813 results about "Medical imaging data" patented technology

The invention discloses a multi-modal medical image data intelligent processing system, relates to the field of medical image analysis, and is applied to multi-modal medical image whole-process analysis of CT, MRI, PET, ultrasound and the like. According to the system, different modal image features are extracted and fused through a cross-modal manifold fusion network; a semantic guidance dynamic registration engine optimizes registration parameters to ensure that the registration error is less than or equal to 1.5 mm; the multi-task collaborative diagnosis network realizes multiple tasks such as disease classification; the clinical knowledge embedding and interpretable module generates a structured report and is in butt joint with an HIS system. Meanwhile, the model is optimized through a federated learning architecture, the adaptability of newly added data is improved by more than or equal to 20%, and intelligent processing and analysis of multi-modal medical images are realized.

The invention discloses a medical image enhancement method and system based on multi-modal fusion, and the method comprises the steps: obtaining medical image data which comprises a CT image, an MRI image, a PET image and an ultrasonic image; calculating a mutual information value of each semantic tag in the cross-modal feature vocabulary, and generating an inter-modal feature correlation matrix; performing non-rigid space registration on the unified dimension image set to generate a geometrically consistent multi-modal image set; extracting skeleton region features, soft tissue region features and high metabolism region features from the multi-modal image set, and generating a core feature set; a deep learning algorithm is adopted to train the core feature set, and a segmented image set containing a segmentation mask is generated; and dynamically adjusting the fusion weight according to the regional features of the segmented image set, and generating a fusion enhanced image. According to the invention, through feature extraction, spatial registration and deep learning fusion of the multi-modal medical image, effective integration of different-modal medical image information is realized.

The invention relates to the technical field of medical image diagnosis, and discloses a medical image automatic diagnosis method and system based on deep learning. According to the method, multi-modal medical image data of a target object is acquired and standardized, a two-channel convolutional neural network is utilized to extract features, the features are processed through cross-modal feature fusion, adaptive attention weight distribution and other technologies, a cascaded two-way long-short-term memory network is adopted for modeling, abnormity is detected based on a probabilistic graph model, and the target object is identified. And the nidus is segmented by a multi-scale context information enhancement module, and finally a diagnosis suggestion is generated by a diagnosis inference engine driven by a knowledge graph. The system comprises a multi-modal image acquisition interface module, a distributed feature calculation cluster, a visual interaction terminal and a security audit module. According to the method, the accuracy and efficiency of medical image diagnosis can be improved, comprehensive diagnosis reference is provided for doctors, and meanwhile data safety and privacy are guaranteed.

The invention relates to the technical field of medical treatment, in particular to an AI-based medical image automatic segmentation and labeling system, which comprises an image data acquisition module, a data preprocessing module, a feature extraction module, an automatic segmentation module and an intelligent labeling module, the data preprocessing module improves image quality through noise suppression, artifact removal and modal alignment, the feature extraction module extracts a feature map of a potential lesion area, and the automatic segmentation module combines a full convolutional network, a bidirectional long-short-term memory network and an attention mechanism to realize accurate segmentation of an organ area and the lesion area. The intelligent labeling module is used for labeling anatomical names, lesion types and pathological features of the segmentation results by using a medical knowledge graph and a semantic embedding technology; the method has the advantages of being high in segmentation precision, comprehensive in marking and high in expansibility, and the automation and intelligence level of medical image analysis can be improved.

The invention relates to the technical field of medical image report error correction, in particular to a medical image report automatic quality control error correction system and method. The method comprises the steps of importing medical image data, analyzing the medical image data by using an open source tool, performing standardization processing and local contrast enhancement on an image, performing automatic segmentation of a target area through a UNet model, and calculating density features of a key area to obtain feature vectors; meanwhile, the report text data is subjected to word segmentation, standardization processing and keyword extraction steps, the word importance is evaluated by utilizing a BERT model, a semantic vector of the report is generated through a text model, finally, the consistency of the image and the report is detected by calculating the similarity between a feature vector and the semantic vector, and an automatic error correction program is started when the image and the report are inconsistent. And error types are analyzed and corrected. According to the method, medical image processing, natural language processing and deep learning technologies are combined, so that human errors are reduced, and the diagnosis efficiency and accuracy are improved.

The invention relates to the technical field of intelligent recognition, in particular to an intelligent tumor recognition system and method based on big data. The system comprises a tumor big data processing module, a tumor identification model construction module, a tumor multi-source feature fusion module and a tumor classification identification mapping module, and can acquire corresponding tumor multi-modal medical image data and patient pathological clinical data through butt joint and perform image registration and filtering cleaning processing. Tumor multi-mode standard image data and patient clinical standard data are obtained; constructing a corresponding tumor recognition model; performing feature analysis and big data fusion splicing on the tumor multi-modal standard image data and the clinical standard data of the patient to obtain a multi-source fusion feature vector of the tumor of the patient; and inputting the multi-source fusion feature vector of the tumor of the patient into a constructed tumor identification model to carry out tumor classification and identification, thereby outputting a medical image tumor identification result. According to the invention, accurate recognition of tumors can be realized.

The invention relates to the technical field of image annotation, in particular to a medical image auxiliary annotation method and system based on image processing, and the method comprises the following steps: obtaining medical image data, and carrying out the edge enhancement processing of the image data; performing preliminary labeling on the central necrosis layer, the edge active layer and the peripheral normal tissue layer to generate preliminary labeling areas of different focus levels; extracting textural features, morphological features and density features of each level, and converting the textural features, morphological features and density features into feature vectors for storage; forming a multi-level lesion association model; adjusting the boundary positions of the layers, and ensuring that the relative position relation between the layers is reasonable; dynamically updating the level annotation according to the change characteristics; and outputting a labeling layer containing the multi-level focus characteristics of the central necrosis area and the edge active area. Compared with an existing single-level or coarse-grained marking method, the method has the advantages that the hierarchical expression effect of the focus area is improved, and the detail level of the focus in the complex form is clearly presented.

The invention relates to the technical field of multi-modal medical image registration optimization methods, in particular to a medical image registration optimization method and system, and the method comprises the following steps: inputting a medical image data set comprising an MRI image data set, a CT image data set and a PET image data set into a trained deformable registration network; obtaining a deformation field by using the deformable registration network; obtaining a final registration result of the two groups of medical image data based on the deformation field, obtaining an optimized final registration result through an iterative optimization algorithm, and obtaining a quality evaluation coefficient at the same time; constructing an optimized objective function by using the quality evaluation coefficient, and realizing the adjustment of the hyper-parameter of the deformable registration network through the back propagation of the optimized objective function; prediction registration is carried out by using the deformable registration network after hyper-parameter adjustment, so that an optimized registration result is obtained, the position and the shape of a convolution kernel are adaptively adjusted based on a feature extraction network of deformable convolution, and nonlinear deformation of a local anatomical structure is better captured.

The invention discloses a multi-granularity knowledge graph auxiliary diagnosis method based on DeepSeek and Agent. The multi-granularity knowledge graph auxiliary diagnosis method comprises the following steps: step 1, receiving electronic medical record text data and medical image data of a patient; 2, constructing a knowledge graph; updating the knowledge graph every day to reflect the latest medical research result; step 3, analyzing the text data of the electronic medical record through DeepSeek-R1; 4, extracting spatial structure feature nodes of the medical image data through a multilayer three-dimensional convolution kernel; the method comprises the following steps: segmenting medical image data into sequence blocks through a Vision Transform; 5, the output of the DeepSeek-R1, the output of the multi-layer three-dimensional convolution kernel and the output of the Vision Transform are input into a multi-modal fusion module; step 6, outputting a high-confidence diagnosis conclusion and probability distribution; and step 7, generating an intelligent report of the structured text. According to the method, by combining natural language processing, computer vision and the knowledge graph technology, accurate and efficient medical examination is achieved.

The invention discloses an intelligent analysis system and method for orthopedic nursing, and relates to the technical field of medical information, and the method comprises the steps: collecting and preprocessing medical image data, combining the daily motion state data of a patient, constructing a multi-dimensional data set containing skeleton morphological features and motion features, extracting data features, and carrying out the analysis of the data. Adaptive fusion is carried out by adopting an attention mechanism, and a digital feature model containing static morphological features and dynamic motion features is established; based on the digital feature model, joint stress features and coupling features are obtained, and a comprehensive evaluation index system is constructed to achieve quantitative evaluation of the bone health condition; and according to the evaluation index system, constructing multi-level risk early warning to carry out real-time monitoring and early warning on abnormal conditions, and meanwhile, based on the rehabilitation progress evaluation indexes, adaptively generating personalized rehabilitation schemes and exercise suggestions. According to the rehabilitation parameter adjusting method combined with double-objective optimization, the safety of the rehabilitation process is guaranteed, and the pertinence of the rehabilitation effect is improved.

The invention relates to the technical field of medical image detection, and discloses a medical image quality detection method based on image processing. The method comprises the following steps: acquiring medical image data to be detected, wherein the medical image data comprises a multi-modal scanning image sequence and corresponding acquisition parameters; the medical image data are preprocessed, standardized image data are generated, and the standardized image data comprise unified parameters of spatial resolution, gray scale range and noise level; extracting structural features of the standardized image data, wherein the structural features comprise tissue boundary gradient distribution, texture consistency and local contrast information; constructing a quality evaluation model according to the structural features, wherein the quality evaluation model analyzes a mapping relationship between the structural features and preset quality indexes through a dynamic convolutional network; and outputting a quality defect detection result based on the quality evaluation model, wherein the quality defect detection result marks an image region with artifacts, fuzziness or distortion.

The invention provides a U-Net model optimization method based on dynamic interpretable pruning, and the method comprises the steps: employing a hierarchical differential pruning strategy, employing structured channel pruning at an encoder, and employing unstructured intra-kernel pruning at a decoder; introducing a multi-dimensional evaluation model, and constructing a ternary evaluation model comprising a weight absolute value, gradient saliency and a feature map entropy value; dynamically associating the hardware characteristics with the pruning granularity, and presetting the corresponding pruning granularity according to the computing power characteristics of the target deployment platform; an existing medical image data set is adopted as an input feature map, and the improved U-Net model is trained through three stages of processes of pre-training, dynamic pruning training and precision recovery in sequence. According to the method, the parameter quantity is effectively reduced, and the reasoning speed of edge equipment is increased, so that the clinical real-time requirement is met; meanwhile, high precision is kept, and the method has practical application value in medical image segmentation tasks.

The invention discloses a heart failure treatment aid decision generation system based on multi-modal data fusion, and the system comprises a data collection module which is used for collecting the multi-modal data of a patient, and the multi-modal data comprises structured data, unstructured data and medical image data; the data processing module is used for carrying out standardization, quantization and vectorization processing on the multi-modal data; the knowledge graph construction module is used for constructing a knowledge graph of heart failure treatment, and the knowledge graph comprises a disease entity, a pathological feature, a treatment scheme and an association relationship thereof; the reasoning module is used for generating a personalized treatment decision based on the knowledge graph and the patient data; and the treatment scheme generation module is used for dynamically adjusting and outputting a personalized treatment scheme in combination with the real-time state data of the patient. The problems that multi-modal data are difficult to fuse and real-time disease change is difficult to dynamically adjust in heart failure diagnosis and treatment are solved, accurate diagnosis and personalized treatment are realized through knowledge graph reasoning and a dynamic correction mechanism, and the diagnosis and treatment efficiency and accuracy are remarkably improved.

The invention relates to the technical field of image enhancement, in particular to a medical image focus recognition method and system based on a neural network, and the method comprises the following steps: setting neighborhood windows of different sizes based on input medical image data, calculating the Shannon entropy value of each neighborhood window, calculating the local energy value, and forming a local energy diagram; and fusing the Shannon entropy value and the local energy map to obtain a fused information entropy energy map. According to the method, neighborhood windows of different sizes are set for medical image data, and the Shannon entropy value and the local energy value are calculated for each window, so that the information complexity and the local pixel active degree of the image in spatial distribution can be fully extracted, and the information entropy energy spectrum formed by fusing the information complexity and the local pixel active degree has higher region sensitivity; high-response areas are screened through the atlas, boundary coordinates of the high-response areas are recorded, and the positioning precision can be improved in high-intensity change areas.

The invention provides a first-aid method and system based on 5G communication and an edge device. The method comprises the following steps: determining a communication protocol target adaptation protocol with a 5G base station based on the edge device; obtaining multi-modal data according to the target adaptation protocol based on the edge device, and performing priority classification on the multi-modal data to obtain a priority classification result; evaluating the 5G network resources of the edge device, determining a resource evaluation result, and determining a cloud transmission strategy based on the resource evaluation result and the priority classification result; performing association analysis on the vital sign data, the medical image data and the voice record data to obtain a calculation task and task complexity, processing the calculation task based on the task complexity and a cloud transmission strategy, and determining a data processing result; and data processing results are transmitted to the first-aid smart center and the mobile terminal in a grading manner. According to the method and the device, the compatibility problem caused by non-uniformity of device interfaces is solved, and the edge device processing capability is improved.

The invention relates to a biological soft tissue virtual simulation method and system and a medium, and the method comprises the following steps: constructing a three-dimensional soft tissue model based on medical image data, dispersing the three-dimensional soft tissue model into a particle system, and binding initial constraint parameters; detecting collision between the particles and the surgical instrument based on a symbolic distance field technology, if collision is not detected, outputting the positions of the particles, generating a simulation result, if collision is detected, applying repulsive force to the particles to correct a deformation path, judging whether stress is balanced or the maximum number of iterations is reached, if yes, returning to perform collision detection, and if not, returning to perform collision detection. The particle speed is updated in real time, the particle motion trend is predicted through an implicit time integration algorithm, position constraint, volume constraint, shape constraint and physical constraint are solved in a coupling mode through a Gaussian-Seidel iteration method, constraint rigidity is dynamically adjusted to be matched with real organization characteristics, and the particle position is updated. Compared with the prior art, the method has the advantage that deformation accords with a real physical change rule.

The invention relates to the technical field of medical image processing and analysis, and discloses a method for identifying and classifying lesions in medical images, which comprises the following steps: acquiring a plurality of medical image data, and constructing a multimode medical image data set containing CT, MRI and PET images; training the multi-mode medical image data set by using a hierarchical attention feature fusion network to generate a focus recognition and classification model; a target medical image is partitioned by a dynamic architecture partitioning algorithm based on image complexity, and analysis is performed by using the focus recognition and classification model to obtain focus features; and classifying the lesion features by using an iterative classification algorithm of Bayesian uncertainty estimation, calculating a classification threshold in combination with a medical expert knowledge base, and outputting a final classification result about the lesion. According to the invention, the accuracy and robustness of identification and analysis are improved, and the accuracy and reliability of a focus classification result are ensured.

The invention relates to the technical field of medical image segmentation, in particular to a medical image segmentation method based on improved SwinUNet. According to the technical scheme, the method comprises the following steps that CT / MRI medical image data of an abdomen or heart area is collected and preprocessed, and preprocessing comprises image gray normalization, size resampling to 224 * 224 and enhancement processing; the preprocessed medical image is divided into image patch blocks, and initial feature representation is formed through linear embedding; through the improvement, the method is superior to a traditional convolutional neural network or a basic SwinUNet model in the aspects of segmentation precision, robustness, boundary processing, clinical applicability and the like, the problems of boundary discontinuity, poor small target recognition, insufficient cross-layer semantic alignment and the like existing in an existing method are effectively solved, a more reliable scheme is provided for automatic segmentation of medical images, and the method is suitable for large-scale popularization and application. The obvious clinical application value is realized.

The invention relates to the technical field of medical data integration, in particular to a block chain and AI-based medical whole industry data asset integration system and method. Comprising a data acquisition layer, a block chain right confirmation layer, a federal learning analysis layer, a data asset transaction platform and a supply chain traceability optimization module. The data acquisition layer comprises an edge computing node, a data cleaning engine and a desensitization algorithm are built in the edge computing node, and the edge computing node is used for carrying out localized cleaning and desensitization processing on hospital HIS system data, medicine RFID data, medicine research and development experiment data and medical image data; the block chain right confirmation layer constructs a medical data asset account book based on an alliance chain architecture, the asset account book comprises a data hash record, a contributor identity identifier and a use authorization log, and an integrated intelligent contract module is used for dynamic right management; according to the invention, medical data islands can be broken, and safe integration and efficient utilization of cross-domain data are realized.

The invention discloses a caesarean section training method and system based on mixed reality, and the method comprises the steps: obtaining the medical image data of the pelvis and abdomen of a pregnant woman, recognizing and marking the boundary and region of a key tissue structure, constructing a three-dimensional model of the key tissue structure, and generating key indexes of caesarean section training. Importing the constructed three-dimensional model into an MR system, collecting action data, generating a real-time feedback prompt, receiving a training ending signal, generating an evaluation result, recording operations which do not conform to an evaluation standard, and providing targeted improvement suggestions for trainees. According to the method, the quality and efficiency of operation training can be effectively improved, a safe, efficient and immersive training platform is provided for trainees through precise processing of medical images, efficient reconstruction of three-dimensional models, real-time feedback and personalized operation evaluation, personalized guidance can be carried out according to the performance of the trainees, and the training efficiency of the trainees is improved. And the training effect of surgical skills is further improved.

The invention relates to the technical field of image processing, and discloses a surgical minimally invasive surgery data processing method and system based on an AR technology. The method comprises the following steps: performing three-dimensional reconstruction and lesion segmentation on medical image data, and extracting feature points; performing position acquisition and space mapping on the surgical instrument by using the feature point data; reconstructing a light field according to the feature points and the position data and generating an augmented reality scene; performing trajectory analysis and path planning based on augmented reality; performing real-time monitoring and risk early warning on the operation path; and finally, extracting an operation rule and establishing a knowledge mapping relation. According to the method, association analysis and fusion processing of multi-source heterogeneous data in the operation process are achieved, a complete technical link from data collection to knowledge extraction is established, and the operation safety and the intelligent level are improved.

The invention discloses an AI-driven complete-cycle scoliosis management system, which belongs to the field of scoliosis management systems and comprises a data fusion module, a feature extraction module, a text processing module, a diagnosis inference module and an output presentation module. Wherein the data fusion module is used for performing deep fusion on sensor data of the wearable equipment and X-ray / CT medical image data to generate comprehensive fusion prediction data; the feature extraction module uses a pre-trained fusion encoder to perform feature extraction and encoding on the fusion prediction data to generate potential feature representation; the text processing module adopts a pre-trained text encoder to encode free text diagnosis information provided by a doctor and a patient and extract semantic features; and the diagnosis inference module is used for fusing the fusion features and the text features. According to the invention, the sensing data of the wearable device and the medical image data can be effectively integrated to give full play to the complementary advantages thereof, so that the diagnosis accuracy is improved.

The invention provides a disease monitoring method and system based on medical image data recognition, and relates to the technical field of image processing. The method comprises the following steps: acquiring multi-level prior information related to a target disease from a medical knowledge graph, encoding focus attributes and time sequence attributes in the multi-level prior information into knowledge prior vectors, inputting the knowledge prior vectors into a generator, and enabling the generator to output focus image data based on the knowledge prior vectors; when the lesion image data output by the generator and the knowledge prior vector are subjected to difference evaluation, the discriminator also calls lesion boundaries and staging limitation rules in the medical knowledge graph to perform compliance judgment on the form and staging state of the lesion image data; updating network parameters of the generator based on a difference evaluation and compliance determination result output by the discriminator to obtain target lesion image data; and combining the focus image data with the original medical image, inputting the combined image data into a preset disease monitoring model, and generating a potential focus detection result.

The invention discloses a medical auxiliary diagnosis method and system based on a convolutional neural network, and relates to the field of medical big data analysis, and the method comprises the steps: collecting various types of medical data, which comprise medical image data, electronic medical record text data and vital sign monitoring data; constructing a convolutional neural network model with a multi-branch structure; preparing a training data set containing labeled samples, wherein labeled information comprises disease diagnosis results and disease severity grades; training the convolutional neural network model by using the training data set, dynamically adjusting the learning rate in the training process by adopting a stochastic gradient descent optimization algorithm and combining a learning rate attenuation strategy, and introducing a regularization method; inputting to-be-diagnosed medical data into the trained convolutional neural network model, and outputting disease diagnosis probability distribution and disease severity prediction results through forward propagation calculation of the model; and generating a medical auxiliary diagnosis report according to the output prediction result.

The invention discloses a system and a method for predicting postoperative recurrence risk after triple negative breast cancer neoadjuvant therapy based on multi-modal time sequence medical image data, and belongs to the field of medical image analysis. The system comprises a data processing module used for constructing a multi-modal data set; the multi-modal feature extraction and screening module is used for extracting deep learning, radiomics and tumor habitat features from the region and carrying out feature screening; the model training module is used for constructing a time sequence model based on a Transform architecture and carrying out training through a multi-task learning strategy integrated with time consistency constraint and gene association auxiliary loss; and the recurrence risk prediction module is used for loading the trained model and outputting a recurrence probability and a risk level. According to the method, the multi-modal time sequence image and gene information are fused, so that the recurrence risk of the triple negative breast cancer patient is dynamically and accurately quantified, and support is provided for clinical individualized treatment decision.

The invention provides a method and system for efficiently marking and segmenting a focus area in a medical image, and the method comprises the steps: S1, collecting a medical image data set, and dividing the medical image data set into a training set and a test set; s2, selecting a reference image from the training set, carrying out initial labeling, generating a visual prompt, and obtaining the reference image with the visual prompt; s3, constructing a deep convolutional neural network architecture, and obtaining a trained model through model training; s4, performing prediction segmentation on the unlabeled image in the test set by using the trained model to generate a prediction segmentation result; and S5, checking and correcting the prediction segmentation result, and improving the model performance through interactive optimization. According to the method, a doctor only needs to carry out initial labeling on a small number of focus areas, the problem that an existing fully-supervised segmentation method seriously depends on a large amount of high-quality labeling data is solved, the requirement for the high-quality labeling data is remarkably reduced, and the workload of medical staff is relieved.

The invention discloses a real-time monitoring puncture robot tumor ablation method and system, and the method comprises the steps: obtaining a tumor molecular level image, combining the tumor molecular level image with medical image data, carrying out the multi-level reconstruction, and obtaining a three-dimensional model of an organ and a tumor; extracting feature points of a tumor and a surrounding anatomical structure, and performing precision registration on a tumor position in the three-dimensional model and an ablation needle; adopting a self-adaptive path planning algorithm to formulate an optimal insertion path of the puncture needle; a sensor network is integrated on the ultrasonic probe and the puncture needle, information of a needle tip and surrounding tissue is monitored, thermal field distribution and the real-time position of the needle are updated in combination with the three-dimensional model, and the power and time of the ablation needle are adjusted; after ablation is finished, ultrasonic and CT image combined examination is carried out, and the tumor coverage condition of an ablation area is detected. According to the system, more accurate real-time guidance can be provided in the puncture process, the system cost is remarkably reduced, and the operation convenience is improved.

The invention relates to the technical field of tumor interventional operations, and discloses a visual navigation method based on a tumor interventional operation robot. The method comprises the following steps: acquiring real-time medical image data of a tumor area containing multi-modal imaging information so as to comprehensively present anatomical details; and performing three-dimensional reconstruction on the image data to generate a tumor area three-dimensional anatomical structure model capable of visually displaying a space structure. Key anatomical feature points are extracted based on the model, space coordinates are calculated, a surgical robot intervention path is planned according to the coordinates, and an initial navigation track is generated; and continuously collecting real-time pose data of the robot in an operation, dynamically matching the real-time pose data with the initial navigation trajectory, adjusting motion parameters according to a matching result, and generating a corrected navigation instruction. The method can reflect the intraoperative anatomy condition in real time, dynamically optimize the path, solve the problems that traditional navigation depends on preoperative static images and lacks real-time adjustment, reduce operative complications and improve the treatment effect of patients.

The invention discloses an adenoid body recognition method and device based on image instance segmentation, and the method comprises the steps: selecting a skull side position film image in a medical image database, and carrying out the marking of the skull side position film image to clearly know the position of a key structure; preprocessing each image to generate a training sample; and training an instance segmentation model by using the samples, wherein the model comprises a backbone network, a feature fusion module and a detection head. The backbone network is responsible for preliminary feature extraction, and the C2f module further performs deep processing on feature maps, generates multi-scale feature maps and fuses the multi-scale feature maps into a comprehensive feature map. And the feature fusion module adopts a path aggregation network to integrate all scale feature maps. And the detection head receives the fusion feature map, generates a candidate frame and performs classification prediction. And adjusting the candidate frame according to the prediction result to ensure accurate identification of the adenoid body, and outputting the adjusted candidate frame and the category label as a final identification result. The problem of how to improve the accuracy and efficiency of diagnosis by accurately segmenting and identifying adenoid bodies and related structures thereof is solved.

The invention discloses an image generation method based on a generative adversarial network, and the method comprises the following steps: 1, collecting data; the method comprises the steps that CT, MRI, ultrasound and other medical image data are collected and cover different disease types and stages, then professional doctors mark the data (such as focus positions and disease types), normalization, cutting and enhancement are carried out, and therefore a marked high-quality medical image data set is obtained; 2, model design: designing a generator and a discriminator; 3, defining a loss function to resist loss; step 4, adversarial training; and 5, generating an image. The defects in the prior art are overcome, and high-quality and diversified synthetic medical images can be generated based on the generative adversarial network technology; the image generated by the GAN can accurately simulate the texture features of a real medical image, such as the roughness of a tumor surface, the microstructure of a blood vessel wall and the like, so that a more real visual reference is provided for doctors.