68 results about "Nodule detection" patented technology

The invention discloses an early lung cancer screening method based on transfer learning. The method comprises the following steps: S1, obtaining preprocessed three-dimensional volume data; s2, generating a multi-scale enhanced feature map; s3, obtaining a preliminary micro-nodule detection result; s4, obtaining a first-stage optimization model; s5, performing second-stage training on the first-stage optimization model by adopting a double-normal-form transfer learning strategy to obtain a transfer optimization model; s6, embedding an adversarial domain adaptive module in the migration optimization model to obtain a cross-domain robust model; and S7, in a clinical reasoning stage, outputting a micro-nodule detection result for the new chest low-dose spiral CT original image data by using the cross-domain robust model. According to the invention, the model can adaptively emphasize the local gradient difference between the lung micronodule and the background texture, thereby improving the discrimination capability of the weak boundary region.

The invention discloses a three-dimensional pulmonary nodule detection method and system based on morphological adaptation convolution, and the method comprises the steps: obtaining a chest CT image sequence, and carrying out the preprocessing of all chest CT images in the chest CT image sequence; according to the three-dimensional pulmonary nodule detection method and system based on form adaptive convolution, scale factors are introduced into a 3D dynamic convolution layer and cooperate with the offset, so that the position of a convolution kernel sampling point can be adaptively adjusted according to the form and size of the pulmonary nodule, and the problem that the traditional fixed convolution is difficult to adapt to the size difference and form heterogeneity of the pulmonary nodule is solved; stable detection of pulmonary nodules with different sizes, especially tiny pulmonary nodules, is realized; space coordinate information and multi-scale features are combined through a channel space attention module, extraction of key information such as pulmonary nodule edge texture and density gradient is enhanced, interference of blood vessel, trachea and CT artifacts is inhibited, and the problem that small nodules and background noise are difficult to distinguish is solved.

The invention discloses a pulmonary nodule detection method based on multi-kernel representation learning, and belongs to the field of medical image analysis. The method comprises the following steps: S1, obtaining suspected region image block data of which the form is similar to that of a nodule, and carrying out standardization processing; s2, performing feature extraction through principal component analysis according to the normalized image blocks; s3, according to the extracted feature representation, calculating corresponding kernel matrixes by using multiple kernel functions; s4, according to the obtained multiple kernel matrixes, based on spectral entropy weighted fusion, obtaining a mixed kernel matrix; s5, training a single-class support vector machine model according to the mixed kernel matrix, and establishing a discrimination boundary of a normal sample; and S6, in a test stage, repeatedly executing the steps S1 to S4 on suspected nodule region image blocks acquired from the CT image of the patient, extracting features, constructing a mixed kernel matrix, inputting the trained single-class support vector machine model for judgment, and if the suspected nodule region image blocks are abnormal, outputting a nodule region until judgment of all candidate regions is completed. The problems that an existing single-core support vector machine model is poor in adaptability and sensitive to data are solved, the deep learning method depends on a large number of labeled samples, and robust pulmonary nodule detection is achieved under the condition that pulmonary nodule samples are scarce by means of the unsupervised characteristic of anomaly detection.

The invention discloses a model uncertainty-driven man-machine collaborative risk grading interpretation method, system and device and a computer readable storage medium. According to the method, under federated learning deployment, calibration confidence, bucket-level calibration deviation and multi-model inconsistency are simultaneously calculated for a single sample, and a comprehensive uncertainty score is formed to perform risk grading: when the comprehensive uncertainty score exceeds a threshold value or the calibration confidence is insufficient, manual re-checking is automatically triggered; otherwise, directly outputting the AI result. Artificially confirmed samples enter a feedback sample library for subsequent federation retraining, temperature parameters and barrel counting are periodically updated, and a continuous learning closed loop is constructed. According to the scheme, in medical scenes such as lung CT nodule detection and lung cancer I-stage recurrence risk prediction, the diagnosis efficiency and clinical safety are effectively considered, and the long-term stability and credibility of the model are improved.

The invention provides a lightweight pulmonary nodule detection method and system based on an attention mechanism and structure optimization, and a storage medium, and belongs to the technical field of medical detection. According to the method, an RFA-C2f module is designed, a re-parameterization technology and an attention mechanism are fused, decoupling of training and reasoning is achieved, the ability of a backbone network in the aspect of space and channel feature extraction is remarkably enhanced, and therefore the detection precision is improved; according to the method, a feature fusion structure Optimized Neck module focusing on up-sampling is constructed, a high-resolution up-sampling layer is introduced, a sampling path is simplified, feature redundancy is effectively removed, feature expression granularity is expanded, and therefore the pulmonary nodule omission ratio is reduced; a Lite Shared Desection module is provided, parameter redundancy is reduced through a shared convolution and group normalization technology, the consistency of feature aggregation is improved, and the model precision is kept while the calculated amount is effectively reduced.

The invention relates to the technical field of medical image processing, in particular to a lightweight pulmonary nodule detection method based on a multi-dimensional collaborative attention mechanism. According to the method, a multi-dimensional collaborative algorithm named as MS-YOLO11 is provided, based on an improved YOLO11 framework, a multi-dimensional collaborative attention mechanism and a multiple attention transformation module are fused, key feature expression is dynamically enhanced through channel, height and width dimensions, and the local and global feature interaction capability is improved in combination with pixel, channel and space attention. According to the MS-YOLO11 model, 83.66% of detection precision is obtained in an unsegmented pulmonary parenchyma CT image while only 9.35 MB model volume is maintained, the small target detection capability is effectively improved, the background interference robustness is enhanced, and the MS-YOLO11 model is suitable for pulmonary nodule auxiliary diagnosis scenes.

The present invention discloses a method, device and computing equipment for pushing image reports based on RPA and AI. The method includes: using a preset lung segmentation model to segment the lung medical image to be detected sent by the RPA robot to obtain a target medical image containing only the lung area, using a preset lung nodule detection model to detect the target medical image to obtain attribute information of each suspected lung nodule, using a preset lung nodule segmentation model to segment the area where each suspected lung nodule is located to obtain three-dimensional contour information of each suspected lung nodule; generating a suspected lung nodule image report based on the attribute information and three-dimensional contour information of each suspected lung nodule and sending it to the hospital platform through the RPA robot. In this way, the lung nodules are detected by AI image analysis technology to obtain a suspected lung nodule image report, and the report is sent to the hospital platform through the RPA robot, which reduces the time doctors spend identifying lung nodules and improves efficiency.

The application belongs to the technical field of image processing, and discloses a malignant nodule detection image processing method based on benign thyroid; the method comprises the following steps: S2, processing a standard image data set to obtain a static feature vector; S4, processing a standard dynamic image data set to obtain a dynamic feature vector; S5, processing based on the static feature vector and the dynamic feature vector, and performing probability evaluation according to a processing result to obtain a malignant probability evaluation value; S6, analyzing the malignant probability evaluation value to obtain a final probability evaluation value and a system evaluation value; S7, analyzing based on the final probability evaluation value and the system evaluation value to obtain a patient diagnosis report and a system evaluation report; and S8, processing based on the system evaluation report to obtain a system optimization report; in general, the application has the remarkable advantages of high multi-source data utilization efficiency, strong detection sensitivity and good diagnosis process efficiency.

The invention provides a thyroid nodule detection system, and the system comprises a receiving module which is configured to receive a thyroid ultrasound video; the multi-target instance segmentation module is configured to perform frame-by-frame detection on target objects in each video frame through a trained target instance segmentation model to obtain detection data of each target object in each video frame, the detection data comprises a detection frame and confidence, and each target object comprises a carotid artery, a thyroid gland and a thyroid nodule; the instance tracking module is configured to track each target object in the thyroid ultrasound video by adopting a multi-target tracking algorithm based on target detection based on each detection data to obtain each tracking trajectory of each target object; and the determination module is configured to determine a target thyroid nodule detection result corresponding to the thyroid ultrasound video based on each tracking trajectory of each target object. According to the invention, the video can be processed for an end-to-end screening scene, and the detection accuracy is improved.

This invention relates to the field of medical image processing, specifically to a multimodal medical image fusion system and method, comprising a data preprocessing module, a domain manifold embedding module, a hierarchical adaptive domain alignment network module, a multimodal feature fusion module, and a diagnostic decision module. The system establishes a topological correspondence between EBUS ultrasound, OCT tomography, and DWI functional images through a multidimensional domain manifold embedding structure; employs a hierarchical adaptive domain alignment network to achieve multi-scale feature extraction and precise alignment; designs a topology-preserving multimodal feature fusion mechanism to ensure the complete preservation of key diagnostic information from each modality during the fusion process; and utilizes a dual-path cross-validation decision strategy to improve the stability and reliability of diagnosis. The system inputs the domain-aligned multimodal features into a Transformer network and generates nodule detection and benign / malignant classification results through dual-threshold judgment, achieving accurate and non-invasive diagnosis of small lesions such as pulmonary nodules and early gastrointestinal lesions.

The invention discloses a nodule detection method and system for a lung CT image, and relates to the technical field of image processing. Comprising the steps of obtaining a to-be-processed lung CT image; inputting a to-be-processed CT image into the nodule detection model, and segmenting the to-be-processed CT image through the multi-task collaborative network to obtain a segmentation result; the segmentation result is processed through a growth form perception-learning module, and a thermodynamic diagram conforming to the pulmonary nodule growth form rule is obtained; dynamically learning time sequence information of a thermodynamic diagram in a doctor interaction process through a memory fusion module to obtain a pulmonary nodule growth form prediction map; and carrying out edge contour feature extraction on the pulmonary nodule growth form prediction map through an anisotropic segmentation module to obtain a pulmonary nodule detection result of the to-be-processed lung CT image. According to the invention, the accuracy and accuracy of the pulmonary nodule segmentation result can be improved.

The present invention belongs to the technical field of model systems for diagnosing benign and malignant solid pulmonary nodules, and specifically relates to a deep learning model system for diagnosing benign and malignant solid pulmonary nodules, including an overall architecture system of the model, a data set introduction system, a data set preprocessing system, a lung CT image segmentation system, a lung nodule detection system, a lung nodule classification system, an experimental parameter setting system, an evaluation index system, and an experimental setting and result system. The present invention proposes a deep learning model system for diagnosing benign and malignant solid pulmonary nodules, and is mainly designed for the lower branch of the network. The lower branch network uses the Inception module to extract multi-scale feature information of the lung CT image, and introduces convolutional attention modules such as ECA and CBAM to enhance the extraction effect of the lower branch network on the position and shape features of the lung CT image.

The application relates to a lung nodule detection network lightweight method, system and equipment based on shift convolution, relates to the technical field of medical image processing, and comprises the following steps: constructing a lung nodule detection model based on an anchor-free target detection algorithm; training the lung nodule detection model based on a preprocessed target lung CT data set to generate a trained lung nodule detection model; updating and replacing standard 3D convolution operation in the trained lung nodule detection model with shift convolution operation to obtain a lightweight lung nodule detection model, wherein the shift convolution operation comprises a shift operation and 2D convolution operation; and training the lightweight lung nodule detection model based on a preprocessed self-defined lung CT data set to generate a final lightweight lung nodule detection model. Through the application, the complexity of the lung nodule detection model can be effectively reduced, the size and calculation amount of the model are greatly reduced, and the lung nodule detection network is lightened.

The present invention discloses a thyroid nodule detection method based on Transformer, which relates to the technical field of image processing. After obtaining the ultrasonic image to be measured in the thyroid region and performing image preprocessing on the obtained ultrasonic image to be measured, it is input into a nodule detection model pre-trained based on the Transformer network. According to the output of the nodule detection model, the position and type of the nodule in the ultrasonic image to be measured are determined, and the detection of the nodule in the ultrasonic image to be measured is completed. The type of the nodule is used to indicate whether the nodule is a benign nodule or a malignant nodule. This method can automatically complete nodule positioning and classification, has a high degree of automation and good objectivity, does not need to construct a dense Anchor Box, does not need to use complex post-processing operations such as NMS, is easy to implement, and has low requirements for computing resources.

The application relates to an improved Mask-R-CNN lung nodule auxiliary detection method fusing a double-path channel attention and a hollow space attention, and belongs to the image processing field, and comprises the following steps: S1, data set pretreatment; S2, lung parenchyma segmentation; S3, constructing an improved candidate nodule detection and segmentation model; S4, modifying RPN according to lung nodule features; S5, improving a loss function according to data imbalance; S6, constructing a three-dimensional ResNet false positive elimination model to eliminate false positives; S7, training the improved lung nodule detection model by using selected data sets, loading the best weight file after training into the model to perform feature extraction, generating a series of candidate regions, then labeling the candidate frame according to the position relationship between the candidate region and the object real frame on the picture, generating a lung nodule prediction frame and lung nodule prediction confidence, and achieving the expected effect.

The application relates to a lung nodule detection method, device and equipment based on an improved network and a storage medium. The method comprises the following steps: processing each CT original image in an acquired image sample set to extract a corresponding lung parenchyma image, so as to reduce the amount of data to be processed and facilitate observation of the effectiveness of detection, and a lung nodule detection neural network used in the method is obtained by improving a YOLOv5 network, wherein in a backbone unit, a MetaAconC activation function is used to replace original activation functions in part of a convolution structure, a CoordAtt attention mechanism structure is added before an SPPF structure, and a BiFPN structure is used in a neck unit to perform multi-size feature fusion, so that the improved YOLOv5 network is more suitable for detection of lung nodules in medical images, and the detection is more accurate and efficient.

The invention discloses a nodule detection method suitable for an ultrasonic image. The method comprises the following steps: carrying out feature extraction on an input ultrasonic image by utilizing a backbone network of an integrated space self-adaptive deformation enhancement module, and introducing deformable convolution to improve the perception capability on an irregular marginal region; constructing a feature enhancement module fusing global and local information, and performing multi-scale fusion on feature maps extracted by the backbone network based on a double-branch network of Fourier transform and large kernel convolution; and inputting the enhanced feature map into an encoder and an interactive learning decoder to realize global context modeling and regression decoding, and outputting a nodule detection result through a composite loss function training model of joint classification and regression loss. The method can be widely applied to ultrasonic image nodule detection tasks of various organs, the radiograph reading burden of ultrasonic doctors can be effectively reduced, objective and quantitative auxiliary diagnosis indexes are provided, and the accuracy and efficiency of clinical diagnosis are improved.

The application relates to the technical field of image analysis, in particular to a respiratory system lung nodule tumor cell detection method and system based on image feature fusion and a storage medium, which comprises the following steps: constructing a two-dimensional pixel gray array of a chest CT image and performing edge positioning, constructing a closed image boundary through gray difference analysis and gradient direction continuity, extracting a lung nodule candidate region image in the closed region, establishing pixel adjacency relations in horizontal, vertical and diagonal directions, constituting a direction difference image set and extracting a continuous texture region with a closed structure, generating a lung nodule edge contour connection image through spatial relationship mapping and pixel connection extension, and finally performing boundary fusion and pixel structure recombination to form a target detection image. In the application, the direction gray difference construction and boundary closure analysis are combined, the connected path judgment and pixel aggregation processing are fused, the texture structure concentration and image recognition definition are effectively enhanced, and the accuracy and readability of lung nodule detection are improved.

The application provides a lung nodule detection method based on deep learning, which comprises the following steps: A, performing parenchymal segmentation on a three-dimensional lung CT image; B, sequentially processing the lung parenchymal image after being divided into blocks to obtain a sparse matrix; C, extracting three-view information of the sparse matrix for false positive screening; D, building a network model; and E, using the trained network model to detect lung nodules and output the detection results. The three-view false positive auxiliary module is combined, directly embedded into an end-to-end framework, and the three-view self-attention information of the sparse three-dimensional image is used to help screen out false positive regions. On the one hand, the problem of learning attention in a complex 3D scene is solved. On the other hand, the detection and false positive region screening are designed into an end-to-end training model, the detection speed and detection accuracy are improved, the overall complexity of the model is reduced, the model training convergence speed is improved when the model is trained, and the loss function is trained uniformly.

This invention discloses a method for constructing a lung weight index based on chest CT images and its application. The method includes: Step 1, low-dose CT image acquisition and preprocessing; Step 2, automatic segmentation of the lung and lung lobes; Step 3, calculation of the average density of lung lobes and conversion to physical density; Step 4, calculation of lung lobe volume; Step 5, calculation of lung lobe weight and whole lung weight; Step 6, construction of the lung weight index; Step 7, risk assessment based on the lung weight index. This method can be applied to practical scenarios such as lung cancer screening, health checkups, and image-assisted diagnosis. This invention comprehensively reflects the state of lung tissue, blood vessels, and interstitium through the lung weight index, providing a more holistic quantitative description. Furthermore, it does not require nodule detection as a necessary prerequisite, making it suitable for individuals with few nodules, small volume, or those who have not yet formed a definite mass, thus better meeting the practical needs of early lung cancer screening.

The invention belongs to the technical field of medical image analysis, and particularly relates to a pulmonary nodule automatic detection method fusing adaptive multi-scale double attention and a Kolmogorov Arnold layer, which comprises the following steps: S1, performing pulmonary nodule candidate region automatic detection through a first-stage model, the first-stage model adopts an encoder-decoder structure and comprises a self-adaptive multi-scale double attention mechanism; s2, eliminating a redundant prediction frame by using non-maximum suppression; and S3, performing false positive removal through a second stage model, wherein the second stage model adopts an encoder-decoder structure and comprises a Kolmogorov Arnold layer. According to the method, the adaptive multi-scale double attention and the Kolmogorov Arnold layer are fused, the feature expression ability of nodules in different forms is enhanced, the complex texture features of the pulmonary nodules are better captured, false positive nodules are reduced, and the pulmonary nodule detection precision is improved.

This invention discloses a prior knowledge-guided ultrasound image thyroid nodule detection device. The method integrates prior knowledge and nodule distribution characteristics into a deep network, comprising two stages: The first stage, a multi-scale coarse detection module (ThyroidDetⅠ), designs a multi-scale region-based detection network to learn pyramid features to detect nodules at different feature scales. Region proposals are trained using prior knowledge about the actual nodule size and shape distribution for coarse nodule detection. The second stage, a multi-branch fine classification module (ThyroidDetⅡ), proposes a multi-branch fine classification network to integrate features for multi-view diagnosis. Each network branch captures and enhances a specific set of features commonly used by physicians for fine nodule classification. This invention effectively reduces the false negative rate for small nodules, lowers the false positive rate for challenging nodules, improves detection accuracy, and significantly reduces subjective judgment errors in the medical diagnostic process.

The present application relates to the technical field of lung nodule detection, in particular to a lung nodule detection method and system based on adaptive multi-scale deformable attention. The method comprises the following steps: acquiring a clinical lung nodule dataset; performing data preprocessing on the acquired dataset; constructing a deep network model based on AMDA-YOLO; training the deep network model based on AMDA-YOLO using a linear warm-up strategy; performing lung nodule detection using the trained model; and outputting the detection results. The detection system constructed by the present application has excellent cross-data domain generalization capability and clinical practical value.

The invention relates to the technical field of paint nodule detection, in particular to a paint nodule detection device which comprises a support. The bracket is connected with a mounting frame; a proximity switch and a pressure sensor are mounted on the mounting frame; a trigger plate is hinged in the mounting frame; the trigger plate is arranged at the bottom of the proximity switch; the trigger plate is propped against the pressure sensor; a flowing groove is formed in the trigger plate; a copper wire is arranged in the flowing groove; the proximity switch and the pressure sensor are connected with a controller; according to the device, counting and data comparison are performed through the proximity switch and the pressure sensor respectively, the number of paint nodules on the copper wire can be displayed more accurately by adopting a large number of data, and therefore the quality of the copper wire can be effectively controlled.

The invention relates to a two-stage pulmonary nodule detection method and system based on cross-layer attention fusion, and belongs to the technical field of medical image processing, and the method comprises the steps: carrying out the preprocessing of a three-dimensional chest CT image, segmenting a pulmonary parenchyma region, and carrying out the standardization of the pulmonary parenchyma region, and obtaining a to-be-detected image; inputting the to-be-detected image into the candidate nodule detection network, and generating a position frame of a candidate nodule; extracting a three-dimensional image region corresponding to the candidate nodule position frame, inputting the three-dimensional image region into a false positive suppression network for classification, and filtering out false positive candidates; and outputting a final pulmonary nodule detection result confirmed by the false positive suppression network. Through the design of dual-stage task decoupling, an innovative space and channel residual module and a multi-scale progressive sensing network module and a self-adaptive training strategy, the core problems of sensitivity and specificity imbalance, weak feature discrimination ability, poor multi-scale adaptability and unstable training in intelligent detection of pulmonary nodules are systematically solved.

The invention discloses a breast nodule detection method and system based on ultrasonic medicine, and the method comprises the steps: firstly obtaining breast ultrasonic video data, and carrying out the preprocessing of interception, zooming, sampling, screening, enhancement and the like; 18 layers of ResNet18 are adopted for feature extraction, a linear classification network and an attention selection network are combined, and a detection model is constructed through optimization of a cross entropy and a center loss function. The system is correspondingly provided with a feature extraction module, a linear classification module and the like. And finally, to-be-tested video data are input into the model, so that whether breast nodules exist or not and the result of benign and malignant nodules can be quickly obtained, and efficient and accurate assistance is provided for breast disease diagnosis.

The invention provides a thyroid nodule detection model construction method and application based on intermediate frequency feature interaction, and the method comprises the steps: obtaining a plurality of training images which are thyroid ultrasound images marked with thyroid nodules; and constructing a thyroid nodule detection architecture formed by sequentially connecting a backbone network, a feature fusion network and a classification network, and training the thyroid nodule detection architecture by using a plurality of training images to obtain a constructed thyroid nodule detection model. According to the scheme, feature processing is carried out through depth separable convolution with different expansion rates by using the expansion convolution unit, the mid-distance context can be aggregated while tiny nodule details are kept, and then efficient coupling of local details and mid-distance semantics is realized through the intermediate frequency feature interaction unit, so that the thyroid nodule detection model has a better classification effect.

The present application relates to the technical field of antibody immunology detection, and particularly relates to a polypeptide based on PDZK1IP1 protein, an antibody and application thereof. A polypeptide for preparing a tumor marker PDZK1IP1 antibody is disclosed in the first aspect, and the amino acid sequence at positions 68-81 of the PDZK1IP1 protein is selected. A polyclonal antibody based on the polypeptide is disclosed in the second aspect, and is used for preparing or screening a lung nodule detection reagent, and the expression amount of the PDZK1IP1 protein in a subject is reflected by the polyclonal antibody. A kit for detecting lung cancer related PDZK1IP1 protein is disclosed in the third aspect, and the kit comprises latex particles coated with the polyclonal antibody, and a standard product obtained by expression and purification of a recombinant expression vector of the whole sequence of the PDZK1IP1 protein gene. The present application discloses a non-invasive biomarker protein for distinguishing benign and malignant lung nodules for the first time, and develops a detection method and a corresponding diagnostic kit based on the immunoturbidimetry technology, so as to provide a new detection approach of non-invasion, rapidness, sensitivity and high specificity for early lung cancer.

The invention discloses a lymph node and nodule segmentation method based on double-flow large model pre-training, and relates to the technical field of ultrasonic image analysis, a double-flow large model is constructed based on a Transform framework, the double-flow large model comprises a space mask reconstruction module and a frequency mask reconstruction module, improvement is performed on the basis of an MAE framework, and the space mask reconstruction module and the frequency mask reconstruction module are optimized. According to the method, a self-supervised training technology based on spatial frequency double masks is provided to fully extract intrinsic information of an ultrasonic image, spatial masks are used for reconstructing spatial features of the image, and robust spatial information is extracted by recovering an original image from the masked image; the frequency mask is used for reconstructing frequency characteristics of the image, and implicit and highly generalized frequency information representation is learned through frequency conversion and decoding; moreover, the method can be used for fine tuning of any downstream task, including but not limited to lymph node classification, lymph node detection, lymph node positioning and the like, and the practicability and universality of the method are further improved.

The application discloses a lung nodule detection and semantic attribute rating method based on multi-task learning, which comprises two sub-tasks of lung nodule detection and semantic attribute rating. In order to realize feature sharing of joint learning between the two sub-tasks, the application connects a lung nodule detection sub-network and a semantic attribute rating sub-network together to form an end-to-end joint model. The lung nodule detection sub-network acquires position information of the nodule, and the output thereof serves as input of the semantic attribute rating sub-network. The two sub-networks share bottom layer features in a down-sampling stage of a U-Net network, so that feature sharing of the multi-task model is realized. In the process of joint learning training of the two tasks, since the training difficulty and convergence speed of different sub-tasks can be different, the application adopts a dynamic weight average method to adjust loss weights of different tasks. The method can not only effectively detect lung nodules, but also identify semantic attributes of the lung nodules as an additional supervision signal.