95 results about "Brain mri" patented technology

The embodiment of the invention discloses an image annotation method and system applied to brain MRI image segmentation, and the method comprises the steps: obtaining a brain MRI image data set of a target object, and the brain MRI image data set comprises original image sequences of a plurality of scanning levels; performing multi-modal feature fusion processing on the original image sequence to generate an enhanced image feature set; calling a multi-layer cascade segmentation network to perform hierarchical feature extraction on the enhanced image feature set to obtain a multi-scale anatomical structure feature map; and performing region boundary optimization processing based on the multi-scale anatomical structure feature map, and generating a marked brain structure segmentation image. Therefore, the boundary of each structure of the brain can be accurately defined, the segmented image is more accurate and clearer, and the image segmentation and marking of the brain MRI image can be accurately and clearer realized.

The invention discloses a method for determining brain PET standardized reconstruction parameters based on clinical brain MRI and PET data, and relates to the field of medical image processing. The method comprises the following steps: firstly, collecting clinically paired MRI (Magnetic Resonance Imaging) and PET (Positron Emission Tomography) images for preprocessing, carrying out virtual reconstruction by combining a partial volume correction algorithm and system parameters of equipment to estimate'real 'brain activity distribution so as to obtain a first simulated PET image, and calculating difference or similarity between the first simulated PET image and a clinical PET image; through multiple iterations, obtaining an optimal'real 'brain activity distribution diagram, and processing the optimal'real' brain activity distribution diagram into a'standardized PET image 'according to standardized Gaussian filtering; then, constructing different candidate combinations of reconstruction parameters, inputting an optimal'real 'brain activity distribution diagram, traversing a second simulated PET image generated by simulation reconstruction under each combination, and respectively performing difference or similarity calculation with the'standardized PET image', so as to obtain an optimal'real 'brain activity distribution diagram; and selecting the group of candidate reconstruction parameters with the minimum difference or the highest similarity as final standardized reconstruction parameters. The method gets rid of dependence on a physical motif.

The invention discloses a medical image intelligent diagnosis and three-dimensional visualization interaction method and system based on artificial intelligence and Unreal Engine. The system is composed of a data acquisition module, an artificial intelligence analysis module, a three-dimensional reconstruction module, a rendering interaction module and a report generation module. The method comprises the following steps: firstly, carrying out voxel normalization on a DICOM / NIfTI image; the improved 3D U-Net is combined with CBAM attention and WGAN-GP training, and a focus initial density field is output; an AI enhanced implicit voxel reconstruction (AEIVR) algorithm is proposed, an enhanced density field is generated by using position coding + MLP, and grid extraction is completed by using improved Marking Cubes of a dynamic threshold; high-fidelity real-time rendering is realized in combination with a Nand micro polygon and Lumen global illumination in Unreal Engine, and VR / AR interaction and virtual surgery (real-time updating of resection mask driving) are supported. According to the method, the problems of large artifacts and insufficient interactivity and real-time performance of existing two-dimensional output and three-dimensional reconstruction are solved, and the end-to-end segmentation-reconstruction-rendering-interaction-reporting process is realized. On typical CT / MRI data, the segmentation Dice is about 0.90-0.94, the reconstruction voxel resolution is 0.1-0.5 mm, the artifacts are reduced by about 20%-30%, the PSNR is improved by about 3-7 dB, and the rendering interaction delay lt is achieved; the system is suitable for scenes such as lung CT, brain MRI, orthopaedic X-ray and cardiac ultrasound, and can be expanded to preoperative planning, remote consultation and teaching.

The invention discloses a cerebral apoplexy patient postoperative neural function real-time monitoring method. The method comprises the steps that brain MRI image data and clinical information of a to-be-monitored patient and post-operation electroencephalogram data collected in real time are collected and preprocessed; respectively extracting neurophysiological features and radiomics features of the patient to be monitored based on the preprocessed data; performing significant feature screening on the clinical information, the neurophysiological features and the radiomics features, performing preprocessing on the screened data, and combining minimum absolute contraction and selection operator regression analysis to obtain quantitative electroencephalogram data feature indexes and radiomics scores; inputting the screened clinical information, the quantitative electroencephalogram data characteristic index and the radiomics score of the to-be-monitored patient into a trained prediction model, and predicting a risk index of early neurological deterioration of the to-be-monitored patient; the problem that the evaluation result is inaccurate due to the fact that a single clinical feature is adopted to evaluate the neural function in a traditional method is solved.

The invention discloses a multi-modal medical image processing method and device, a storage medium and computer equipment. Comprising the following steps: performing three-dimensional discrete wavelet transform on a brain MRI image of a target patient to generate an MRI wavelet coefficient; inputting the MRI wavelet coefficient into a diffusion model to obtain a reference PET wavelet coefficient of the brain of the target patient in a healthy state; performing inverse wavelet transform on the reference PET wavelet coefficient to generate a reference PET image; and comparing the brain PET image of the target patient with the reference PET image, and determining the metabolic deviation index of the brain of the target patient. Therefore, each patient can take the condition without the neurodegenerative change as a contrast, space standardization does not need to be carried out on a group template, anatomical structure distortion caused by the space standardization is greatly reduced, voxel-level accurate analysis of the neurodegenerative disease is realized, tiny pathological change aiming at the patient can be identified, and the accuracy of voxel-level accurate analysis of the neurodegenerative disease is improved. And clinical doctors are assisted in early diagnosis.

The invention discloses a brain MRI super-resolution reconstruction method based on a learnable weight linear accumulation strategy, and relates to the technical field of magnetic resonance imaging image super-resolution reconstruction. According to the method, the Swin-WKV model is built, the model is based on a Swin Transform and RWKV architecture Transform-like model, high-quality reconstruction of the brain MRI image can be achieved with quite low memory consumption, a mixed loss function is provided, image domain difference and frequency domain difference are combined, and the model is guided to restore frequency spectrum and pixel-level features of a reference image as much as possible.

The invention discloses an MRI (Magnetic Resonance Imaging) accelerated reconstruction method and system based on a K-space enhanced reversible diffusion model, and the method comprises the steps: firstly collecting a brain MRI scanning image, carrying out the undersampling processing, normalizing a gray value, and randomly cutting an image block with a specified size; inputting the image blocks into an end-to-end adaptive K-space enhanced reversible diffusion model to generate a reconstructed image; a progressive mask strategy and cross-domain feature injection are adopted, image edges and detail textures are recovered in a targeted mode, adaptive optimization of different anatomical structures is achieved through a learnable mechanism, and reconstruction quality and robustness are remarkably improved. According to the method, the performance is outstanding on the core indexes such as the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM), the precise recovery capability of the image edge structure is verified through Canny and Sobel operators, key breakthrough is achieved on the reconstruction quality, the training efficiency and the clinical applicability, and a solution with theoretical innovation and engineering application value is provided for the MRI accelerated imaging technology.

The invention relates to the technical field of psychiatric disease diagnosis, and discloses a defective schizophrenia prediction and analysis system based on multi-modal brain network characteristics, which comprises an image data acquisition module, an image data processing module, a deep learning model module and a prediction module which are connected in sequence, the image data acquisition module acquires a brain MRI image; the image data processing module carries out cortical and subcortical reconstruction on the brain MRI image to obtain a model input index; the deep learning model module performs training by using a training data set composed of a plurality of brain MRI images and defective schizophrenia diagnosis results thereof to obtain a target prediction model; and the prediction module inputs the real-time brain MRI image into the target prediction model to obtain a prediction result. According to the method, the multi-dimensional brain structure image features are integrated, diagnosis information contained in brain region changes is fully mined, prediction results of defective schizophrenia and non-defective schizophrenia are improved, and a more comprehensive biological basis is provided for clinical diagnosis.

The invention relates to the technical field of medical image recognition, in particular to an Alzheimer's disease MRI diagnosis method based on width neural network learning, and the method comprises the steps: 1, carrying out the adaptive ROI cutting of a brain MRI image; step 2, performing dynamic contrast compensation binaryzation; step 3, morphological optimization; step 4, gray scale returning; 5, density peak value guided K-means + + feature extraction is carried out; and step 6, obtaining a width neural network, inputting the final enhanced feature map into the width neural network for final classification prediction, and realizing diagnosis of the Alzheimer's disease MRI. According to the method, a binary segmentation method of dynamic contrast compensation and multi-scale feature enhancement of K-means + + driving are combined, the method is devoted to the hippocampus segmentation problem in image recognition for the Alzheimer's disease and the problem that subtle variation of an early-stage AD patient cannot be captured, and convenience is provided for doctors.

The invention discloses a whole brain positioning and health state evaluation method and system based on an adaptive attention mechanism, electronic equipment and a computer readable storage medium. The whole-brain positioning and health state evaluation method based on the adaptive attention mechanism comprises the following steps: collecting whole-brain MRI data, and marking the structure of each part of the region of the whole brain; performing data preprocessing on the whole brain MRI data; on the basis of the whole-brain MRI data after data preprocessing, detecting each partial region of the whole brain by using a whole-brain positioning network based on an adaptive weight and residual error reconstruction mechanism; and according to the regional form of each part of the whole brain in the detection result, performing comprehensive health form evaluation. The method can solve the problems that the edge detection precision of each region of the whole brain is low and each partial region of the whole brain is difficult to accurately distinguish at present; the generalization ability is improved; the health state is accurately evaluated; manual intervention is reduced, one-key rapid whole-brain positioning and accurate health state evaluation are achieved, and simplicity and high efficiency are achieved.

The invention discloses a three-dimensional surgical risk model reconstruction method based on brain anatomy and a functional atlas, and the method comprises the steps: S1, carrying out the image processing and fine segmentation of a brain MRI image or a DICOM sequence image of a patient, and recognizing a plurality of anatomical structures; s2, establishing a multi-dimensional risk assessment model, and performing quantitative scoring on each brain region from four dimensions of functional importance, structural vulnerability, recovery potential and clinical significance; s3, constructing a continuous three-dimensional risk field based on the risk score, and realizing the propagation of the risk value in the brain tissue based on a Gaussian diffusion model; and S4, based on an improved path planning algorithm, searching an optimal operation path in the three-dimensional risk field to realize risk minimization. The invention further discloses a corresponding system, electronic equipment and a computer readable storage medium.

The invention relates to the technical field of image processing, in particular to a brain age prediction method, system and device based on federal learning. According to the method, a three-dimensional brain MRI image is taken as input data, and multi-center collaborative modeling is realized by adopting a federated learning framework aiming at the problems of medical data privacy protection and data islands; each medical institution does not need to share original image data, and only completes feature extraction, model training and local parameter updating locally based on private data; after the central server receives the local parameters uploaded by the mechanisms, data distribution differences are considered, and a unified global model is generated through fusion of a preset aggregation rule. According to the method, privacy leakage and compliance risks of cross-mechanism transmission of original image data are effectively avoided, common characteristics of multi-center data can be integrated, the brain age prediction precision and generalization ability of a global model are improved, and the method is suitable for brain age evaluation research and clinical auxiliary diagnosis scenes jointly developed by multiple mechanisms.

The application provides a child neuropsychological development monitoring and management system. The system uses brain physiological parameters, brain MRI images, development scale characteristics and corresponding child neuropsychological development suffering grades to construct a child neuropsychological development evaluation model. The constructed child neuropsychological development evaluation model generates a current child neuropsychological development suffering grade of a user according to real-time brain physiological parameters collected by a system terminal, real-time brain MRI images monitored by a hospital and corresponding real-time development scale characteristics, provides a basis for whether the corresponding child needs further monitoring according to the current child neuropsychological development suffering grade, and improves the probability of finding potential abnormal development children. The application uses three kinds of fusion parameters related to child brain nerves, such as brain physiological parameters, brain image characteristics and subjective measurement table characteristics, to construct a high-precision real-time processing model, realizes intelligent monitoring of the morbidity of child neuropsychological development disorders by doctors, and reduces the burden on families.

The application provides a medical image classification and feature visualization method based on a deep learning model and a feature visualization tool, and comprises the following steps: A, removing non-key information frames from a brain MRI image; B, performing slice enhancement on the data to obtain a slice data set; C, transversely splicing the slices in sequence to obtain a spliced picture data set; D, putting the spliced picture data set into SE-ResNet50 for training and testing to obtain a classification result; E, putting the trained spliced picture network and a sample spliced picture into Grad-CAM to generate a heat map; F, selecting key frames from the heat map, putting the key frame slice set into SE-ResNet50 to extract features and classify; G, generating a key frame heat map through Grad-CAM; and H, using the obtained classification result and heat map to assist in diagnosis. The application discloses a method for classifying and visualizing features of brain images based on a deep learning method. An optimized SE-ResNet50 network is used to extract features and classify patient brain images. Abnormal regions are marked and visualized through Grad-CAM, so that the efficiency and diagnosis rate are improved.

The application provides a transfer learning driven intracranial tumor image data full-automatic segmentation method, relates to the technical field of medical image segmentation, and comprises the following steps: pre-processing a brain MRI image of a target user, obtaining a standard brain MRI image, and analyzing to obtain brain image features; generating an image feature vector, setting an adaptive transfer learning scheme, calling a sample intracranial tumor image segmentation model library, and integrally constructing a first intracranial tumor image segmenter; training a convolutional neural network by using a sample ependymoma image dataset to construct a second intracranial tumor image segmenter, and setting an adaptive fusion weight; after the standard brain MRI image is segmented by using the first intracranial tumor image segmenter and the second intracranial tumor image segmenter, the intracranial tumor image segmentation result is fitted according to the adaptive fusion weight. The technical problem of weak generalization ability and easy segmentation deviation of an intracranial tumor image segmentation model in the prior art is solved.

The present application relates to the field of computer image processing technology, and in particular, to a brain puncture path planning method and apparatus. An MRI-compatible robot, by acquiring pre-operative and intra-operative brain MRI images and registering the pre-operative and intra-operative images, can determine a tissue drift in the target tissue of brain puncture and registration errors caused by registering different brain MRI images. Simultaneously, by combining the three-dimensional reconstruction error that would be generated by three-dimensional reconstruction of the brain MRI images and the positioning error of the puncture needle, the MRI-compatible robot performs robustness evaluation on a plurality of brain puncture paths obtained from pre-operative planning. The brain puncture path with higher robustness is selected as the target brain puncture path. At this time, the target brain puncture path has higher safety and lower risk, is more reliable even in the presence of the aforementioned errors, and can reduce the possibility of damage to the patient's intracranial nerves, blood vessels, and functional areas due to errors.

The application discloses an auxiliary identification method for brain inflammatory pseudotumor MRI images based on machine learning, and belongs to the technical field of medical image processing, and comprises the following steps: acquiring preoperative magnetic resonance imaging (MRI) data of a brain lesion of a to-be-measured individual, and performing standardization preprocessing on the MRI data to obtain a standardized brain MRI image; a lesion target region containing a tumor core region and a peritumoral edema region is segmented on the standardized brain MRI image, and a training set, a verification set and a test set are generated based on the lesion target region; a lesion type auxiliary identification model is constructed based on feature screening and feature topology graph attention network of SVM weights, and the training set and the verification set are used for training; the test set is input into the trained lesion type auxiliary identification model to obtain a lesion type identification result, and the problems of time-consuming and labor-consuming, low efficiency, omission of hidden dangers, low accuracy and the like in existing artificial visual measurement and traditional image processing measurement methods are solved.

The application discloses a nasopharyngeal carcinoma radioactive brain damage image auxiliary judgment method, equipment and medium, the method is: collecting MRI image and preprocessing, segmenting the temporal lobe region, using the temporal lobe region and corresponding lesion annotation training level prediction model of MRI image, the probability that a single temporal lobe image region exists radioactive brain damage lesion is predicted;With the probability that each subject all layers contain lesions output by the level prediction model as input, whether the subject exists radioactive brain damage as output, training patient level prediction model;Finally, the trained level prediction model and patient level prediction model are used to obtain the incidence probability of radioactive brain damage of a single nasopharyngeal carcinoma subject, when multiple modes coexist, the average of multiple mode prediction result outputs is taken as the final prediction result. The application can judge whether the corresponding nasopharyngeal carcinoma brain MRI image exists radioactive brain damage according to the input, provide reference for clinical doctors to diagnose, and reduce the missed diagnosis of nasopharyngeal carcinoma radioactive brain damage.

The application relates to the technical field of image processing, in particular to a cerebral small vessel lesion recognition and detection system based on MRI image analysis, which comprises a processor and a memory, and the processor executes a computer program stored in the memory to realize the following steps: obtaining a normal possibility representation value of a pixel point; obtaining a suspected slice image and a suspected lesion area on the suspected slice image according to the normal possibility representation value; obtaining a same-layer area corresponding to the suspected lesion area on the suspected slice image according to all slice images with the same layer as the suspected slice image in other brain MRI images except the brain MRI image where the suspected slice image is located, and obtaining a real lesion area according to the same-layer area corresponding to the suspected lesion area. The application can improve the accuracy and integrity of the recognition and detection of the real lesion area or the real cerebral small vessel lesion area.

The application provides a method for imaging-based diagnosis of neuropsychiatric lupus based on machine learning. The method comprises: obtaining a brain MRI image of a patient; segmenting the intracerebral region of interest of the preprocessed brain MRI image; extracting radiomics features in the region of interest; screening features related to the diagnosis and prediction of neuropsychiatric lupus; dividing the screened features into a first training set and a first test set; building N machine learning-based radiomics prediction models; saving the trained N machine learning-based radiomics prediction models; using multiple linear regression to save the radiomics prediction model corresponding to the optimal AUC brain region; obtaining the serological indicators of the NPSLE patient; dividing the serological indicators into a second training set and a second test set; building a joint prediction model combining radiomics features and serological indicators; and inputting the second test set into the joint prediction model to obtain the prediction classification result of the patient's neuropsychiatric lupus. The application fills the gap of magnetic resonance imaging in the diagnosis of NPSLE, and provides practical guidance for clinicians in assisting the diagnosis of NPSLE.

A medical image focus detection method based on edge guidance comprises the following steps that S1, brain MRI image data are obtained, and the brain MRI image data comprise a Br35H data set and an MBTBB data set; s2, carrying out image preprocessing and data enhancement, including image normalization, size adjustment and Mosaic enhancement, random erasure and CLAHE enhancement; s3, designing an edge integration multi-scale selection module and a hierarchical re-parameterization interaction fusion module; s4, training the model, and loading the preprocessed image into the model to start training; and S5, verifying the performance of the model, and outputting the category and position information of the focus. According to the method, an edge enhancement mechanism and multi-scale feature adaptive fusion are introduced into the model, so that the perception ability of the model to the focus boundary is improved, and the detection precision is improved.

The application relates to the technical field of image processing, in particular to an MRI image feature extraction method for dynamic evaluation of child brain development, which comprises the following steps: determining a cerebrospinal fluid coefficient of a child brain; dividing an MRI image into a cerebrospinal fluid region and a residual brain region based on the cerebrospinal fluid coefficient of the child brain of each pixel point; determining a white matter coefficient of each pixel point in the residual brain region according to the gray value of each pixel point in the decayed weighted image of each pixel point in different gradient directions, and the gray value of each pixel point in the first weighted image and the second weighted image in the residual brain region; segmenting the white matter region and the gray matter region of the residual brain region based on the white matter coefficient, and extracting image features in the white matter region and the gray matter region. In this way, the accuracy and reliability of the segmentation result of different tissue regions in the child brain MRI image are improved.

The invention relates to a multi-modal MRI brain tumor segmentation method based on convolution attention, and the method comprises the steps: carrying out the parallel coding of multi-modal medical image data, and extracting the specific low-layer space features of each modal; performing feature fusion and high-level coding on the low-level spatial features to obtain shared features; carrying out convolution attention processing on the shared features to obtain enhanced features; and carrying out decoding processing on the enhanced features to obtain a segmentation result. According to the method, more accurate tumor boundary segmentation and tissue differentiation can be realized in medical image analysis such as brain MRI (Magnetic Resonance Imaging), meanwhile, the parameter efficiency, the calculation speed and the generalization ability under limited data of the model are remarkably improved, and the clinical practicability is enhanced.

The invention provides a transfer learning-driven intracranial tumor image data full-automatic segmentation method, and relates to the technical field of medical image segmentation, and the method comprises the steps: carrying out the preprocessing of a brain MRI image of a target user, obtaining a standard brain MRI image, and carrying out the analysis to obtain brain image features; generating an image feature vector, setting an adaptive transfer learning scheme, calling a sample intracranial tumor image segmentation model library, and integrating and constructing a first intracranial tumor image segmentation device; training a convolutional neural network by adopting a sample room tumefacial tumor image data set to construct a second intracranial tumor image divider, and evaluating and setting an adaptive fusion weight; and after segmenting the standard brain MRI image by using the first intracranial tumor image segmentation device and the second intracranial tumor image segmentation device, fitting according to the adaptive fusion weight to obtain an intracranial tumor image segmentation result. The technical problems that in the prior art, an intracranial tumor image segmentation model is poor in generalization ability, and segmentation deviation is prone to occurring are solved.

This invention discloses a method and system for identifying brain tumors in brain MRI images. The method includes the following steps: S1, acquiring the original brain MRI image data of the object to be identified and performing preprocessing operations to obtain standardized multimodal MRI image data; S2, performing preliminary tumor region screening on the preprocessed image data using a tumor tissue probability decoupling identification method based on multi-echo relaxation differences. This invention relates to the field of medical image processing technology. This method and system for identifying brain tumors in brain MRI images employs a tumor tissue probability decoupling model based on multi-echo relaxation differences and cross-scale brain network topology perturbation analysis, which can accurately screen potential tumor regions. Furthermore, it improves identification accuracy through dual constraints of metabolism and morphology. The introduction of cross-scale topology analysis, combined with abnormal propagation detection of brain network structures, effectively identifies suspected tumor regions and improves identification accuracy.

The invention provides an MRI (Magnetic Resonance Imaging) image brain partitioning method and device. The method comprises the following steps: constructing a training model, inputting training data into the training model for histogram statistical analysis, and estimating initial mixed model parameters by utilizing histogram analysis and a clustering algorithm; inputting brain MRI data into the training model, and performing coarse segmentation according to expectation maximization parameter estimation and histogram fitting; constructing an MRF model according to the initial hybrid model parameters and an MRF potential energy function, iterating a coarse segmentation result, and updating the initial hybrid model parameters; stopping iteration until the parameters of the initial hybrid model reach a convergence condition, otherwise, resetting the parameters of the initial hybrid model, and carrying out iteration again; in the iteration process, the model parameters can be adaptively adjusted according to the current segmentation result; the adaptive strategy enables the algorithm to better deal with noise and other complex conditions in the image, and at the same time maintains a high identification rate for different brain tissue types.

The present invention discloses a diffusion magnetic resonance (MR) method for measuring arterial pulsation dependence of perivascular cerebrospinal fluid flow in glymphatic system: DTI acquisition: Brain MRI images were acquired using dynamic diffusion tensor imaging (DTI); Cardiac signal synchronization: Simultaneously collect heart rate fluctuation time-series signals; Peak coordinate determination: Identify the peak timing of cardiac pulsation signals from the time-series data; Image realignment: Reorganize brain MR images according to their temporal positions within the cardiac cycle; Temporal interpolation: Perform uniform time-sampling reconstruction to generate equidistant diffusion MRI datasets across the cardiac cycle; Parameter calculation: Compute axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) at each voxel level; Mask-based analysis: Generate characteristic curves of AD / RD / MD and spin density(S) dynamics using region-specific masks in individual space. This method enables non-invasive measurement of: (1) Cerebrospinal fluid (CSF) flow velocity / direction in large perivascular spaces during heartbeats; (2) Microvascular perivascular CSF dynamics through diffusion parameter analysis.

The application discloses an AD characteristic information analysis method based on brain medical images, which comprises the following steps: data preprocessing is performed on brain MRI images to be analyzed to obtain standard images of brain regions; spatial characteristic analysis of the whole brain and key brain regions; whole brain image characteristic analysis; cognitive characteristic analysis of the whole brain and key brain regions; and aggregation analysis results are obtained to form corresponding explanation data. The application further discloses an AD characteristic information analysis system based on brain medical images. The method for analyzing cognitive index characteristics such as brain age, logical memory score, visual memory score and long-time delay memory score from brain MRI images by using deep learning technology can provide analysis results of brain MRI images and other important medical indexes for doctors, and can provide effective arguments and explanations for subsequent scientific research analysis and interpretation.

The invention discloses an interpretable Alzheimer's disease auxiliary judgment method guided by grey matter attention, and relates to the technical field of image processing and mode recognition. The grey matter attention-guided interpretable Alzheimer's disease auxiliary judgment method mainly comprises the following steps: carrying out preprocessing and grey matter segmentation on brain MRI image data to obtain preprocessed brain MRI image data and a grey matter structure information graph; constructing an Alzheimer's disease diagnosis framework based on a grey matter attention guidance anti-fact, and performing iterative optimization by using a diagnosis-interpretation interaction enhancement mechanism to obtain an optimized Alzheimer's disease diagnosis framework; and inputting an image to be diagnosed into the optimized Alzheimer's disease diagnosis framework to obtain an Alzheimer's disease diagnosis result and an interpretable anti-fact graph. The interpretable Alzheimer's disease auxiliary judgment method guided by grey matter attention provided by the invention can efficiently assist doctors in AD judgment and interpretation.