152 results about "Lesion Identification" patented technology

The invention relates to the technical field of intelligent manufacturing, and discloses a target structure automatic detection method, device and equipment and a medium, and the method comprises the steps: obtaining scanning path planning data of a target detection structure, driving an ultrasonic probe to execute surrounding scanning motion, and collecting an ultrasonic image sequence and spatial pose data; dynamically adjusting a pressure application angle and a scanning speed based on force feedback information, fusing spatial pose data and an image sequence to perform three-dimensional reconstruction, constructing a three-dimensional geometric model of a target detection structure, extracting feature distribution data by applying an intelligent analysis model, generating a feature decision set, and performing feature extraction; and mapping the feature decision set to a three-dimensional coordinate system to construct an analysis report containing the feature type marks and the topological relation. Through fusion of force control scanning, image reconstruction and intelligent analysis, standardization and intelligence of a detection process are realized, image consistency and structure identification precision are improved, manual dependence is reduced, and comprehensiveness and reliability of lesion identification are enhanced.

The invention discloses an industrial robot image processing method based on image fusion, and relates to the technical field of intelligent aquaculture, and the method comprises the steps: collecting an original image in real time through deploying an image collection device integrating visible light, polarization and multispectral imaging, and extracting suspended matter density, water body light transmittance and illumination intensity change information; generating a first image set; suppressing suspension interference through image filtering and enhancement processing to obtain a first corrected image; extracting an aquatic product individual region, performing multi-source image fusion, identifying color deviation, texture interruption and reflection feature anomaly regions, and constructing a lesion candidate set; gray scale reconstruction, edge gradient and brightness normalization correction of a multispectral channel are executed based on illumination and reflection changes, and a high-quality fusion image is generated; and calculating a health anomaly probability coefficient of the target individual by using the depth recognition model, comparing the health anomaly probability coefficient with a threshold value, and recording a recognition result and collecting information if the threshold value is exceeded. The method can significantly improve the accuracy of aquatic individual lesion recognition.

The invention relates to the field of medical image processing, in particular to a lesion recognition method and system for non-staining biopsy cells, and the method comprises the steps: carrying out the imaging collection of the non-staining biopsy cells flowing at a high speed through the time sequence synchronous control of pulse laser and high-speed imaging, and obtaining an original cell image; performing image preprocessing on the original cell image to obtain a preprocessed cell image; performing multi-target cutting on the preprocessed cell image to obtain a standardized single cell image; extracting multi-scale features of the standardized single cell image based on a deep learning model to obtain a lesion recognition result corresponding to the non-staining biopsy cells; and based on a preset structured report generation algorithm and a statistical distribution modeling mechanism, performing structured integration on the lesion recognition result to obtain a structured cell diagnosis report. Through pulse laser imaging and deep learning feature extraction, automatic lesion recognition of non-staining biopsy cells is realized, a report is efficiently generated, and accurate diagnosis is assisted.

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 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 discloses a multi-modal medical image data analysis method and device, equipment and a medium, relates to the technical field of medical treatment, can be applied to a medical health business scene, and comprises the following steps: obtaining an image analysis task corresponding to multi-modal medical image data; the image analysis task is divided into a first computing power task and a second computing power task, the first computing power task is a preprocessing and real-time preliminary screening task, the second computing power task is a deep analysis task, and the output of the first computing power task serves as the input of the second computing power task; scheduling a first hybrid model deployed at the edge device side to execute a first computing power task, and scheduling a second hybrid model deployed at the cloud side to execute a second computing power task; and outputting a collaborative image analysis result of the first mixed model and the second mixed model for the multi-modal medical image data. According to the invention, the complex focus recognition sensitivity can be improved, and the misjudgment rate is reduced.

The invention relates to the field of medical image processing, in particular to an oral medical image precision optimization method based on multi-modal data. Comprising the steps of obtaining oral cavity CBCT, X-ray and MRI images, performing preprocessing and spatial registration, respectively extracting a three-dimensional skeleton structure, two-dimensional texture density and soft tissue features, realizing multi-modal feature weighted fusion through an adaptive fusion module based on an attention mechanism, and completing oral cavity structure segmentation and lesion detection by using a convolutional neural network. According to the method, multi-source heterogeneous image information can be effectively integrated, and the lesion recognition accuracy and segmentation precision are remarkably improved.

The invention relates to the technical field of medical training, and discloses a lumbar puncture simulation teaching system based on an AR technology, and the system comprises an AR content generation module which collects a lumbar medical image and carries out the tissue segmentation and focus recognition; performing three-dimensional geometric modeling based on the tissue segmentation result and the focus recognition result to generate a lumbar vertebra 3D model; the AR display interaction module develops an interaction function of the lumbar vertebra 3D model by using a Unity platform and AR equipment; interactive operation of the user is monitored, and feedback is conducted in real time; and the operation evaluation module is used for evaluating the interaction operation of the user and assigning an improvement suggestion according to an evaluation result. A three-dimensional visual scene is constructed through the AR technology, a user can observe the needle inserting process from different angles according to the complex anatomical structure of the lumbar vertebra part, and dynamic effects such as cerebrospinal fluid pressure change are simulated; in the lumbar puncture simulation training, the AR system can feed back operation data in real time, such as the needle inserting angle, the puncture strength and the puncture depth, and the user is helped to accurately master operation skills.

The invention discloses an intracranial SEEG electroencephalogram data analysis method and system based on artificial intelligence, and relates to the technical field of data analys.The method comprises the steps that original electroencephalogram signals are obtained, and electrode space coordinates and imaging information are synchronized; extracting robust features in a time-frequency domain through synchronous extrusion in combination with learnable chirp and multi-resolution attention; according to the electrode coordinates and the anatomical topology, topology sensing alignment is executed, and an anatomical function diagram is constructed; performing continuous time evolution modeling on a node state through a graph neural continuous dynamic structure, and introducing an energy conservation and event jump mechanism to realize focus transmission chain inference; estimating orientation information in different physiological contexts, and screening a stable direction as a prior constraint; and outputting a continuous risk curve according to propagation embedding, and generating a coverage-controllable confidence interval through online conformal calibration. Energy consistency, causal stability and space-time continuous modeling of SEEG signals can be realized, and the accuracy and interpretability of electroencephalogram focus recognition and clinical risk assessment are improved.

The invention discloses a magnetic resonance image target identification method based on multi-modal feature fusion, and the method comprises the steps: firstly obtaining multi-sequence magnetic resonance image data of the same object, the data comprising a structure sequence, a function sequence and a quantization parameter sequence, and carrying out the synchronous preprocessing; the pre-processed data is input to a feature extraction network to obtain a multi-dimensional feature representation. A cross-modal interaction map is constructed based on feature representation, interaction and fusion of different modal features are realized through an attention mechanism, and a fusion feature vector is obtained. And generating a focus candidate region set, and correcting the candidate region in combination with the related information of the patient to obtain a corrected focus region set. And inputting the corrected target area set into a discrimination network, and outputting a positioning result and a type result of the target. According to the method, through multi-modal feature fusion and map modeling, the accuracy and robustness of focus recognition are effectively improved, misjudgment caused by single-modal limitation is reduced, and the method has high clinical application value.

The invention provides a dual-order optimization self-adaptive diabetic mesh screening model generation method and lesion recognition equipment, and relates to the technical field of diabetic mesh screening, and the method comprises the following specific steps: collecting a plurality of fundus images and corresponding medical record data, carrying out fine processing and detailed labeling, presetting a machine learning detection model for training, and carrying out the recognition of the fundus images and the corresponding medical record data. The fundus image and the medical record data of the samples in the training set are used as input features, the corresponding label content is used as an output label, and dual-order optimization and self-adaptive adjustment are adopted to enhance the recognition capability of the model on lesion features; screening a model according to a category consistency coefficient between lesion types, a feature deviation coefficient between lesion features and a logic consistency coefficient of logic rules, and screening a dual-order optimization adaptive sugar mesh screening model by using samples in a test set. Clinical diagnosis can be accurately assisted, the missed diagnosis and misdiagnosis rate is effectively reduced, the problem that the feature recognition precision of a traditional model is insufficient is solved, it can be ensured that prediction conforms to clinical logic, and the reliability of the model is improved.

The application provides a crop disease precision identification method and system, and relates to the technical field of crop disease identification. The method comprises the following steps: establishing standard lesion feature image datasets of different crops and different organs; constructing a background feature database of common crop diseases; forming a crop type and organ identification algorithm model and an organ lesion identification algorithm model; determining the crop type and the organ of the crop in an image to be identified based on the crop type and organ identification algorithm model; determining the disease range of the image to be identified based on the organ of the crop and using the organ lesion identification algorithm model corresponding to the organ of the crop, and outputting a lesion disease with similar features; outputting a matched crop disease type based on the background feature database of common crop diseases; and obtaining a common disease type in the determined lesion disease and the determined crop disease type, so as to realize the precision identification of the disease. The application classifies and grades to form a structured disease identification process, and improves the identification precision.

The present application relates to medical image recognition technology field, specifically to a kind of based on frame segment stabilization and gray enhancement feature urinary system tumor screening method, comprising the following steps: extracting stable frame section, generating kidney reference template, establishing cortex gray contrast, combined template analysis enhancement feature, locate screening area and delineate boundary, generate tumor screening set.In the present application, by identifying image sequence gray level jump, avoid the interference of abnormal frame to image processing, combined with edge gradient change extraction structure boundary clarity minimum frame as anatomical structure reference image, using spatial position mapping to carry out dynamic analysis of cortex area gray change, make the direction, amplitude and duration of enhancement response more accurate, and then complete the automatic screening and boundary delineation of suspicious area, ensure that screening positioning result is continuous in time and consistent in space, improve lesion identification accuracy and objectivity of result, generate image set of urinary system tumor suspected area.

The present application relates to the technical field of lesion recognition, in particular to an automatic lesion recognition ultrasonic system for real-time monitoring, which comprises a delay path discrimination module, a gray level trend detection module, a texture feature screening module, a feature fusion sorting module and a region highlight labeling module. Based on continuous ultrasonic frames, the collected deep tissue echo path is analyzed, and the echo arrival time of each pixel point in the continuous frame is detected. The present application supports multi-type data fusion judgment through a comprehensive judgment process supported by multi-dimensional parameter collaborative screening, penetration behavior, gray level trend and texture aggregation. The regional abnormal priority sorting mode improves the hierarchy of lesion feature discrimination, provides partition directional recognition for local structural abnormalities and early micro-variation, and converts the feature judgment result into a high confidence region label by a weight aggregation method. The image output process automatically completes the real-time visual presentation of the high-risk area, improving the clarity and pertinence of the lesion region presentation.

An adaptable deep learning method is provided that delivers sound hepatic lesion identification in NETs, while significantly reducing human effort for data annotation and improving model generalizability for PET image quantification. A region-guided GAN (RGGAN) model conducts image-to-image translation between list-mode simulated PET images and real-world clinical data, while preserving semantic content of interest, e.g., lesions. The RG-GAN model is integrated with a lesion detection model into an end-to-end, unified framework for joint-task learning, such that the two models can benefit from each other. The RG-GAN translates the list-mode simulated data into real world-style images, which appear to be drawn from the real clinical PET image dataset, and feeds the translated images into the lesion detection model for training. In order to deal with the limited diversity of list mode-simulated PET image data, a specific data augmentation module is incorporated into the unified framework to improve model training.

The application relates to the field of data processing, in particular to a fish disease monitoring method and system based on feature image recognition. The method comprises the following steps: monitoring the water quality change in a target area in real time through a water quality sensor; collecting fish image information in the target area; performing behavior recognition on the fish image information to obtain fish behavior information of each fish object in the target area; performing lesion recognition on the fish behavior information, the fish image information and the water quality change through a fish lesion recognition model to obtain potential lesion information of each fish object; and generating lesion risk information of each fish object according to the potential lesion information and displaying the lesion risk information in a visual model corresponding to the target water area. The method can realize individualized potential fish lesion recognition for different types of fish, improve the accuracy of fish lesion monitoring, simplify the fish lesion monitoring process and further improve the monitoring efficiency of fish lesions.

The application provides a liver hydatid disease screening method and system based on plain CT and deep learning, belongs to the technical field of intelligent medical treatment, and realizes liver hydatid disease identification based on plain CT by combining a liver segmentation model, a liver hydatid lesion identification model and a liver hydatid screening model, reduces the risk of patients of intake of more radiation due to enhanced CT, reduces the risk of patients of allergy due to injection of contrast agent, and has strong clinical applicability; can realize patient-level diagnosis on the basis of slice-level identification, classification and segmentation, and has interpretability; in combination with the real situation of hydatid disease in clinical diagnosis and treatment and the clinical characteristics of hydatid disease, the identification accuracy is greatly improved, and in addition, the population including healthy people can be identified and screened, and the use range is wide.

The invention belongs to the technical field of artificial intelligence and medical image processing, particularly relates to an intelligent auxiliary diagnosis system for radiographic images, and aims to solve the problems of missed diagnosis and misdiagnosis caused by overload of doctors, large subjective difference and low early focus recognition sensitivity in the prior art. The system integrates a multi-scale feature extraction engine, a cross-modal alignment network, a pathological semantic reasoning unit and a diagnosis consensus generator, and realizes multi-modal image fusion analysis, dynamic knowledge reasoning and man-machine collaborative decision. A thermodynamic diagram and a feature contribution degree map are output through an interpretability visualization module, and a structured report conforming to clinical specifications is generated by a report automation engine, so that the diagnosis accuracy and efficiency are improved, the report period is shortened, and credible deployment of AI auxiliary diagnosis in a complex medical scene is supported.

The present invention relates to an integrated diagnostic system for deep endometriosis and / or adenomyosis, comprising three interconnected platforms and a method for analysing images generated by algorithms. The first platform combines patient medical history data with transvaginal ultrasound and / or pelvic magnetic resonance images, using algorithms to generate clear and detailed images of lesions. The method for analysing these images includes pre-processing, merging of clinical data and images, lesion identification, and comparison with a clinical database, resulting in a visual and descriptive analysis of the lesions, facilitating early and assertive diagnosis. The second platform is a communication network among specialist physicians, who perform a second reading of the images and issue a validated final report. The third platform is educational, offering personalised training and mentoring for imaging physicians using the images generated by the first platform to improve their diagnostic skills. The system reduces diagnostic time, increases accuracy, and contributes to the continuous professional development of physicians involved in the management of deep endometriosis and / or adenomyosis.

The present invention relates to a weakly supervised learning diabetic retinopathy grading and lesion identification method and system, the method comprising: S1: inputting a diabetic retinopathy fundus color image into a CNN network to obtain a feature map F; inputting F into a lesion point capture module based on weakly supervised object positioning, selecting to enter a branch module, calculating a score map, multiplying the score map with the feature map F to obtain a new feature map F'; S2: using an optimal network structure search module based on reinforcement learning to calculate the insertion position, selection probability, feature discarding threshold and retention threshold of the weakly supervised lesion point capture module in the CNN network, and finally outputting the feature map F. NA S3: Lesion attribute prediction and disease classification are obtained through attribute mining and lesion identification modules. S4: Based on the results of S3, the final lesion identification result is obtained through multiple iterative erasure calculations. The method provided by the present invention can improve the effectiveness of lesion capture and use lesion mining methods to determine lesion type, providing a basis for disease classification.

The present disclosure relates to a lumbar vertebra lesion identification method, device, medium and electronic equipment, wherein the method comprises: determining a lumbar vertebra image of a user, the lumbar vertebra image being a T2 weighted magnetic resonance image; inputting the lumbar vertebra image into a target detection model to obtain a lesion category of the lumbar vertebra of the user, wherein the target detection model comprises a down-sampling layer and a pooling layer, the down-sampling layer is used to extract feature data of the lumbar vertebra image, and the pooling layer is used to classify the feature data to obtain the lumbar vertebra lesion category of the user. Compared with the way of judging the lumbar vertebra lesion by artificial judgment and identifying the lumbar vertebra lesion by machine learning method in the related art, the error of identifying the lumbar vertebra lesion type can be reduced, and thus the accuracy of identifying the lumbar vertebra lesion can be improved.

The application relates to the field of image analysis, in particular to an AKI diagnosis method fusing multi-modal image features, which obtains kidney area MRI and CT images, extracts kidney pelvis candidate response boundary change information, completes area alignment and multi-scale response fusion through path aggregation, priority judgment and directionality adjustment, outputs a diagnosis graph and combines abnormal expansion direction to optimize an image set. According to the application, a high-response pixel set of a kidney pelvis area in an MRI image is extracted, pixel density and edge gradient change trend are combined, boundary change feature groups are established, the spatial distribution characteristics of potential lesion areas in the image can be finely described, and the initial accuracy of lesion identification is enhanced. The connectivity stability and path consistency of a response block are taken as the measurement basis, a path sorting mechanism in path construction is introduced, and the structural integrity and boundary controllability are improved in the feature path screening stage.

The present invention discloses an endoscope-based digital image enhancement method and system, which aims to solve the imaging limitations of traditional endoscopes in multi-lesion positioning and targeted intervention. The endoscope-based digital image enhancement method can perform endoscopic image acquisition, wireless transmission, abnormal anchor point identification and controllable terminal targeted intervention operations. The enhancement system for implementing the endoscope-based digital image enhancement method identifies suspected lesions through image analysis, the control unit issues a release instruction, causes the endoscope to release the controllable terminal and establish communication, the scheduling system navigates the controllable terminal to the abnormal anchor point according to the position information, acquires and transmits the digitally enhanced lesion image, and the judgment unit analyzes whether there is a target lesion with intervention value. This method can enhance the digital image in the human body cavity and improve the accuracy of lesion identification and intervention efficiency.

The application relates to the technical field of image recognition, and discloses an electronic laryngoscope image recognition method and system based on artificial intelligence. The method comprises the following steps: acquiring a continuous image sequence of a throat region, and recognizing a suspected lesion candidate region in the image through an identification model; acquiring a first image sequence, and judging whether each first candidate region in the first image exists in the sequence; if yes, calculating a motion vector consistency and rigidity degree parameter of the region based on the first image sequence, and judging whether the region is a lesion region according to the parameters; if no, acquiring a subsequent second image sequence, and judging whether the disappeared candidate region appears again; for the candidate region appearing again, calculating related parameters of the candidate region by comprehensively considering the first and second image sequences, and finally judging the candidate region by combining image features. The application can improve the accuracy and reliability of electronic laryngoscope image lesion recognition.

The application discloses a kind of B ultrasound image's lesion identification method, device, medium and equipment.The application is first obtained by real-time acquisition technology continuous multiple frames B ultrasound image sequence, then utilize a pre-set lightweight convolutional neural network to these images feature extraction, to obtain multiple continuous frame single frame feature map.Next, these single frame feature map will be based on the time sequence continuity of image, a pre-set fusion window and a group of preset weight feature fusion, to generate multiple fusion feature representation.Finally, utilize a lightweight inference network that is optimized by quantization and pruning, to these fusion feature representation carries out lesion identification, to obtain the lesion identification result of continuous multiple frames B ultrasound image.This process not only improves the accuracy and real-time of identification, but also optimizes the energy consumption and computing efficiency of low-end mobile edge device.

The invention discloses an abdominal surgery image data analysis method and system, and relates to the technical field of data analysis of reinforcement learning, and the method comprises the steps: carrying out the feature classification of abdominal surgery image data, and obtaining an independent part representing each organ; carrying out feature decomposition on the abdominal surgery image data in each independent part through reinforcement learning, and constructing an association network between the independent parts according to a feature decomposition result; integrating the associated network into the knowledge graph between the independent parts to obtain a featured knowledge graph; in the featured knowledge graph, triggering a feature reaction through a variation mechanism; and performing data analysis according to the feature reaction to generate a final analysis result. According to the method, the analysis precision of the multi-organ image data is improved, the capabilities of feature decoupling, variation response modeling and focus recognition are achieved, and the clinical interpretability and application effectiveness of the analysis process are enhanced.

The invention relates to the technical field of biomedical engineering, and provides a tongue body motion form reconstruction and neuromuscular lesion recognition method, which comprises the following steps: S1, acquiring a multi-modal original signal by using a flexible electrode array attached to a lingual surface, and recording a system timestamp; s2, performing targeted filtering, artifact removal and feature extraction processing on the multi-mode original signal; s3, reconstructing a local dynamic curved surface of the lingual surface by adopting a preset model, and inferring an overall position and posture sequence of the tongue body in combination with an IMU signal; s4, a joint optimization objective function is constructed, and a tongue body three-dimensional dynamic form sequence is obtained through iterative solution; s5, performing motion unit action potential MUAP decomposition and feature extraction on the surface electromyogram signal sEMG; and S6, constructing lesion feature vectors based on the multi-dimensional abnormal features, and analyzing and generating a tongue lesion risk map TDM through a machine learning model. The defects that real motion reconstruction, form inference and neuromuscular lesion positioning of the tongue cannot be achieved through an existing tongue detection technology are overcome.

An image processing device 1 comprises: an identification result reception unit 102 that receives a lesion identification result a from an image; a text generation unit 102a that generates, as text, basis information of the identification; a classification unit 103 that classifies constituent elements of the text into an object to be emphasized and an object not to be emphasized; an emphasis unit 104 that emphasizes the object to be emphasized; and an output unit 105 that outputs at least the object to be emphasized in the text to a monitor.

The present application relates to a kind of based on multimodal ultrasound time series data lesion identification method.It includes: obtaining the multimodal target time series data generated by ultrasound scanning to organ, the multimodal target time series data includes at least a group of multimodal data to be examined, multimodal target time series data is loaded to lesion instance segmentation model, to utilize the lesion instance segmentation model to carry out lesion segmentation identification processing to multimodal target time series data, and output the lesion instance state information corresponding to current multimodal target time series data;When it is determined that there is lesion in multimodal target time series data by lesion segmentation identification processing, then the lesion instance state information at least includes the category of each lesion.The present application can realize the continuous detection identification to lesion, improve the precision and reliability of lesion identification.

The invention discloses a bladder cancer early screening method based on plain-scan CT and MRI time sequence fusion, which realizes high-precision lesion recognition and positioning under the condition of low-dependence enhanced scanning through knowledge distillation and bimodal feature fusion. The method comprises the following steps: constructing a multi-modal data set including plain scanning, arteriovenous phase enhanced CT and MRI; a key frame sparse annotation and linear interpolation complementation strategy is adopted to reduce the annotation cost; performing preprocessing such as window level adjustment, normalization and zooming on the image; a CT / MRI recognition and positioning network is constructed, each sub-network comprises a 2D positioning branch and a 3D classification branch, and the characterization capability of plain scanning data is enhanced through a teacher-student knowledge distillation mechanism; and finally, integrating the bimodal information through a feature fusion module, and outputting a classification and positioning result. According to the method, time sequence consistency and modal consistency constraints are introduced, the recognition robustness of small focus and unclear boundary areas is improved, the manual film reading burden is remarkably reduced, the screening efficiency and safety are improved, and the method is suitable for early large-scale screening scenes of bladder cancer and has important clinical popularization value.