194 results about "Computer-aided diagnosis" patented technology

The invention discloses a heterogeneous double-flow fusion method and system for diabetic retinopathy grading. The method comprises the following steps: obtaining an output result of diabetic retinopathy grading by utilizing a heterogeneous double-flow architecture; processing an input fundus image into images with different resolutions; extracting global context features from the low-resolution image by using a lightweight visual Transform model distilled by composite knowledge, and extracting local focus features from the high-resolution image by using a convolutional neural network model; performing interactive fusion on the global context features and the local focus features of the double-branch architecture through a symmetric bidirectional cross attention fusion module to obtain enhanced fusion feature representation; and finally, inputting the fusion features into a classifier, and outputting a severity grading result of the lesion. The method aims at improving the accuracy and robustness of hierarchical diagnosis through deep analysis of global information and local details, and can be applied to the medical fields of clinical computer-aided diagnosis, eye image analysis and the like.

The invention discloses a liver and gall lesion early screening system and method based on image fusion, and relates to the technical field of medical image processing and computer-aided diagnosis, and the method comprises the following steps: reconstructing a multi-modal image space-time coordinate system under a unified event time baseline, generating a respiratory displacement field and a magnetic sensitive pulse fingerprint, and constructing an artifact suspicion map; and performing anti-fact playback based on the artifact suspicion chart, performing frame-by-frame playback on the image acquisition sequence, quantifying artifact superposition tracks with consistent directions, and solidifying an artifact anchor point set. According to the method, space-time coordinates are constructed based on a unified event time baseline, a breathing displacement field and magnetic sensing pulse fingerprints are introduced, anti-fact playback, distortion kernel inference and residual decoupling are combined, artifact recognition and fusion intervention are achieved, and artifact closed-loop elimination is completed by judging threshold-driven fusion regulation and time reversal phase gating, so that the artifact recognition accuracy is improved. And the fused image authenticity is improved.

The embodiment of the invention provides an image processing method, a computer-aided diagnosis method and equipment. The image processing method comprises the following steps: acquiring a medical image, wherein the medical image comprises a plurality of parts of a detection object; feature extraction is carried out on the medical image, and part-level image features corresponding to all parts are obtained respectively; determining detection result category information corresponding to each part, and performing feature extraction on each piece of detection result category information to obtain text features corresponding to each piece of detection result category information; and performing detection result prediction based on the part-level image features corresponding to the parts and the text features to obtain prediction detection results of the multiple parts of the detection object. According to the embodiment of the invention, multi-disease detection of multiple parts of the detected object is realized, and the expansibility of medical image interpretation and auxiliary diagnosis is improved.

The invention discloses a brain tumor MRI image semantic segmentation method, and belongs to the technical field of medical image processing and computer-aided diagnosis. According to the invention, the problem of low segmentation precision obtained based on the existing brain tumor MRI image semantic segmentation technology is solved. The invention provides a semantic segmentation model which combines a U-Net framework with an LWD module, an MFDF module and various filters, the LWD module can keep information as much as possible in a down-sampling process, the MFDF module extracts operators by constructing new directional gradient features, and combines the operators in different directions by using dual-channel filtering, so that the semantic segmentation of the U-Net framework is realized. And the constructed multi-directional filter can respectively extract low-frequency and high-frequency characteristic direction information. The MFDF module transmits detail features from the coding module to the corresponding decoding module, so that spatial information including feature boundaries and textures is recovered, the precision of model segmentation is improved, and the method has good adaptability to the randomness of brain tumor shapes, sizes and boundaries. The method can be applied to brain tumor MRI image segmentation.

Systems and methods for performing one or more medical imaging analysis tasks on PCCT (photon-counting computed tomography) images are provided. Image acquisition parameters of a PCCT image acquisition device are determined for acquiring PCCT images. One or more PCCT images of an anatomical object of a patient acquired using the PCCT image acquisition device configured with the image acquisition parameters are received. One or more medical imaging analysis tasks analyzing the anatomical object are performed based on the one or more PCCT images using one or more machine learning based models. Results of the one or more medical imaging analysis tasks are output.

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 embodiment of the invention provides an image processing and computer-aided diagnosis method, electronic equipment, a storage medium and a program product, and the image processing method comprises the steps: obtaining a to-be-processed medical image which comprises the information of a plurality of target parts; performing first feature extraction on the medical image to obtain medical image features of the medical image; performing second feature extraction on the medical image features to obtain part identification information of each target part corresponding to the plurality of target parts in the medical image features; taking the part identification information of each target part as guidance, performing part feature extraction based on the medical image features, and obtaining part features of each target part; and predicting text features of the text based on the part features of the target parts and the detection results corresponding to the target parts to obtain prediction detection results of the multiple target parts. Therefore, the target part in the medical image can be accurately positioned and detected without depending on an external mask.

The invention discloses a variable convolution UNet tooth image segmentation method and system fusing an attention mechanism, and belongs to the technical field of medical image processing and computer-aided diagnosis. A tooth image data set is obtained and preprocessed; using the preprocessed data set to train a DSC-UNet segmentation model; the DSC-UNet segmentation model introduces a multi-modal feature fusion module and a deformable convolution module into a UNet framework; and segmenting an input tooth image by using the trained DSC-UNet segmentation model, and outputting a pixel-level segmentation result of the tooth structure. By constructing a multi-level feature interaction mechanism and a deformable convolution module, the capturing capability of the network on tooth edge details and the segmentation robustness of special-shaped teeth are remarkably improved, more reliable technical support is provided for an intelligent dental diagnosis and treatment system, and the method is particularly suitable for high-precision segmentation of complex tooth structures in oral panoramic X-ray films and CBCT images.

The invention discloses a method and a device for performing focus detection on a pancreatic ultrasonic endoscope image, and belongs to the field of computer-aided diagnosis and image data processing. According to the method, a focus detection model capable of processing two tasks of disease classification and image focus segmentation is constructed, the focus detection model is trained by constructing a pancreas ultrasonic endoscope image data set with a real label, and focus detection is performed on an input pancreas ultrasonic endoscope image by using the trained focus detection model. The disease type and the lesion area in the segmented image can be accurately predicted, the lesion detection model can be optimized through fine tuning in clinical application, and the prediction accuracy and applicability are improved.

The invention discloses a mild cognitive impairment auxiliary recognition system and method based on eye movement characteristics, and relates to the technical field of computer-aided diagnosis, and the system comprises a data obtaining module which is used for obtaining eye movement data of a patient in the emotion classification task process; the data preprocessing module is used for performing data preprocessing on the acquired eye movement data; the auxiliary recognition module is used for inputting the preprocessed eye movement data into an auxiliary recognition model based on a double-flow fusion network, the auxiliary recognition model comprises a spatial feature extraction module, a time sequence feature extraction module and a multi-modal fusion classification module, key features of the eye movement hotspot map are extracted through the spatial feature extraction module, and the key features of the eye movement hotspot map are extracted through the time sequence feature extraction module; the time sequence features of the dynamic eye movement sequence are extracted through the time sequence feature extraction module, the features extracted by the two feature extraction modules are spliced and fused through the multi-modal fusion and classification module, and final accurate classification and recognition results of the Alzheimer's disease, the mild cognitive impairment and the health are output.

The invention relates to the technical field of MRI image processing, and discloses a multi-MRI sequence deletion interpolation method based on a variational auto-encoder. According to the method, the FSL software is used for performing registration processing on the multi-mode MRI image, so that the consistency of the image in space, position and voxel pitch is ensured, and the precision of subsequent analysis is improved. In combination with the minimum and maximum value normalization and slice screening technology, the data preprocessing process is optimized, the data quality is improved, and redundancy is reduced. An end-to-end pre-trained variational auto-encoder model is adopted, an accurate potential space mapping relation is constructed, and the image reconstruction capability is enhanced. And fine training is carried out by using the SSIM loss after weight adjustment and the perception loss, so that high-quality modal missing interpolation is realized, and the integrity and availability of the medical image are further improved. According to the method, the multi-modal medical image processing capability can be effectively improved, and accurate technical support is provided for medical image analysis and computer-aided diagnosis.

The invention provides an audio-based mild cognitive impairment auxiliary diagnosis method and device, and relates to the technical field of computer-aided diagnosis. The method comprises the following steps: acquiring original dialogue audio; performing audio and acoustic parameter feature extraction according to the original dialogue audio to obtain a multi-resolution spectrogram feature and a side language feature; based on a multi-scale double attention mechanism of a hierarchical attention weight generation strategy, performing dynamic feature fusion according to the multi-resolution spectrogram features to obtain optimized spectrogram features; performing optimization fine tuning on a pre-trained auxiliary diagnosis model by using the optimized spectrogram features and the secondary language features to obtain an optimized auxiliary diagnosis model; and obtaining an actual dialogue audio, and performing cognitive disorder detection by using the optimized auxiliary diagnosis model to obtain an auxiliary diagnosis result. The invention provides an efficient and accurate mild cognitive impairment auxiliary diagnosis method based on audio.

The invention relates to the field of computer-aided diagnosis, cross-modal fusion, image morphological operation and feature extraction related to computer vision and artificial intelligence, in particular to a method for predicting mild cognitive impairment in the progress stage of Alzheimer's disease, which comprises the following steps: S1, respectively inputting MRI (Magnetic Resonance Imaging), Clinical Table and PET (Positron Emission Tomography) data into respective encoders, and extracting initial image features and table features; s2, for the MRI branch and the PET branch, inputting respective image features and table features into a mixed channel attention module together, and calculating a mixed channel attention feature; s3, performing element-by-element multiplication on the mixed channel attention features obtained in the step S2 and original image features of corresponding branches to realize feature enhancement of channel dimensions; and S4, inputting the feature enhanced in the step S3 into a space attention module, and calculating a space attention feature. Structural MRI, functional PET and clinical table data are integrated for predicting the conversion from MCI to AD.

The invention discloses an Alzheimer's disease diagnosis classification method based on a multi-modal nerve image, which is used for auxiliary diagnosis of Alzheimer's disease. The method comprises the following steps: firstly, respectively extracting image features of sMRI and PET brain images by using a self-attention vision converter, extracting global information, improving the feature discrimination capability, and enabling a model to capture a remote dependency relationship more easily, and secondly, realizing complementary fusion of the two image features by using an interactive attention fusion network. And finally, using a Stacking ensemble learning framework as a classifier. According to the method provided by the invention, the pathological information of sMRI and PET brain images is combined, the Alzheimer's disease classification accuracy is improved, a doctor can be assisted in diagnosis, and the method has a wide application prospect in the field of computer-aided diagnosis.

The invention discloses an Alzheimer's disease computer-aided diagnosis system based on a unified multi-modal mechanism visual language model. The Alzheimer's disease computer-aided diagnosis system comprises a multi-modal data input module, a feature extraction and projection module, an abnormal mark token integration module, a unified multi-modal attention mechanism module, an anomaly detection module and a diagnosis result generation module. The method has the outstanding advantages of improving the early diagnosis precision of the Alzheimer's disease, enhancing the model robustness and improving the clinical practicability.

The invention discloses an intelligent auxiliary diagnosis and grading system for gastrointestinal tumor endoscopic images, which belongs to the technical field of medical image processing and computer-aided diagnosis and comprises a multi-modal image preprocessing and segmentation module, a lesion feature extraction and diagnosis module, a grading and depth evaluation module and a closed-loop feedback optimization module. The system receives white light, a narrow band and an amplified endoscopic image, adaptive segmentation is performed to obtain a lesion area, mucous membrane morphology, capillary and gland features are extracted, Paris classification, Vienna classification and infiltration depth evaluation are realized, and segmentation parameters are subjected to closed-loop optimization according to classification confidence. And intelligent auxiliary support is provided for early diagnosis and treatment decision of gastrointestinal tumors.

Provided in the embodiments of the present disclosure are a target object identification method, an object identification model training method, a method for detecting a visible lymph node in a CT image, a computer aided diagnosis method, an electronic device, a storage medium, and a program product. The target object identification method comprises: determining an image to be subjected to identification; and inputting said image into an object identification model for object identification, so as to obtain a target object in said image, wherein the object identification model is trained by means of a target sample object, which is identified from a sample image, and a sample label of the sample image, the target sample object is determined from among a plurality of candidate sample objects by means of object type identification results and object position detection results of the plurality of candidate sample objects, the object position detection results are obtained by means of performing position detection on object positions of the plurality of candidate sample objects, and the plurality of candidate sample objects are obtained by means of performing object identification on the sample image.

The invention relates to the technical field of computer-aided diagnosis, and discloses a block classification aided diagnosis system based on a self-prompt mechanism, which comprises the following steps: dividing a medical ultrasonic image into a plurality of image blocks, carrying out convolution and flattening processing to obtain a feature vector, extracting a preliminary global feature through a plurality of visual conversion blocks, and carrying out self-prompt on the image blocks; a fusion feature is obtained through a plurality of prompt attention conversion blocks, a self-prompt mechanism is introduced into the prompt attention conversion blocks, a learnable space prompt vector and a channel prompt matrix are introduced into the self-prompt mechanism, and a space mask is generated by calculating cosine similarity of the space prompt vector and a preliminary global feature. After spatial screening operation is carried out on the initial global features, channel guiding operation is applied through a channel prompt matrix; and performing weighted adjustment on the fused features to obtain final features, and obtaining benign and malignant classification results through a classification head. The feature modeling capability of the local fine-grained discriminative region in the image can be effectively improved, and the auxiliary diagnosis accuracy is improved.

The embodiment of the invention provides a pathological image analysis method and a tumor computer-aided diagnosis method, and the method comprises the steps: obtaining pathological image information corresponding to a target pathological image which comprises at least one cell entity, the pathological image information comprises cell attribute information corresponding to each cell entity; acquiring density characteristic information corresponding to each cell entity based on the cell attribute information corresponding to each cell entity; generating pathological image feature information corresponding to the target pathological image according to the density feature information corresponding to each cell entity; and generating an image analysis result corresponding to the target pathological image based on the pathological image feature information. The pathological image information of the cell attribute information corresponding to the plurality of cell entities corresponds to the pathological image, so that the spatial characteristics of the cells in the pathological image can be comprehensively captured, and the limitation of a traditional method on clinical importance is made up.

The invention discloses a Parkinson's disease diagnosis method based on graph attention, and the method comprises the steps: building a brain function connection graph based on a resting state functional magnetic resonance image, expanding the information exchange between graph nodes to the global graph through the self-attention mechanism of a Transform model, and achieving the diagnosis of Parkinson's disease. Relative and absolute position codes of the graph are constructed on the basis of a graph Laplacian matrix and characteristic decomposition thereof, node characteristic information and graph topological structure information of the brain function connection graph are fully utilized, and rich information of attention layers of different graphs is further fused through jump connection; the problems of over-smoothing and insufficient aggregation of partial node information are solved, and finally the brain function connection diagram is divided into two types of Parkinson's disease patients and normal people, so that accurate Parkinson's disease computer-aided diagnosis is realized. The highly-discriminated brain region for Parkinson's disease diagnosis has high correlation with the research result of the brain function of the Parkinson's disease in the past, so that the invention provides a new strategy for searching imaging markers for diagnosing Parkinson's disease.

The invention relates to the technical field of medical image processing and computer-aided diagnosis, in particular to a CT liver segmentation and three-dimensional reconstruction method combined with image enhancement. The method comprises the following steps: 1) establishing a plain-scan CT database, and collecting enough human body plain-scan CT image data; 2) screening and cleaning data in the plain-scan CT database; 3) performing image enhancement on CT plain scanning data according to a region of interest (ROI); 4) performing semantic segmentation on the enhanced image, and automatically labeling the liver in the plain-scan CT image by a model; and 5) performing three-dimensional reconstruction and smoothing processing on the segmented result image data to generate a simulated three-dimensional model, which is helpful for a doctor to more clearly master the relative position of the human anatomical structure, improves the safety and treatment effect of an operation, and facilitates the communication between the doctor and a patient at the same time.

The embodiment of the invention provides a cancer computer-aided diagnosis method and system, an esophageal cancer computer-aided diagnosis method and a gastric cancer computer-aided diagnosis method.The tumor detection method comprises the steps that a CT image processing task is received, the CT image processing task carries a plurality of CT images corresponding to a target detection area, and the target detection area corresponds to the CT images; the CT image processing task is used for detecting whether a tumor exists in the target detection area; the multiple CT images are input into a CT image processing model, a detection result corresponding to the target detection area is obtained, the CT image processing model generates the detection result corresponding to the target detection area based on the multi-scale feature information corresponding to the multiple CT images, and the detection result corresponding to the target detection area is obtained. The detection result comprises detection annotation information, detection category information and a detection guidance text. The accuracy of subsequently generated detection results is improved by obtaining the multiple pieces of scale feature information corresponding to the CT image. The detection result comprises the position information of the abnormal object, the information of the to-be-detected object and the guidance text, the detection result is enriched, multi-dimensional detection information is provided for the user, and the use experience of the user is improved.

The invention provides an intelligent assessment method for cervical lateral deviation based on multi-modal fusion and deep learning, and solves the technical problems of single data source, weak feature generalization ability and large assessment result error caused by only analysis of a static single frame in existing cervical lateral deviation assessment. The method comprises the steps of obtaining multi-modal data, performing preprocessing, attitude normalization and feature weighted fusion to obtain fused multi-scale features, performing mapping to generate a heat map, and performing peak retrieval on the heat map to output key points; tracking time sequence tracks of the key points, correcting abnormal key points, and collecting continuous multi-frame key points for key point detection to obtain target key points; and constructing a cervical vertebra trunk coupling mechanical model, dynamically optimizing a gravity line reference axis, correcting pelvic inclination attitude deviation, and optimally solving the optimal offset through a Lagrange multiplication method and a least square method. The method can be widely applied to the technical field of medical image analysis and computer-aided diagnosis.

The invention relates to the field of intelligent medical treatment, in particular to a method, system and equipment for diagnosing osimertinib drug-resistant lung cancer patients, predicting drug treatment effects and screening drugs based on UQCC2 computer assistance. According to the application, the remarkable high expression of UQCC2 in the osimertinib drug-resistant lung cancer is found for the first time, and the effect between UQCC2 and METTL3 is found, so that an efficient and rapid method is provided for diagnosis of osimertinib drug-resistant lung cancer patients, prediction of the drug treatment effect of the osimertinib drug-resistant lung cancer patients and screening of drugs based on targeting UQCC2 and METTL3; the method has important research significance on related disease prevention and treatment research.

The invention relates to the technical field of medical image processing and computer-aided diagnosis, in particular to a liver blood vessel three-dimensional reconstruction method based on deep learning and image processing. The method comprises the following steps: 1) constructing a data set containing multiple liver blood vessel CT images for three-dimensional reconstruction; 2) screening out qualified CT images, and ensuring that the reconstruction result is accurate; 3) inputting the screened image into a three-dimensional reconstruction system, and preparing for further processing; 4) preprocessing the CT image by using an image processing technology to highlight the ROI of the liver blood vessel; 5) automatically identifying and segmenting a liver blood vessel structure in the CT image through a semantic segmentation algorithm embedded in the system; 6) performing enhancement processing on the segmented image, and constructing a three-dimensional form of the liver blood vessel from different directions by using a multi-view reconstruction technology; and 7) performing three-dimensional reconstruction on the segmented blood vessel image to generate an accurate liver blood vessel three-dimensional model. Doctors are helped to master the anatomical structure of the human body more clearly, so that the operation safety and the treatment effect are improved.

The invention discloses a lightweight dynamic multi-scale segmentation method for a colon polyp. The lightweight dynamic multi-scale segmentation method comprises the following steps: S1, image input and data enhancement; s2, backbone network feature extraction: constructing a U-shaped encoder-decoder structure, wherein an encoder extracts multi-scale features through a dynamic multi-scale convolution module; s3, local and global feature enhancement: capturing local texture details through a regional detail aggregator, and obtaining global semantic information through a global context integrator; and S4, detection and segmentation: integrating the local details and the global semantic information by a decoder by adopting a hierarchical later fusion strategy, and outputting a final segmentation result. According to the method, the dynamic multi-scale convolution module is introduced, and the dynamic kernel selection and structure re-parameterization technology is used, so that the multi-scale feature extraction capability is greatly improved while the light weight of the model is kept; a regional detail aggregator and a global context integrator are introduced, so that local details and global semantics are effectively coordinated, and the segmentation precision of the polyp boundary is remarkably improved; compared with the prior art, the colon polyp segmentation method has the advantages that higher accuracy and intersection-union ratio are achieved on colon polyp segmentation tasks, and meanwhile, the colon polyp segmentation method has lower calculation complexity and parameter quantity and is suitable for real-time deployment of a clinical computer-aided diagnosis system.

The invention relates to the field of medical image processing and computer-aided diagnosis, in particular to an OCT (optical coherence tomography) retina layer and hydrops segmentation method based on a graph attention dual-decoder network, which comprises the following steps of: preparing and preprocessing data, acquiring OCT image data and generating corresponding segmentation labels and boundary labels; feature coding: processing the input image through a shared encoder network, and extracting and outputting feature maps of multiple levels; carrying out dual-path decoding and interaction, carrying out region segmentation and boundary segmentation by using two parallel decoders, and carrying out cross-path feature interaction through an attention mechanism during decoding; a graph attention mechanism is introduced into a region segmentation path, and information modeling is carried out on the deep features; and calculating a composite loss function according to the output of the decoder, and optimizing network parameters. According to the method, high-precision segmentation of the lesion OCT image is realized through a double-decoder multi-task architecture, cross-level space attention interaction and image attention global reasoning.

The embodiment of the invention provides a cell labeling model training method and a tumor computer-aided diagnosis method, and the method comprises the steps: obtaining a target pathological image, and generating at least one pathological image slice which comprises at least one cell entity based on the target pathological image; inputting each pathological image slice into a cell labeling model, obtaining cell category information and cell position information corresponding to each cell entity in each pathological image slice generated by the cell labeling model, and performing cell labeling according to the cell category information and the cell position information corresponding to each cell entity in each pathological image slice, determining an initial slice category corresponding to each pathological image slice; inputting each pathological image slice into a slice verification model to obtain a target slice category corresponding to each pathological image slice generated by the slice verification model; and training a cell labeling model according to the initial slice category and the target slice category corresponding to each pathological image slice. The cost is reduced, the efficiency is improved, and new technical requirements are met.

This invention relates to a grading method for diabetic retinopathy based on a local-global interactive dual-branch network, belonging to the fields of deep learning, medical image analysis, and computer-aided diagnosis. It includes: inputting the generated initial feature sequence into a global scanning module; using dilated reparameterized convolution to capture multi-scale structural prior information; inputting the feature map with structural prior information into a core dual-branch LoGo module; the local flow extracting fine-grained lesion features through depthwise separable convolution; the global flow generating spatially variable convolution kernels based on the global context through contextual hybrid dynamic convolution; and performing bidirectional cross-modulation through an adaptive multi-scale feature interaction aggregation module to sharpen local details for global semantics and filter local noise from the global context, ultimately outputting a disease severity grade. This invention explicitly simulates the cognitive strategies of clinicians, significantly improving grading accuracy while maintaining computational efficiency.

A method of sampling a target tissue in an animal body, comprising the steps of inserting a endoscope-needle system into the animal body, guiding the endoscope-needle system along a path toward the target tissue using a Doppler-based optical coherence tomography (OCT) system coupled with machine-learning-based computer-aided diagnosis (ML-CAD), wherein the Doppler-based OCT system coupled with ML-CAD enables the identification of blood vessels along the path in advance of the needle tip of the endoscope-needle system thereby substantially avoiding damage to the blood vessels along the path as the needle tip of the endoscope-needle system is guided into the target tissue, after the needle tip is guided into the target tissue, and removing a tissue sample from the target tissue.