraction result to obtain a diagnosis category and a most matched image-text prototype pair, outputting the diagnosis category and the most matched image-text prototype pair by an output module, and finally performing CHD classification and screening according to the diagnosis category and the most matched image-text prototype pair. The problem that the model generalization ability is insufficient due to the fact that a single prototype center is mainly relied on to represent each category and the significant difference of the same category under different conditions is neglected is solved, and multi-category CHD intelligent screening is achieved by utilizing unlabeled images and a small amount of text priori on the premise that a large number of graph-text pairs are not needed.

The invention relates to the technical field of medical image processing, and discloses a temporomandibular joint three-dimensional reconstruction system based on joint imaging diagnosis. The system utilizes CBCT equipment to collect temporal-mandibular joint tomographic image data, and extracts facial feature sites and maxillofacial joint standard model approval sites through a multi-scale residual network. And carrying out space registration by adopting an affine transformation and nonlinear optimization algorithm to generate a synchronous correction parameter, further carrying out deformation interpolation on the standard model of the maxillofacial joint to construct a maxillofacial model, and adjusting the position of a condylar process. And based on the deformed maxillofacial model, generating an occlusal plate structure model by using an isogeometric analysis method, and iteratively optimizing the degree of fit through an edge calculation frame. The system can accurately reconstruct the three-dimensional model of the temporomandibular joint, optimizes the adaptation of the biteplate, provides powerful support for the diagnosis and treatment of temporomandibular joint diseases, and facilitates the improvement of the diagnosis and treatment level.

An image diagnosis system is configured to: scan paper before an image for image diagnosis is formed by an image forming apparatus; scan the paper after the image is formed by the image forming apparatus; and perform the image diagnosis by classifying at least one of a fault due to the paper, a fault due to the scanning, or a fault due to the image formation from the other fault, based on a scan image of the paper before the image is formed and a scan image of the paper after the image is formed.

The application provides an ophthalmic image diagnosis method and system based on multi-modal imaging cooperation, and relates to the technical field of medical image diagnosis. First, the OCT, fundus camera and ultrasonic original image data of an ophthalmic examination object are acquired, and multi-modal dynamic correlation mapping results are obtained through dynamic correlation and trend correlation processing. Cross-modal lesion feature progressive mining and interactive verification are performed to obtain a cross-modal lesion correlation feature set. Multi-modal cooperative diagnosis reasoning and weight feedback optimization are used to generate an ophthalmic disease reasoning result containing disease types, lesion dynamic distribution and reasoning confidence. Finally, an ophthalmic diagnosis report with dynamic labeling and confidence explanation is generated. The application comprehensively utilizes the advantages of various image technologies to improve the accuracy of ophthalmic image diagnosis.

The application discloses a kind of based on CBCT image data evaluation implant stability classification model and the construction method and device thereof.First, through the Mobilenetv2-DeepLabV3+ network after training in cross-sectional image implant is segmented, then in combination with the knowledge of oral implantology and segmentation result corresponding implant around bone image is extracted on cross-sectional image, finally using the deep residual network Resnet-50 after training is completed classification, obtains implant stability evaluation result.Test evaluation, the model of the application has higher diagnostic performance, the time consumption of evaluation is short, accuracy is high (>90%), can be effectively used for implant stability evaluation.The classification model for evaluating implant stability of the application has important significance for better guiding further implant restoration treatment, and provides important reference for artificial intelligence used for image diagnosis.

A medical image diagnosis apparatus according to an embodiment includes a processing circuit. The processing circuit is configured to obtain three-dimensional data related to a target site. The processing circuit is configured to generate a three-dimensional model of the target site by using the obtained three-dimensional data. The processing circuit is configured to calculate positions of one or more recommended cross-sections to be set for the target site, on the basis of information about the size of the target site obtained by using the three-dimensional model. The processing circuit is configured to cause a display device to display the positions of the one or more recommended cross-sections.

The invention relates to the field of CT image diagnosis, in particular to a comparison device for CT image diagnosis, which comprises a base, an observation assembly arranged at the upper end of the base, a placement assembly arranged in the observation assembly, a fixing assembly arranged on the inner wall of the observation assembly, an irradiation assembly arranged at the upper end of the base, and a rotating groove arranged in the rotating groove. A rotating groove is formed in the upper surface of the base, a fixing ring is fixedly connected to the inner wall of the bottom of the rotating groove, a lantern ring is connected to the outer wall of the fixing ring in a sleeving mode, a rotating box is fixedly connected to the outer wall of the lantern ring in a sleeving mode, and openings are formed in the outer wall of the rotating box. Then a transparent inserting plate is pushed to move towards the interior of the rotating box, the transparent inserting plate drives a push rod to approach an L-shaped rod, the inclined face of the L-shaped rod pushes a push plate, the push plate pushes the push rod, the push rod pushes a clamping block to extrude the CT film to be attached to a baffle, then the CT film is fixed, and the CT film is prevented from sliding off.

The invention discloses a tumor specific signal analysis device and equipment for uncertain pulmonary nodules and a storage medium, and relates to the technical field of electrical digital data processing.The device constructs a LungTCR database containing lung cancer tissue enrichment type and blood enrichment type CDR3 sequences through TCR beta chain sequencing and IMGT database comparison, and the LungTCR database is used for analyzing the lung cancer tissue enrichment type and blood enrichment type CDR3 sequences. A basis is provided for tumor specific signal recognition; a Needleman-Wunsch algorithm and an editing distance threshold value are adopted for screening, so that high-precision sequence matching is realized; through quantification of lung cancer tissue scores, blood scores and mutation specificity scores, a TCR feature group with biological significance is formed; and finally, the TCRnodseek plus model trained by clinical and image features is combined, a reliable malignant probability prediction value can be output, the model AUC can reach 0.90 or above, and the model is obviously superior to traditional imaging diagnosis and tumor markers.

This invention discloses a cross-modal medical image segmentation method based on pathological anchoring. Using pathological priors as semantic anchors, this method integrates cross-scale collaboration and uncertainty perception in a closed-loop prompting refinement process. This achieves interpretable suppression and robust learning of cross-modal domain shifts, forming a closed-loop path from structure to semantics to noise control. Under strong cross-modal zero-sample settings such as multi-source ultrasound cross-domain segmentation and color dermoscopy, stable and consistent improvements are achieved, manifested in more precise boundaries, better calibration, and more robust generalization. This provides technical support for disease image diagnosis.

To provide a diagnostic imaging device, a diagnostic imaging method, a program, and a storage medium, which are not easily affected by unnecessary matters other than a diagnosis object part existing in an image extracted from a crop image when acquiring a growth index and the resolution of the crop image.SOLUTION: A diagnostic imaging device includes an object pixel determination unit configured to divide a crop image into a plurality of divided images and determine object pixels including at least a part of the crop with each of the plurality of divided images as an input, and a feature quantity diagnostic unit configured to output a feature quantity relating to the growth state of the crop using the object pixels.SELECTED DRAWING: Figure 3