107 results about "Disease classification" patented technology

The invention discloses a multi-omics data integration and classification method, system and device based on hierarchical attention, and is applied to the field of precise medical big data analysis. The method comprises the following steps: firstly, generating feature embedding and feature importance scores through a plurality of parallel feature-level attention modules; then, embedding and inputting all the characteristics of the omics into a unified omics-level attention module, and generating omics embedding and omics importance scores; and finally, a classification prediction task is executed based on omics embedding, and a classification result is output for disease classification. The invention completely abandons a traditional dependency graph convolutional network and an integration normal form of variants of the dependency graph convolutional network, and provides a universal hierarchical attention integration architecture. The framework supports classification tasks of any complex diseases, is not limited by omics data types and combination modes, not only is remarkably superior to a traditional integration normal form in classification performance, but also shows a unique negative generalization distance, and proves that the framework has excellent generalization ability. Meanwhile, features and omics importance scores automatically output by the model provide a powerful analysis tool for biomarker discovery and precise diagnosis and treatment of complex diseases.

The invention relates to a fig health status assessment method and system based on multi-modal data fusion, and the method comprises the steps: constructing a multi-modal feature vector through fusing the features of a multi-spectral image, a hyperspectral cube and a visible light image; then proposing a multi-modal fusion suitability evaluation model, pre-processing the multi-modal feature vector, and then carrying out deep fusion and feature association to obtain a fusion feature; and finally, according to the fusion features, outputting an adaptation score and disease and pest classification. Therefore, the characteristic representation integrity is obviously enhanced, the evaluation accuracy and the pest and disease classification precision are improved, and the robustness and the anti-interference capability are enhanced.

The invention relates to the technical field of artificial intelligence, in particular to a special child disease classification method based on particle-ball cross-granularity knowledge collaborative feature selection. The method comprises the following steps: firstly, providing a cross-granularity knowledge collaboration method based on granules and balls; further designing a fuzzy rough set model based on particle-ball cross-granularity knowledge collaboration to perform feature selection; according to the method, feature knowledge under different granularity levels is mined and coordinated to realize more robust and more efficient feature selection, so that the accuracy, generalization ability and interpretability of a classification model are improved, and a more reliable technical tool is provided for early screening and auxiliary diagnosis of diseases of special children.

The invention provides a mental disorder aided decision-making method and system based on multi-modal data, and belongs to the technical field of disease aided decision-making, the method is applied to a system comprising a data acquisition module, a preliminary screening module and an aided decision-making module, and the method specifically comprises the following steps: preprocessing the multi-modal data of a patient; in combination with the mental disorder risk level of the patient of the preliminary screening model, decision assistance is triggered according to the mental disorder risk level, or corresponding decision suggestions are matched and output; when decision assistance is triggered, the disease classification probability and severity are obtained through a diagnosis model based on a cross-modal attention mechanism, meanwhile, according to a time-dependent risk prediction model, a survival probability curve of a recurrence risk is generated in combination with historical diagnosis data of a patient, and decision assistance suggestions are determined by integrating outputs of the two models. According to the method, on the basis of multi-modal data, multiple types of intelligent models are fused, objective data support is provided in the assessment and intervention process, and the missed diagnosis and misdiagnosis risks of mental disorders are reduced.

The invention provides a method, system and device for vocal cord disease classification based on a ResNet model of MFCC, a medium and a program product, and relates to the field of vocal cord disease classification. The method comprises the following steps: S1) collecting pronunciation of a subject, constructing a corresponding label, and preprocessing the pronunciation of the subject; s2) converting the standardized pronunciation data set into a three-dimensional Mel spectrum feature map; s3) a vocal cord disease classification model is constructed, the vocal cord disease classification model adopts a ResNet architecture, the three-dimensional Mel spectrum feature map is used as input, and an SE module is embedded in each level of residual unit of the ResNet; s4) using the loss function as supervision of the vocal cord disease classification model to obtain a trained vocal cord disease classification model; and S5) inputting the pronunciation of the to-be-classified testee into the vocal cord disease classification model.According to the method, through organic combination of convolution kernel design, attention mechanism embedding and a lightweight channel expansion strategy, efficient and accurate recognition of pathological voice is realized.

The application discloses a kind of Zhang Xizheng pure Chinese medicine theory prescription medicine digital inheritance and intelligent analysis system.The system includes disease and syndrome differentiation rule engine subsystem, prescription three-way search subsystem, drug knowledge enhancement subsystem, medical record intelligent search subsystem, Zhang Xizheng school review subsystem and large language model interaction subsystem.The method performs the following steps: LLM clinical information extraction and disease prediction (extract symptoms, tongue, pulse, and 8 big disease classification prediction), disease and syndrome differentiation KB rule matching (8 big disease classification x multiple syndrome type weighted scoring matching), multi-source search (prescription three-way search+drug knowledge double-way vector recall and prescription-drug dynamic association+medical record multidimensional mixed scoring search), LLM fusion generation (inject all search results Zhang Xizheng school special prompt word generation theory prescription medicine analysis report) and Zhang Xizheng school four-dimensional review (western medicine perspective+air theory+drug use principles+theory prescription medicine consistency).The present application first realizes the computer formalization expression of Zhang Xizheng "disease differentiation before syndrome differentiation" mode and "air monism" theory, based on "Medical Western Medicine Record" 172 prescriptions+213 drugs / medical theory / medical conversation+137 cases to build a complete knowledge search and intelligent analysis system, with the ability of western medicine perspective+air theory+drug use principles+theory prescription medicine consistency.

The present invention relates to a diagnosis assistance device and method based on artificial intelligence processing of a radiographic image, wherein a disease diagnosis assistance device according to an embodiment of the present invention may comprise an information providing unit that constructs a diagnosis assistance model by learning a training radiographic image and additional information through a first feature processing unit, a second feature processing unit, and a feature fusion processing unit, determines a bone mineral density value and disease classification information with respect to a radiographic image to be diagnosed on the basis of the diagnosis assistance model, and provides disease diagnosis assistance information including the bone mineral density value, the disease classification information, and diagnosis basis information.

The invention relates to the technical field of skin disease classification, in particular to an auxiliary skin disease classification method and system based on multi-modal medical data, and the method comprises the steps: extracting features of a plurality of types of first data, and obtaining a plurality of initial feature vectors; performing low-level fusion and high-level fusion according to the plurality of initial feature vectors to obtain a comprehensive feature vector, inputting the comprehensive feature vector into a classifier, and determining a predicted skin disease category; and generating a diagnosis report according to the predicted skin disease category. According to the invention, low-level fusion and high-level fusion are carried out according to the plurality of initial feature vectors to obtain the comprehensive feature vector, and the predicted skin disease category is determined by fusing the data of the plurality of categories of the skin diseases and inputting the comprehensive feature vector into the classifier, so that the accuracy of skin disease classification is improved.

The application discloses a medical data processing method and product based on multi-stage transfer learning and multi-modal data collaborative fusion, relates to the technical field of medical data processing and artificial intelligence, and adopts MedicalNet medical special pre-training weights to initialize a classification model backbone network; a three-stage progressive fine-tuning framework is constructed on the basis, field adaptive coarse classification fine-tuning and target task fine classification are performed, and clinical structured data is introduced in the third stage; after high-dimensional image features are reduced in dimension and low-dimensional clinical features are increased in dimension through an adaptive multi-branch multi-level MLP architecture, mid-term deep fusion is performed, the application can effectively mine the complex relationships such as complementation, correlation and cooperation of multi-modal heterogeneous data, improve the lung disease classification precision and model generalization capability, and significantly inhibit the small sample overfitting phenomenon.

The invention discloses an Alzheimer's disease classification prediction method and system. According to the method, firstly, a bimodal brain network diagram is constructed, brain region features serve as nodes, and edge features are constructed through single nucleotide polymorphism data and structural magnetic resonance imaging data; the method is characterized in that closed-loop information interaction between nodes and edges is realized through a mutual feedback backflow graph neural network: firstly, edge features are dynamically updated based on node similarity, and information transmission from the nodes to the edges is realized; carrying out aggregation propagation and key screening among edge features by utilizing a topological neighborhood relationship; and finally, returning the edge feature information subjected to cross-modal fusion to the nodes, and updating node features. According to the method, the limitation that an existing graph neural network only pays attention to edge-to-node one-way information flow is overcome, and by describing dynamic coupling of nodes and edges and complementation of multi-modal information on a connection layer, the capturing capacity of early and tiny pathological modes of the Alzheimer's disease is enhanced, so that the accuracy and interpretability of classification prediction are improved.

The invention provides a lung rare disease CT image automatic auxiliary diagnosis system based on deep learning, and belongs to the technical field of deep learning and auxiliary diagnosis, and the system comprises a data collection and preprocessing module which is used for carrying out the privacy processing, format conversion, standardization processing and data enhancement of CT image data, and outputting a three-dimensional input block meeting the model input requirements; the three-dimensional feature modeling and training module is used for taking the three-dimensional compact connection neural network as a core, learning morphological and distribution features of cystic lesions through a training strategy, and constructing and training to obtain a disease classification model; the online reasoning and interpretable display module is used for performing probability calibration on the trained model output to generate a visual result; and the clinical application module is used for receiving the current preprocessed CT image, inputting the CT image into the calibrated model for processing and analysis, and outputting a classification result and a visualization basis. High-dimensional structure modeling and credible reasoning of the rare diffuse lung disease image are realized, and AI auxiliary diagnosis is facilitated.

The application relates to the technical field of natural language processing, and particularly discloses a disease classification method and device and a medium, the method comprising the following steps: performing data preprocessing and data enhancement on a medical text data set to obtain medical text data; analyzing the medical text data to construct a heterogeneous graph comprising patient nodes, symptom nodes and disease nodes; performing hierarchical attention training on the heterogeneous graph in combination with the relationships among the patient nodes, the symptom nodes and the disease nodes to obtain a disease classification model; inputting patient node data in the heterogeneous graph into the disease classification model to output disease probability and complete disease classification. The method prevents data sparseness through data enhancement, realizes relationship modeling by constructing a heterogeneous graph of medical text, thereby preventing data fragmentation, and finally adopts hierarchical attention training to dynamically screen weights, ensures effective information transmission, and ensures the robustness and interpretability of the medical text classification result.

The invention discloses a multi-scale microscope pathological image flow analysis method based on deep learning. The method comprises the following steps: acquiring pathological image data of a slice and disease classification information of the slice; the acquired pathological image is preprocessed; a multi-scale feature fusion classification network is constructed, the preprocessed training data set is used for training, and the trained multi-scale feature fusion classification network is obtained; obtaining positive and negative labels by using the trained multi-scale feature fusion classification network, and training the constructed multi-scale feature fusion classification network again by using a positive and negative label combined training method to obtain a final model; and carrying out data preprocessing on the collected microscope pathological image, inputting an obtained data set into the trained multi-scale feature fusion classification network model, and obtaining classification prediction of the model on diseases. According to the method, after multi-scale pathological information is fused, the slice samples are classified, and a positive and negative label combined training method is adopted, so that the prediction effect of the model on the image frame is effectively improved.

The invention relates to a tunnel leakage water disease automatic identification method, device and equipment and a medium thereof. The method comprises the following steps: acquiring multi-source original data in a tunnel; preprocessing the multi-source original data to obtain multi-sensor data; the multi-source original data comprises visible light images, thermal imaging and point cloud data; dividing a tunnel section along the center line of the tunnel based on the multi-sensor data to obtain a tunnel section sequence; the tunnel section sequence comprises each tunnel section and the corresponding multi-sensor data; based on the tunnel section sequence, performing binary segmentation on the tunnel section to obtain a section binary segmentation mask; and based on the section binary segmentation mask, identifying the leakage water disease type of each tunnel section, and obtaining a disease classification result. By adopting the method, a unified section reference coordinate system can be established to track a disease development track, and further disease development can be accurately warned.

The embodiment of the application discloses a kind of Alzheimer's disease classification and key brain area determination method based on random attention and counterfactual contrast learning, it is related to medical image analysis technical field;Alzheimer's disease classification and key brain area are determined by the functional magnetic resonance imaging data of subject;The accuracy, overall performance and stability of the graph convolution network model are significantly better than the existing GNN baseline model in the AD vs.NC task of ADNI real data set;The output key brain area node is highly consistent with clinical medicine priori;Specific brain area and functional connection leading to abnormal classification can be intuitively presented;It conforms to the real pathological mechanism, and whether the verification model in the same framework is really highly dependent on the selected key brain area structure, so that the explanation result can be accepted by clinician in the medical scene with extremely high safety requirement.

The application discloses an Alzheimer's disease classification method based on channel residual attention. The method classifies Alzheimer's disease by fusing the characteristics of residual modules and channel attention mechanisms. The network is composed of an input module, a channel separation residual module, a channel attention module and an output module. Firstly, aiming at the problems existing in the current research data set, a scientific division strategy of the data set is formulated under the guidance of a clinician; then the processed data are sent into the channel separation residual module to extract the shallow and deep features of the network and prevent repeated gradient information; then the extracted features are sent into the channel attention module to adjust the weight between channels and obtain more accurate classification features; finally, the feature matrix is sent into a linear classification layer to output a classification result. The method has broad application prospects in the field of medical images and Alzheimer's disease classification.

The application discloses an Alzheimer's disease pathological region positioning and classification prediction method, comprising the following steps: constructing an Alzheimer's disease classification model; constructing a visual explanation model based on counterfactual reasoning; introducing a three-dimensional coordinate attention mechanism guided by a counterfactual graph to further enhance the classification model; and iterating the explanation model and the Alzheimer's disease classification model. The counterfactual reasoning explanation model divides fine pathological regions by using a counterfactual reasoning method, uses the position information of the generated pathological regions to guide the classification model, and enables the classification model to focus on learning the discriminant region related to the disease and to have higher sensitivity to the pathological region; the three-dimensional coordinate attention mechanism is used to obtain the dependency relationship between three-dimensional image regions and to retain the accurate position information in the three-dimensional space, the model is more likely to obtain the interested region, the whole brain structure is analyzed, and thus the training efficiency and the accuracy of the classification model are improved, and the model has higher robustness.

The application discloses an Alzheimer's disease stage recognition method and system based on an interpretable multi-modal, and the method comprises the following steps: acquiring and preprocessing sMRI images of Alzheimer's disease patients and corresponding clinical texts to generate a clinical data set; constructing a multi-modal enhancement fusion model comprising an image feature extraction channel, a text feature extraction channel, a Mamba global sequence module embedding an image feature extraction channel and a feature fusion module based on a gating mechanism; extracting image features and text features of the clinical data set, and performing cross-modal interaction by using a multi-head attention mechanism to generate a feature fusion sequence; inputting the feature fusion sequence into a convolution-based multi-layer perceptron for feature enhancement, performing an Alzheimer's disease classification task and generating a classification result; and using a test set of the clinical data set and a ten-fold cross-validation method to quantitatively analyze the model performance on the Alzheimer's disease classification task, and integrating post-hoc interpretability technology to analyze the model classification result.

The invention discloses a kidney stone, hydronephrosis and pyosis detection system based on multi-scale feature fusion, and belongs to the technical field of medical detection, and the working process of the detection system comprises the following steps: S1, image data acquisition and preprocessing; s2, feature extraction and multi-scale feature fusion of the model; s3, classification decision and output layer design; and S4, model training and optimization. According to the system, from original DICOM image input to final diagnosis report generation, full-process automatic processing is achieved, manual intervention on feature engineering or intermediate steps is not needed, the system can generate a structured diagnosis report which comprises specific disease classification, confidence score and visual evidence heat map, the output format is normative, and the diagnosis report can be used for diagnosis. The method is easy to integrate with an existing image archiving and communication system of a hospital, and the model is clearly guided to pay attention to specific features related to ponding and infection in the learning process by introducing independent ponding and infection auxiliary discrimination branches and performing joint optimization with a main classification task.

The invention provides an automatic international disease classification code coding method based on a multi-synonym matching network. The method comprises the following steps: S1, preparing a medical text database; s2, a main structure layer of a multi-synonym matching network algorithm model is constructed, a main calculation structure is designed, and the main structure layer of the multi-synonym matching network algorithm model comprises an encoder, a label encoder and a decoder; s3, training by using the prepared data set for training to obtain a model weight for reasoning, and evaluating a training result; s4, setting of various parameters of the multi-modal large language model is adjusted, the multi-modal large language model is designed to be combined with a multi-synonym matching network algorithm, the function of the multi-modal large language model is matched with a method target task, and a multi-modal ICD coding model based on a multi-synonym matching network is obtained; and S5, testing the multi-mode automatic international disease classification code coding model based on the multi-synonym matching network to enable the model to meet task requirements. According to the method disclosed by the invention, the ICD coding accuracy and convenience are effectively improved.

The application provides a kind of generation device and method of image recognition model, it is related to image recognition model technical field, the application first constructs initial model, including sequentially connected anatomic perception module, sign discovery module and evidence reasoning module;Integrate credibility prediction sub-network in evidence reasoning module to calculate the credibility score vector of sign feature vector;Sample image is associated with three types of annotations of pixel level, area level and image level, and a training dataset is constructed;Adopt multi-task joint training mode, and total loss function includes anatomical segmentation loss, sign recognition loss, disease classification loss and credibility prediction auxiliary loss;Save model parameters after training, and obtain medical image recognition model.The application can output accurate disease classification results, and provide clear diagnostic basis, while improving the robustness and explainability of model decision through credibility evaluation, suitable for medical image assisted diagnosis and other scenarios.

The invention provides a graph neural network modeling and causal interpretation method for mental disease recognition. The method comprises the following steps: constructing multi-modal data; performing label labeling on the multi-modal data according to the disease type, and forming a data set by labels and the multi-modal data; constructing an initial model, wherein the initial model comprises a graph construction module, a graph neural network module, a causal interpretation module and a prediction and interpretation output module; training the initial model by using the data set to obtain a graph modeling model for disease classification; and inputting the multi-modal data of the patient into the graph modeling model to obtain a prediction result of the patient. The invention also provides a graph neural network modeling and causal interpretation system. The method provided by the invention is suitable for personalized diagnosis and intervention path analysis, and has good practical application potential.

The application discloses an anterior segment disease identification method, system, device and storage medium. The anterior segment disease identification method comprises the following steps: acquiring an image to be identified; inputting the image to be identified into a trained semi-supervised segmentation model to obtain a first image segmentation result output by the trained semi-supervised segmentation model; extracting a feature vector of the first image segmentation result; inputting the feature vector into a trained support vector machine model to obtain a disease classification result output by the trained support vector machine model; selecting a corresponding high-precision disease diagnosis model from a high-precision disease diagnosis model set based on the disease classification result, and taking the high-precision disease diagnosis model as a high-precision disease diagnosis model to be used; determining a second image segmentation result from the first image segmentation result based on the disease classification result; inputting the second image segmentation result and the image to be identified into the high-precision disease diagnosis model to be used to obtain an anterior segment disease identification result of the image to be identified, and the efficiency and accuracy of diagnosis are improved.

The invention discloses an intelligent inspection method and system for apparent diseases of railway housing construction equipment, and belongs to the field of railway facility apparent disease detection. The problems of high risk, low efficiency, strong subjectivity and low intelligent degree of the existing railway housing construction equipment apparent disease manual detection means are solved; according to the technical scheme, the method comprises an intelligent tour inspection link and an intelligent detection link, the intelligent tour inspection link realizes tour inspection task planning, three-dimensional model construction, tour inspection route planning and route task generation, downloads and issues the generated route task to a flight control unit of an unmanned aerial vehicle, and controls the unmanned aerial vehicle to execute tour inspection operation according to the route task; finishing acquisition of image data of the housing construction equipment; in the intelligent detection link, image data stored in a storage unit of the unmanned aerial vehicle platform is transmitted to an intelligent detection terminal, the intelligent detection terminal carries out disease classification and identification on disease image data, and two-dimensional positioning and three-dimensional calibration are carried out on disease positions in combination with geographic space information; and generating an inspection detection report.

The invention discloses a Parkinson's disease classification method and system based on Transform and a graph neural network, and belongs to the technical field of medical image analysis. According to the model, a multi-level feature extraction and fusion scheme combining visual Transform, a frequency domain network and an image convolutional network is provided aiming at the defects of an existing method in the aspects of feature fusion and image region relation modeling. The method specifically comprises the following steps: preprocessing and slicing an MRI image; extracting spatial domain features by using ViT, and extracting frequency domain features by using GFNet; designing an adjacent matrix construction method based on patch center points, and modeling a topological relation between patches through a Gaussian attenuation function; carrying out fusion and relation modeling on the multi-source features by adopting a GCN; and finally, realizing classification of Parkinson's disease and health control through global pooling and a classification head. According to the method, space and frequency domain information is effectively fused, expression of local and global features is enhanced, classification accuracy and robustness are improved, and reliable technical support is provided for auxiliary diagnosis of Parkinson's disease.

The application relates to a mild cognitive impairment development process prediction method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring first diffusion tensor imaging images of a mild cognitive impairment patient in multiple follow-ups; processing each first diffusion tensor imaging image, extracting white matter fibers between all hippocampal voxels and other brain voxels; performing regional segmentation on the hippocampus according to the white matter fibers between all hippocampal voxels and other brain voxels, and constructing hippocampal subregions; constructing a first gradient change feature of the white matter fibers of each hippocampal subregion according to the gradient change of the target feature of the white matter fibers of the hippocampal subregion; and predicting the cognitive impairment development process of the measured object according to the basic information, clinical information and first gradient change feature of the white matter fibers of the hippocampal subregion of the patient. The method can improve disease classification accuracy and specificity, and can predict the cognitive impairment development process of the measured object.

The invention relates to the technical field of image recognition, in particular to a silkworm disease recognition system which comprises an image gray analysis module, a self-adaptive image enhancement module, a multi-dimensional feature extraction module, a feature saliency evaluation module, a dynamic weight distribution module and a disease classification mapping module. Identifying an overall gray level distribution condition and a local gray level fluctuation condition in the original image data of the target silkworm body to determine a dynamic adjustment parameter, and correcting the original image data to obtain enhanced image data; integrating multi-dimensional feature information in the enhanced image data into silkworm body state feature information; judging the significance degree of different types of information in the silkworm body state characteristic information for distinguishing different diseases so as to allocate the emphasis proportion of the characteristic information in the silkworm body state characteristic information; fusing the emphasis proportion and the silkworm body state feature information, and mapping the fused silkworm body state feature information to a preset silkworm disease feature template library to obtain disease categories; according to the invention, the accuracy of silkworm disease recognition can be improved.

The application provides a medical guidance information generation method, device, equipment, medium and product, wherein the method comprises the following steps: obtaining a call voice text, performing entity and key information extraction on the call voice text to obtain entity information and key field information; determining a disease classification result and a disease classification result based on the entity information and the key field information; inputting the entity information, the key field information, the disease classification result and the disease classification result into a language processing model to obtain medical guidance information output by the language processing model. The application can reduce the work burden of an operator.

The application discloses a kind of multi-disease prediction systems based on binocular fundus image, including fundus feature extraction module, similarity-difference feature fusion module, clinical feature extraction module, multi-modal feature fusion module and multi-disease classification prediction module.System uses unified backbone network to extract left and right fundus image features, by similarity-difference feature fusion module, the features of two-way are added to each element to obtain preliminary fusion features, and the absolute value is obtained by subtracting each element to obtain different features, after normalization and 1 complement operation generates dissimilarity weight and similarity weight, separate out dissimilarity information and similarity information after weighting fusion according to hyperparameter α Fusion fundus feature is obtained;Multi-dimensional clinical variable features are extracted by two fully connected layers, after fundus features and clinical features are spliced, input multi-disease classification prediction module, and output disease prevalence probability.The application makes full use of the correlation and complementarity of binocular fundus image, and realizes high-precision, low-cost multi-disease screening combined with clinical indicators.