804 results about "Aided diagnosis" patented technology

The invention discloses an inquiry model training method and device based on a long thinking chain, and relates to the field of large models, semantic information is extracted through strategy network analysis of a model, and an initial step decision is generated in combination with context information in a historical memory library; sending the initial step decision into a reasoning path generator, and reasoning to generate a primary diagnosis disease source and an intermediate diagnosis step; sending the primary diagnosis source and the intermediate diagnosis step into a verification module, performing pathological logic verification according to a case diagnosis report and a medical knowledge base, and feeding back a verification result; the reasoning path generator updates the historical memory bank based on the feedback result, the preliminary diagnosis disease source and the intermediate diagnosis steps; the strategy network continues reasoning based on user feedback input and the updated context information in the historical memory bank, and finally an inquiry result is output. According to the scheme, technical means such as reinforcement learning, self-adaptive backtracking and memory enhancement are introduced into a long thinking chain reasoning framework, so that a large language model realizes multi-aspect comprehensive improvement in medical question and answer and auxiliary diagnosis scenes.

The invention belongs to the technical field of medical artificial intelligence, and discloses an artificial intelligence-based auxiliary diagnosis and treatment system, which comprises a multi-modal data acquisition module, a dynamic learning module, a diagnosis reasoning module, a privacy protection module, an interactive decision module and an early warning monitoring module, the output end of the multi-modal data acquisition module is connected with the input end of the privacy protection module, the output end of the privacy protection module is connected with the input end of the dynamic learning module, the output end of the dynamic learning module is connected with the input end of the diagnostic reasoning module, and the output end of the diagnostic reasoning module is connected with the input end of the interactive decision module. And the early warning monitoring module monitors abnormal data in real time and performs bidirectional interaction with the diagnosis reasoning module. According to the method, multi-source medical data are integrated, and high-precision real-time auxiliary diagnosis is realized by adopting a dynamic incremental learning and privacy encryption technology; the medical worker cooperation efficiency is improved through an interactive interface, the safety is guaranteed in combination with real-time monitoring and early warning, and the system can remarkably improve the diagnosis and treatment efficiency and accuracy.

The invention relates to the technical field of battery fault detection, and particularly discloses a new energy automobile battery fault intelligent diagnosis method and system, and the method comprises the steps: collecting the three-dimensional time sequence data flow of the voltage, temperature and internal resistance of a battery pack through a distributed sensor array; the environment temperature, the number of charge and discharge cycles and the vehicle operation condition parameters are synchronously integrated as auxiliary diagnosis dimensions, and a multi-source heterogeneous data sensing system is constructed; the method comprises the following steps: preprocessing original data by adopting a dual-channel hybrid filtering architecture based on a multi-source heterogeneous data sensing system, and establishing an adaptive filtering parameter adjustment mechanism to generate multi-modal data; according to the method, multi-dimensional time sequence data and auxiliary diagnosis parameters of the battery pack are comprehensively collected through a constructed multi-source heterogeneous data sensing system, a rich and accurate data basis is provided for fault diagnosis, noise interference is reduced through a two-channel hybrid filtering architecture and a self-adaptive filtering parameter adjustment mechanism, and the fault diagnosis accuracy is improved. And a solid foundation is laid for subsequent feature extraction and model training.

The invention provides a disease screening system based on a large model. The disease screening system is used for solving the technical problem that an existing disease screening system is intelligently used for single special disease screening. The system comprises a scheduling model and a plurality of AI auxiliary diagnosis models, the scheduling model is connected with the plurality of AI auxiliary diagnosis models, and the scheduling model is connected with a big data disease library. According to the method, a high-level scheduling model is utilized, multiple single AI auxiliary diagnosis models are managed in a centralized mode, automatic calling of a multi-disease AI system is achieved, automatic structured report generation is achieved through a large language model, historical medical history is combined, progress is predicted, treatment suggestions and reference cases are automatically given, an existing clinician film reading workflow is fitted, and the efficiency is improved. The whole process of actual diagnosis decision making of doctors is greatly fitted, and the working efficiency of the doctors can be improved to the maximum extent.

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 invention relates to the technical field of medical artificial intelligence, in particular to a pulmonary nodule malignant risk dynamic prediction method and system based on space-time attention and multi-modal data guide fusion. Clinical features are obtained based on clinical data, and a lung CT slice image is obtained based on a lung CT image; the method comprises the following steps: extracting general medical visual features in an image by using an open-source medical large model, extracting image features in a lung CT slice image through a visual Transform model, and performing feature guide optimization on the image features based on clinical features and the general medical visual features to obtain patient image specific features; performing joint mapping on the specific features of the patient image by using a space-time attention mechanism to obtain space-time enhancement features of the patient image; and performing weighted fusion on the clinical features, the general medical visual features and the space-time enhancement features of the patient by using a gating mechanism, and obtaining the risk prediction probability of the malignant pulmonary nodules of the patient by using a classifier. The method can predict the malignant probability of the pulmonary nodule, and facilitates the realization of auxiliary diagnosis such as pulmonary nodule screening.

The invention discloses an Alzheimer's disease auxiliary screening system based on intelligent interaction, particularly relates to the field of computer-aided diagnosis, and is used for solving the problem that in an existing screening method, it is difficult to quantify cognitive performance differences of life knowledge and professional knowledge at the same time. The method comprises the following steps: constructing a task interaction group covering life knowledge and professional knowledge, collecting and coding multi-dimensional interaction operation of a testee in a task process, and generating an interaction vector matrix; further performing jump point identification, vector residual calculation and invalid operation density statistics on the interaction behavior through a preset task logic chain, and quantifying the operation deviation condition of the interaction behavior; respectively generating life and professional cognitive performance indexes based on the analysis result, and constructing a cognitive deviation evolution curve of a difference value of the life and professional cognitive performance indexes along with time change in a set period; and finally, through identifying a continuous rising trend in a cognitive deviation evolution process, auxiliary screening and early warning of asymmetric degradation of cognitive competence are realized.

The invention relates to the technical field of medical image processing and computer vision, in particular to a medical image segmentation method and system based on deep learning, the method is based on a U-shaped encoder-decoder architecture, a DSAB module is introduced into an encoder, and context perception of a directional anatomical structure is enhanced through complementary directional space shift and CSA mechanism weighting; an MGCF module is designed in a decoder, and a parallel multi-scale convolution path and an AGCA mechanism are combined, so that multi-level features are efficiently fused to recover boundary details. Meanwhile, links of data preprocessing, Transform structure details, segmentation result post-processing and the like are supplemented, the model performance is improved through a mixed loss function and an optimization training strategy, and the method has remarkable advantages in segmentation precision and boundary definition and provides powerful support for clinical auxiliary diagnosis.

The invention discloses a three-dimensional positron emission tomography data image classification method based on multi-stage progressive feature extraction, and is applied to the technical field of Alzheimer's disease auxiliary diagnosis. The auxiliary diagnosis method comprises the following steps: acquiring and preprocessing PET image data of an Alzheimer's disease patient; improving the reliability of the data set by using data enhancement; performing long-range dynamic modeling on the three-dimensional voxel sequence through a stacked Lmamba block; global context semantic adaptive fusion is realized through a layer-by-layer cross-scale channel attention fusion module (CSACF), and a channel spatial perception module (CSPM) is constructed to optimize spatial feature fusion; an inverted bottleneck module is mixed with long-distance space and position information to enhance the capturing capability of the model on detail features; and finally, predicting the disease category probability through global average pooling, full connection and softmax functions. According to the method, the precision of AD early diagnosis and MCI conversion risk prediction can be greatly improved, the defects of a medical image diagnosis method of a convolutional neural network (CNN) and Transform in long-range dependence on modeling and calculation complexity are overcome, and the method has good application prospects and is suitable for AD early detection and MCI conversion risk assessment.

The invention discloses a cervical cytopathy detection method based on a hypergraph convolutional network. The method comprises the following steps: performing sliding window slicing processing, unsupervised image decomposition and dyeing normalization on a cervical cytopathy image, generating normalized image input, and constructing an image input sample set; a target detection model is constructed based on YOLO11, a block-level feature extraction network (BBMM) module is embedded to enhance the perception ability, and a sparse attention module is adopted to perform key region feature enhancement; constructing a hypergraph neural network HGNN module based on a Patch-level relationship, and extracting a structural relationship between cells; integrating an uncertainty quantification mechanism, and generating a confidence thermodynamic diagram; a front-end and rear-end separated diagnosis platform is built, image uploading, detection result display, frame selection correction and interactive management are supported, and whole-process auxiliary diagnosis is achieved; the method has the advantages of high detection accuracy, high interpretability, flexible deployment and the like, and is suitable for intelligent early screening and clinical auxiliary diagnosis scenes of cervical cytopathy.

The invention discloses an intelligent auxiliary diagnosis system and method based on big data analysis, and relates to the field of intelligent auxiliary diagnosis. Original CT scanning data and electronic medical record data of a patient are processed in parallel, and a deep learning model is introduced to extract high-dimensional image features and text features; through constructing a knowledge prior relation matrix derived from clinical guidelines and expert rules, knowledge-guided cross-modal attention fusion is carried out on image features and text features so as to realize deep and accurate joint characterization of patient conditions, and then intelligent risk prediction and classification are carried out on the basis of the deep and accurate joint characterization. Thus, through deep coupling of data-driven feature learning and medical knowledge-driven logical reasoning, intelligent and logical integration of multi-modal medical information can be realized, and the accuracy and reliability of an auxiliary diagnosis model in a complex clinical scene are effectively improved.

The invention relates to the technical field of medical image intelligent analysis and artificial intelligence auxiliary diagnosis, in particular to a medical image disease course prediction system based on an industrial neural network, and the system comprises a reference generation module which is used for obtaining static image data; processing the static image data by using a physical perception neural network to generate a pure ideal state reference; a perturbation simulation module; the industrial kinetic parameters are used as perturbation terms to be superposed to a pure ideal state reference, and a theoretical damaged state is generated; the projection verification module is used for acquiring real multi-modal observation data; generating a real residual error; generating a theoretical residual error based on the theoretical damaged state and the pure ideal state reference; calculating a manifold coupling confidence coefficient; the closed-loop correction module is used for performing inversion optimization on the industrial kinetic parameters; outputting a disease course prediction result according to the manifold coupling confidence coefficient; according to the method, the problem that a traditional medical model lacks physical consistency explanation is solved, and the credibility of artificial intelligence auxiliary diagnosis is remarkably improved.

The invention belongs to the field of medical artificial intelligence, and particularly relates to a multi-modal data acquisition and fusion method for Alzheimer's disease. The method comprises the following steps: firstly, synchronously acquiring eye movement, expression, voice, gait and grip strength data of a subject through a virtual reality multi-task normal form, and combining with an MoCA scale to score a result; then preprocessing and feature extraction are carried out on each modal data, and unified feature representation is constructed; on the basis, a cross-modal attention mechanism is adopted to realize interaction and weighted fusion of multi-modal features, and a unified fusion feature vector table is generated; and finally, outputting structured data organized according to task fragments for auxiliary evaluation and modeling of cognitive impairment. The method can effectively solve the problems that in the prior art, single-mode information is insufficient, and multi-mode data are difficult to align and fuse, has the advantages of being low in cost, easy to popularize and high in detection accuracy, and can be widely applied to early recognition and auxiliary diagnosis of the Alzheimer's disease.

The invention relates to the technical field of medical and multi-modal large models, and discloses a disease prediction and auxiliary diagnosis system construction method and system based on a multi-modal large model. The method comprises the steps of report format conversion, data cleaning and table image-to-structured text conversion. Constructing a retrieval knowledge base to enhance the retrieval capability; precise cue word design and reasoning optimization are carried out; small sample learning and model fine tuning; the invention discloses a multi-modal large model integration and visualization system. According to the system, the accuracy problem of a traditional disease risk prediction method is effectively solved, and a more reliable auxiliary diagnosis tool is provided.

The invention relates to the field of traditional Chinese medicine clinical auxiliary diagnosis, and discloses an acupuncture point recommendation method, an acupuncture point recommendation model obtaining method, an acupuncture point recommendation model obtaining device, acupuncture point recommendation equipment and a medium, and the acupuncture point recommendation model obtaining method comprises the following steps: collecting patient medical record data to form a medical record data sample set; preprocessing the medical record data sample set to obtain a standard medical record data sample set; inputting the standard medical record data sample set into a preset language model, performing preliminary training on the language model based on the standard medical record data sample set, so that the language model establishes a semantic mapping relationship between the patient symptom information and the acupuncture points, and performing parameter fine tuning and strategy optimization on the preliminarily trained language model in sequence, obtaining an acupuncture point recommendation model; according to the acupuncture point recommendation model obtaining method, the accuracy and individual adaptability of acupuncture clinical treatment are improved, the diagnosis and treatment efficiency is optimized, and a scientific and systematic treatment scheme is provided for a patient.

A method of providing vehicle diagnostics includes receiving a request for AI-assisted diagnostics, receiving diagnostic data obtained from a vehicle, prompting a user to describe one or more symptoms exhibited by the vehicle, processing the one or more symptoms and the diagnostic data at least in part using a natural language processing (NLP) model, and providing natural language guidance to the user based on a result of the processing. The natural language guidance may include testing instructions for the user to perform a step-by-step procedure to diagnose the vehicle and / or repair instructions for the user to perform a repair on the vehicle.

The invention discloses a cervical lesion intercellular relation modeling and analysis system based on a graph neural network, and the system comprises a medical image collection module which is used for collecting a digital image of a cervical tissue pathological section or a cervical TCT slide; the cell detection and segmentation module is used for extracting spatial position information and morphological characteristics of cells; the cell feature extraction module is used for extracting and fusing the spatial position, morphology, texture and biological marker features of the cells; the cell relation graph construction module is used for constructing a heterogeneous cell relation graph with cells as nodes and inter-cell relations as edges; the graph neural network analysis module is used for carrying out feature learning and modeling on the heterogeneous cell relation graph; the intelligent auxiliary diagnosis module is used for generating auxiliary diagnosis suggestions; and the data management and automatic control module is used for realizing automatic control and case data management of the whole process of the data. The intelligent and automatic level of cervical lesion cell analysis can be comprehensively improved, and the accuracy and efficiency of diagnosis are improved.

The invention discloses a newborn face feature rare disease auxiliary diagnosis system based on AI image recognition, relates to the technical field of rare disease auxiliary diagnosis, and aims to solve the technical problem that dynamic feature analysis is insufficient in existing newborn rare disease auxiliary diagnosis. Detection, segmentation, feature classification and dynamic change analysis are completed, and abnormal textures, pigment distribution or blood vessel features are identified; the face key point analysis module is used for marking face key anatomical points, calculating three-dimensional space coordinates and analyzing relative positions and geometrical relationships of the key points and evolution trends of the key points along with time; the dynamic modeling module is used for constructing a dynamic evolution model of the facial features of the newborns along with the change of day ages, and correlating the time dependence of growth and development stages and rare disease features; and the feature fusion unit is used for integrating output results of the skin analysis module, the face key point analysis module and the dynamic modeling module. The method has the advantage of improving the diagnosis accuracy.

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.

The invention relates to the technical field of medical image auxiliary diagnosis, in particular to a nuclear magnetic resonance image auxiliary diagnosis method and system based on multi-modal fusion. The method comprises the following steps: step 1, synchronously acquiring a three-dimensional T1 weighted structure image, a T2 weighted fluid attenuation inversion recovery image and diffusion weighted imaging data of a subject, carrying out spatial registration by taking the T1 weighted image as a reference, and executing skull stripping and gray scale standardization; 2, individualized brain region segmentation is carried out based on a brain anatomical map, the lesion sensitivity weight of each modal is calculated for each segmented brain region, and the weight is obtained by quantifying the following parameters; step 3, extracting multi-modal image features in each brain region; and 4, inputting the fusion features of the whole brain region into a multi-task classifier. The standardization and alignment of the multi-mode MRI image in the space and gray level are realized, and the problems of space mismatch and feature interference among different modes are effectively solved.

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 relates to the technical field of state monitoring, in particular to a wind turbine generator main transmission chain monitoring system based on cloud edge collaboration, which comprises a signal synchronization module, a fault cause identification module, a local classification module, a thermal distribution positioning module and a load evaluation module. According to the method, the key parameters of the main transmission chain are extracted in real time and the timestamps are structured, so that accurate signal synchronization and abnormity elimination are realized, the integrity and time sequence consistency of multi-source data are improved, and the fault indexes are extracted in combination with parameter fluctuation characteristics and threshold screening; a trigger list is constructed based on double judgment of peak value and rate, edge and cloud tasks are linked, node-level classification response is realized, a thermodynamic concentration area is positioned through parameter comparison of abnormal nodes and adjacent nodes, the accuracy and visualization effect of fault aggregation identification are enhanced, and the rotation speed and current change in the thermodynamic area are extracted to evaluate the load trend. And the dynamic identification of the local operation state change and the improvement of the auxiliary diagnosis capability are realized.

The invention discloses a CT (Computed Tomography) image pulmonary embolism segmentation and classification method combined with quality evaluation, which relates to the technical field of image processing, and comprises the following steps: inputting a 256 * 256 pulmonary embolism CT image and a quality score thereof into a quality score guide encoder, expanding a quality score dimension through linear transformation, and carrying out point product fusion with a feature map extracted by ResNet34 layer by layer to obtain a final product; generating multi-scale coding features; performing wavelet domain decomposition and reconstruction on the coding features through a wavelet transform fusion jump link module, and optimizing feature transmission; a multi-scale cross enhanced decoder is adopted to carry out multi-scale deconvolution fusion on the features, a segmentation result is output in combination with an efficient channel attention mechanism, meanwhile, pulmonary embolism existence judgment is output through a classification head, and the method provides powerful support for early diagnosis of pulmonary embolism, development of an image auxiliary diagnosis system and clinical application, and has good application prospects. Wide application prospects and profound social significance are realized.

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

The invention relates to a large model auxiliary diagnosis method based on retrieval enhancement generation and a knowledge graph. The method comprises the following steps: S1, constructing a case database and a medical knowledge graph; s2, generating matched image-text pairs from the case database and the medical knowledge graph based on retrieval enhancement; s3, the matched image-text pairs are input into the multi-modal large model, an auxiliary diagnosis result is obtained, and a loss function trained by the multi-modal large model is a direct preference optimization function. Compared with the prior art, the method has the advantages of reducing diagnosis errors caused by subjective judgment and the like.

The invention discloses a hydroelectric equipment anomaly detection method based on physical mechanism guidance and sequential topological entropy fluctuation characteristics, and belongs to the technical field of hydroelectric equipment monitoring and fault diagnosis. The method comprises the steps that multi-source sensor data of hydroelectric equipment is collected and preprocessed; constructing a physical weighted distance function in combination with an equipment physical mechanism, and embedding time sequence data into a high-dimensional point cloud space; extracting persistent homology features through a sliding window, generating a persistent graph sequence and calculating topological feature indexes; a persistence graph entropy fluctuation index is provided, and anomaly detection is realized by quantifying time sequence fluctuation of topological entropy; and finally, visual output and an alarm mechanism are combined to assist diagnosis. According to the method, equipment physical characteristics and topological data analysis are fused, the problems that a traditional method is insufficient in nonlinear system modeling, insensitive to dynamic evolution and the like are solved, the early warning capacity and detection precision of early faults are improved, and the method is suitable for anomaly detection application of core equipment such as a water turbine and a generator.

The invention relates to the field of rail transit vehicle intelligent diagnosis, in particular to a train fault intelligent auxiliary diagnosis method fusing multi-source knowledge, which comprises the following steps of: constructing a multi-source knowledge document library, and performing text cleaning, segmented disassembly and vectorization processing to obtain a vector database; then obtaining a fault alarm list and capturing event information, and performing diagnosis according to the vector database to obtain a diagnosis suggestion; then time sequence operation parameters before and after the fault are extracted and analyzed, and a trend chart and characteristic indexes are generated to obtain an analysis result; constructing a multi-dimensional auxiliary analysis mechanism based on the diagnosis suggestion and the analysis result, and generating a maintenance suggestion in combination with historical maintenance work order information; and finally, generating a comprehensive auxiliary diagnosis report according to the maintenance suggestion, automatically generating a maintenance dispatching task list, determining a task execution sequence according to a preset priority scheduling algorithm, and executing the maintenance dispatching task list. The accuracy and timeliness of train fault diagnosis can be improved, and the safety, the operation and maintenance efficiency and the intelligent level of rail transit equipment are improved.

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 myocardial perfusion image classification method and system based on a single resting state, and belongs to intelligent analysis and auxiliary diagnosis of medical images. The method comprises the following steps: acquiring SPECT three-dimensional voxel data in the single resting state, and performing image reconstruction by adopting an OSEM algorithm; segmenting the myocardial region by using a pre-trained U-Net convolutional neural network, and mapping a segmentation result to a two-dimensional polar coordinate graph conforming to the AHA17 segment model; extracting a multi-dimensional feature vector; a single-phase inference model MSR-Net based on biphase labeling is constructed, in the training stage, segment classification labels of resting-load biphase images are used, in combination with segment consistency indexes, label correction is carried out, and in the inference stage, only resting state features are input, and then a pixel-level perfusion defect distribution diagram and ischemia scores of 17 myocardial segments can be output. Quantitative and segmental evaluation of myocardial ischemia can be completed by using single resting state imaging, exercise or drug load examination is avoided, and cardiovascular adverse events and complication risks are reduced.