351 results about "Tumor region" patented technology

The invention relates to the technical field of medical image processing, in particular to a multimodal-based digestive tract tumor lesion image segmentation method and segmentation system. The method comprises the following steps: acquiring an alimentary canal tumor lesion image, and extracting an alimentary canal tumor ultrasonic image; evaluating the tumor invasion depth based on the digestive tract tumor ultrasonic image; performing focus three-dimensional visual modeling according to the tumor invasion depth to obtain a digestive tract tumor focus model; extracting a tumor tissue pathological image according to the digestive tract tumor lesion image; performing tumor region segmentation based on the tumor tissue pathological image to obtain digestive tract tumor region data; performing tissue arrangement anomaly detection based on the digestive tract tumor region data to obtain tissue arrangement anomaly data; and calculating a tissue arrangement disorder index according to the tissue arrangement abnormal data and the digestive tract tumor area data. According to the invention, the tumor identification accuracy and the malignant region segmentation precision are improved based on the medical image processing technology.

The invention relates to the field of brain tumor analysis, and discloses a brain tumor image analysis system based on artificial intelligence, comprising: a spatial alignment unit for acquiring original image data of the brain of a subject; performing spatial alignment on the original image data according to a cross-modal registration algorithm to obtain standardized image data; the feature extraction unit is used for performing tumor region initial segmentation on the standardized image data according to a three-dimensional convolutional neural network so as to obtain a coarse segmentation probability graph; and extracting three-dimensional geometric feature parameters of the tumor candidate region according to the coarse segmentation probability graph. According to the method, the original image data is spatially aligned through the cross-modal registration algorithm, and the spatial consistency between different image sources is ensured, so that the image data under different modals can be accurately compared and analyzed, and an accurate spatial reference is provided for subsequent tumor region identification and processing.

The invention discloses a cascade multi-scale convolution and modal enhancement brain tumor segmentation method based on a Mama framework, and relates to the technical field of brain tumors. According to the method, an MCME-UNet model integrated with an MCMS module is provided, through a hierarchical feature extraction mechanism and a multi-modal feature fusion strategy optimized by an MEM module, the segmentation precision is remarkably improved while the calculation efficiency is kept, and particularly, an EEM module innovatively applies Sobel operator to be connected with residual errors, so that the tumor boundary continuity and the segmentation accuracy are synchronously improved; moreover, a novel training normal form of a focus Tversky loss function is introduced, so that the problem of class imbalance is effectively solved, the sensitivity of the model to a small tumor region is enhanced, a smoother segmentation boundary can be generated, and the stability and accuracy of a segmentation result are greatly 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 a tumor prognosis prediction method and a tumor prognosis prediction system, which are used for constructing a multi-modal fusion model based on image-pathology to improve the prognosis prediction efficiency of tumors, especially pancreatic cancer, and providing reference information for clinical decision-making. According to the technical scheme, the method comprises the following steps: S1, preprocessing an original tumor enhanced CT image, segmenting a tumor region, extracting radiomics features and depth image features of the tumor region, and establishing a CT image feature set; s2, after feature preprocessing is carried out on the tumor clinical data, clinical features with statistical significance are screened out, and a clinical feature set is established; s3, carrying out Hamp; e, preprocessing the pathological image, segmenting a tissue region, extracting spatial relation features, and generating a pathological spatial feature set; and S4, based on a feature interaction method, carrying out multi-modal fusion on the CT image features, the clinical features and the pathological spatial features, inputting a full-connection neural network, constructing a tumor survival risk prediction model, and outputting a tumor survival risk probability through the tumor survival risk prediction model.

The invention discloses a tumor image segmentation method and system of a multi-scale feature fusion network based on boundary enhancement, and relates to the technical field of image segmentation, and the method comprises the steps: obtaining a to-be-segmented tumor image; constructing a tumor image segmentation model based on a pyramid visual converter PVTv2 backbone network; training the tumor image segmentation model through the to-be-segmented tumor image and the known tumor image to obtain an optimal tumor image segmentation model; and acquiring a real-time to-be-segmented tumor image and inputting the real-time to-be-segmented tumor image into the optimal tumor image segmentation model to obtain a tumor image segmentation result. Aiming at the endoscope image segmentation of the kidney tumor, the kidney tumor in the endoscope image is efficiently, stably and automatically segmented through the boundary-enhanced multi-scale feature fusion network, clinical doctors are helped to provide accurate tumor area positioning in endoscopy and surgical operations, and compared with the most advanced method, the method has the advantages that the accuracy is high, and the efficiency is high. According to the method, better segmentation capability and stronger generalization capability are obtained.

The invention discloses a brain multi-modal multi-sequence data registration method and device based on deep learning. The method comprises the steps of performing first iteration processing on a target image and a first moving image to obtain a first deformation field, wherein the first iteration processing comprises image alignment constraint processing of the target image, smooth constraint processing of the first deformation field, and area alignment constraint processing of a tumor area in the image; registering the first moving image to the target image based on the first deformation field to obtain a second moving image; performing second iteration processing on the target image and the second moving image to obtain a second deformation field; and registering the second moving image to the target image based on the second deformation field to obtain a registered moving image. In the process of performing unsupervised training on the deep learning model, after multiple times of iterative processing, model parameters are updated based on image alignment constraint loss with a target image, deformation field smooth constraint loss and region alignment constraint loss of a tumor region in the image.

The invention discloses an HER2 state recognition method and system based on an IHC and HE bimodal image, and relates to the medical image processing and analysis technology, and the method comprises the steps: carrying out the preprocessing of a full-slice image WSI containing IHC and HE, and cutting the WSI into designated image blocks; constructing a multi-task learning framework to classify image blocks obtained by cutting the IHC image, and determining HER2 expression intensity in the tumor region; calculating the area ratio of the image block corresponding to each grade in the tumor region to calculate an IHC HER2 score; taking each image block of the cut HE image as the input of a Prov-GigaPath model, and aggregating the multi-dimensional features of each image block by using an ABMIL model, and taking the aggregated multi-dimensional features as an HE HER2 score; and converting the IHC HER2 score and the HE HER2 score into one-hot codes, and outputting an HER2 judgment result by using a logistic regression model. The HER2 state interpretation result can be quickly output, a pathologist is assisted in diagnosis, and the diagnosis efficiency and accuracy are improved.

The invention discloses a tumor and target region synchronous segmentation method and system based on visual language guidance. The method comprises the following steps: constructing a multi-modal data set fusing a medical image and a clinical text; a unified encoder is adopted to extract shared visual features, a double-branch decoder is adopted to predict a tumor region (GTV) and a clinical target region (CTV) respectively, and information sharing and task differentiation modeling are achieved; introducing a medical pre-training large language model, and respectively extracting language features related to GTV and CTV; a visual language collaborative attention module is designed, dynamic fusion and alignment of visual languages are carried out, task related semantics are guided and enhanced, and redundant information is inhibited; and outputting a precise synchronous segmentation result of the GTV and the CTV after fusing the multi-modal features. Compared with an existing scheme, the problems that the boundary of a tumor target region in a medical image is fuzzy and multi-modal information is insufficient in utilization are effectively solved, the segmentation precision and robustness are remarkably improved, and reliable support is provided for clinical radiotherapy planning.

The invention discloses a multi-task collaborative bone tumor CT image segmentation method, system and device, and relates to the technical field of bone tumor CT image segmentation. According to the multi-task collaborative bone tumor CT image segmentation method, a bone tumor CT image segmentation model is adopted to carry out bone tumor segmentation on a target bone tumor CT image; the bone tumor CT image segmentation model is trained and obtained through the following method: obtaining an original bone tumor CT image; according to the original bone tumor CT image, screening through a CT value range to obtain a bone image and a soft tissue image; marking a tumor area and a skeleton area in the bone image and the soft tissue image to form a training sample; constructing a multi-task learning network including a skeleton segmentation task and a tumor segmentation task; and training the multi-task learning network based on at least one group of training samples to obtain a bone tumor CT image segmentation model. According to the invention, the bone tumor can be identified more accurately, and the precision of bone tumor segmentation is improved.

The invention relates to the technical field of medical image processing, in particular to a tumor image classification method and system based on deep learning, and the method comprises the following steps: obtaining a tumor image gray-scale map, positioning an edge, recognizing a closed structure region, dividing tumor region image blocks, calling a standard image block, analyzing the contour and texture offset degree, and carrying out the layering comparison of labels. And reversely marking image blocks, analyzing edges and consistency of a detail layer, a texture layer and a structure layer, constructing a collaborative matrix to carry out multi-task mapping, and outputting a tumor classification set. According to the method, a closed structure area is positioned and extracted through gray boundary change, the initial recognition precision is enhanced, non-structural interference is reduced, a standard image block is mapped to realize morphological comparison, the classification distinguishing force is improved, label collection prediction errors are compared in a layered manner, error saliency image blocks are dynamically labeled, and a more sensitive feedback mechanism is realized; the multi-layer division enhances the hierarchical expression of the features, enhances the multi-scale aggregation capability, and improves the classification accuracy and the structure recognition generalization capability.

The invention provides a nerve invasion prediction system and method based on a hybrid model, and electronic equipment. The method comprises the following steps: acquiring initial medical information; preprocessing the initial medical information to obtain target medical information; based on the target medical information, acquiring deep learning image features through multi-scale convolution and an attention mechanism of a deep learning model, investigating a tumor region and a peritumor region through a radiomics model, and acquiring radiomics image features; carrying out feature splicing on the radiomics image features and the deep learning image features to obtain classification model input information; and inputting the classification model input information into the classification network to obtain a category prediction result of the initial medical information. According to the feature extraction method combining deep learning and radiomics, the features of the tumor can be captured from different angles, the accuracy of nerve infringement prediction is improved, and the precision requirement of nerve infringement prediction of the prostate part is met.

The invention relates to the technical field of minimally invasive ablation, in particular to a liver tumor microwave ablation postoperative contraction deformation prediction method, which comprises the following steps: performing unified standardization processing on different periods of liver MRI images; accurately marking a preoperative liver tumor area, a postoperative ablation area and a postoperative liver tumor ghost area of the liver MRI image to obtain masks of the corresponding areas; designing a distance perception function as an attention parameter of different areas of the liver MRI image; constructing a multi-sequence distance guide complementary network model; selecting a loss function; selecting an elastic registration method to perform pre-operation and post-operation registration; and calculating an ablation safety boundary through the ablation area mask and the adjusted tumor mask. The liver tumor microwave ablation postoperative curative effect evaluation accuracy can be effectively improved, and a foundation is laid for formulating a subsequent treatment plan of a patient.

The invention belongs to the technical field of medical image processing, and particularly relates to a glioma segmentation method based on a multimodal fusion network guided by anatomical symmetry, which comprises the following steps of: jointly inputting an FLAIR image, a T2 image, a T1 image and a T1c image of the same glioma into a trained image segmentation model, and outputting a predicted segmentation image by the trained image segmentation model, the prediction segmentation image is a glioma MRI image with three segmentation areas obtained through prediction, and the three segmentation areas are an edema area, an enhanced tumor area and a necrosis area respectively; the image segmentation model comprises an encoder, a jump connection part and a decoder; the encoder comprises an ASG module, the jump connection part comprises an IMP module, and the decoder comprises a CMF module. Through a three-module cooperation mechanism, the performance of tumor localization, cross-modal fusion, subregion segmentation and the like is improved, and a reliable image basis is provided for glioma operation plan formulation, prognosis evaluation and personalized treatment decision.

The invention requests to protect a brain tumor segmentation network method in a missing mode based on mode perception, aims to solve the problem of missing modes in multi-mode MRI data, and belongs to the technical field of computer vision and medical image processing. The method comprises the following steps: step 1, providing a dynamic sampling encoder module which dynamically extracts available modal features and explicitly fills missing modal information through cross-modal feature mapping to enhance the robustness of a model; 2, a modal perception attention fusion module is designed, the module combines local and global attention mechanism adaptive fusion features, and the long-tail tumor region segmentation capability is further improved. And step 3, a two-stage training strategy is designed and comprises self-supervised modal reconstruction pre-training and supervised segmentation fine tuning, and the self-supervised modal reconstruction pre-training and the supervised segmentation fine tuning are supervised by a double-task loss function so as to optimize the network to adapt to various missing modal scenes.

The present invention discloses a multi-wavelength tunable laser and sensing system, which relates to the field of lasers and includes: a setting module for presetting or selecting two wavelengths of laser beams in the laser; a camera module for collecting the user's target skin surface image for treatment in real time, identifying the tumor area and the healthy skin area in the image data, and identifying the current skin surface position of the laser beam. The present invention identifies the tumor area and the healthy skin area in the operation data by collecting the user's target skin surface image for treatment, further plans the laser treatment path based on the target skin surface image, and controls the laser in the process of moving according to the treatment path, in combination with the sensor to sense the tumor thickness, so as to achieve further intelligent control of the movement speed of the laser in the process of moving according to the treatment path, so that the tumor position on the user's skin surface can be treated more adaptively.

The invention provides a T1 enhanced image generation method and system based on multi-mode MRI fusion. The method comprises the following steps: processing an original multi-mode MRI image to sequentially obtain a registered multi-mode MRI image, soft mask embedding and preprocessed image data; sequentially obtaining global features, features after coordinate system alignment and projection features by using the preprocessed image data; splicing the soft mask embedding and projection features of the tumor area to obtain a weight coefficient; fusing the aligned features of the coordinate system by using a weight coefficient to obtain a fused feature; utilizing the global features to obtain features after cross-modal attention enhancement; generating a T1CE image and a focus probability graph through the fused features and the features after cross-modal attention enhancement; training the model by using the T1CE image and the lesion probability graph to obtain an optimized image generation model; and obtaining a T1 enhanced image by using the optimized image generation model. According to the invention, a cross-modal attention mechanism is introduced to make full use of information of different modals.

The invention relates to the technical field of medical images, and discloses a segmentation method of a tumor region in a medical image, computer equipment and a storage medium, and the method comprises the steps: obtaining a to-be-segmented medical image, and carrying out the random sampling and local search based on the to-be-segmented medical image, and obtaining a target voxel with a minimum local gray value; secondly, taking the target voxels as seed points of a watershed algorithm, and performing watershed algorithm segmentation on the to-be-segmented medical image to obtain a preliminary segmentation result; thirdly, combining and optimizing the candidate segmentation regions in the preliminary segmentation result by utilizing texture feature data of the candidate segmentation regions to obtain an intermediate segmentation result; and finally, clustering the intermediate segmentation results to obtain a target segmentation result corresponding to the tumor region so as to ensure the accuracy of the segmentation result of the tumor region.

The invention provides a liver tumor early diagnosis method and system based on artificial intelligence, and relates to the technical field of biomedical engineering, and the method comprises the steps: obtaining original information metadata related to the liver, the original information metadata comprises image data original information metadata and biomarker original information metadata, the image data comprises liver CT (computed tomography) and MRI (magnetic resonance imaging) image data, the biomarkers comprise serum tumor marker levels, and the physiological and environmental factors of the patients comprise age, gender, dietary habits and genetic backgrounds of the patients. According to the method, the problems that a tumor in a small and complex area is easily missed or misdiagnosed in the prior art are solved, the path density is predicted through denoising, segmentation and feature extraction of image data preprocessing in combination with a path simulation model based on a graph theory and a Monte Carlo simulation method, and a potential tumor area can be accurately judged.

The invention belongs to the technical field of image processing, and particularly relates to a semi-supervised coarse-to-fine multi-mode brain tumor image segmentation method based on comparative learning. Inputting the image into a preset semi-supervised coarse segmentation network model based on comparative learning for comparative learning segmentation to obtain a whole tumor segmentation mask; cutting background images of the four modal images based on the guidance of a whole tumor segmentation mask to obtain four modal image data only retaining a tumor area; and inputting the image data of the four modes into a preset cross semi-supervised fine segmentation network model, obtaining a fine segmentation result containing a tumor core, an enhanced tumor and an overall tumor through cross pseudo-supervised operation, and drawing a tumor region in the FLAIR image by adopting an initial coarse segmentation network. These coarse segmentation results generate a tumor mask, which is then applied to other modalities. And refining segmentation is carried out in the multi-modal MR image through a semi-supervised learning strategy, and the segmentation precision is gradually improved through a global-to-local refining process.

The invention relates to a planning method, device and equipment for treating brain glioma through multi-fiber laser ablation. The method comprises the following steps: according to pre-acquired image information of a target patient and a segmentation result of a tumor region, sequentially performing segmentation sampling processing on the image information to obtain a candidate optical fiber path set; screening the candidate optical fiber path set according to an optical fiber planning objective function and an optical fiber planning constraint condition to obtain an intermediate optical fiber path set, and determining the number of optical fibers required by tumor ablation according to the intermediate optical fiber path set; determining a target optical fiber path set from the intermediate optical fiber path set according to the number of optical fibers required by tumor ablation and a predetermined path evaluation score; the target optical fiber path set at least comprises position information corresponding to each target optical fiber path. The method can improve the tumor ablation rate and reduce the use of optical fibers.

The invention relates to the technical field of medical image processing, in particular to a pancreatic cancer focus tissue identification method and system based on multi-task learning, and the method comprises the following steps: obtaining a CT image containing a pancreatic tumor; marking pancreatic tumor segmentation candidate regions in the CT image through a feature extraction network and a region suggestion network; and segmenting a tumor region of interest and a region of interest around the tumor on the CT image in the pancreatic tumor segmentation candidate region through a segmentation network pre-established based on a multi-task learning optimization mechanism. According to the method, synchronous segmentation of the tumor area and the tumor surrounding area is realized, the segmentation efficiency is improved, a multi-task learning optimization mechanism is combined in the training segmentation network, the training priorities of the two tasks of dynamically and synchronously segmenting the tumor area and the tumor surrounding area in the model training process are maintained, the balance of the two tasks is trained, and the segmentation efficiency is improved. And a guarantee is provided for the segmentation synchronism of the tumor area and the tumor surrounding area.

The invention relates to the technical field of medical instruments, in particular to a steep pulse ablation coordinated control system and method based on three-dimensional dynamic electrode array.The steep pulse ablation coordinated control system comprises a multi-degree-of-freedom electrode array module, a dynamic pulse control module, a multi-modal fusion module and an intelligent optimization module; self-adaptive layout is carried out according to the shape and size of a tumor, the electrode needles can be accurately distributed no matter the tumor is spherical or irregular, for the tumor with the diameter being 3.5 cm close to the porta hepatis, six-electrode annular arrangement and vertical insertion of a central electrode can be planned, and the distance between the electrodes can be dynamically adjusted; the problems that a traditional electrode is rigid in layout and difficult to adapt to complex tumor forms are effectively solved, it is ensured that an electric field completely covers a tumor area, the matching degree of an ablation area is improved, and complete ablation of irregular tumors is facilitated.

The invention discloses an image recognition method and system for tumor three-dimensional positioning, and relates to the technical field of image recognition. The method comprises the steps of extracting modal features of a CT image and an MRI image, performing alignment in a manifold space, performing weighted fusion to obtain a fusion feature tensor, calculating spinor representation of each position through a spinor deformation field generation network SpinNet, and generating a deformation field by using a fractional order ordinary differential equation; mapping the MRI image to a CT image space through a deformation field to generate a registration fusion image, and performing tumor region segmentation to generate a tumor probability graph; and calculating a tumor center coordinate according to the tumor probability graph and the dynamic image sequence, fitting a motion track, and generating a three-dimensional recognition image. The accuracy and robustness of tumor segmentation are improved through multi-modal feature matching and non-exchangeable geometric modulation fractional order U-Net, precise modeling of tumor space-time motion is achieved in combination with hyperbolic space Mean Shift clustering and quaternion spline interpolation, and a solid technical basis is provided for dynamic navigation and motion compensation.

The invention discloses a multi-modal MRI (Magnetic Resonance Imaging) brain tumor image segmentation method. The method comprises the following steps: data preprocessing and feature extraction: collecting and processing image data from different MRI sequences; adaptive feature selection and alignment: dynamically selecting the most representative modal features for alignment according to the information of the brain tumor; multi-scale feature fusion enhancement: introducing an extra network layer to perform feature fusion, the network layer being responsible for learning a weight distribution strategy, dynamically adjusting a fusion weight and strategy, inputting a fused feature map into a segmentation network, performing preliminary segmentation, and on the basis of the preliminary segmentation, performing multi-scale feature fusion enhancement; introducing an attention mechanism to guide the segmentation network to pay attention to the tumor region; reinforcement learning optimization: adopting a reinforcement learning algorithm to optimize the parameters and the structure of the segmentation network; uncertain region optimization: further optimizing the uncertain region in the segmentation result by adopting a post-processing method; and obtaining a three-dimensional tumor segmentation result: outputting the three-dimensional tumor segmentation result.

The invention relates to the technical field of image recognition, in particular to an osteosarcoma image intelligent recognition and classification method and system based on an image recognition model. The method comprises the following steps: collecting a tumor image and carrying out multi-sequence disturbance equilibrium enhancement to generate an enhanced block tumor image, then obtaining a color standardized image through color space transformation remapping, then extracting a tumor region image and carrying out contrast inversion processing, reconstructing a tumor feature image, and finally, carrying out multi-sequence disturbance equilibrium enhancement on the tumor feature image. Based on a preset image recognition model, tumor edge features are recognized, the edge rule degree is determined, preliminary classification is carried out, a benign tumor image is obtained, surrounding metabolite signal features of the benign tumor image are analyzed, a benign reference group is formed through combination, metabolism state comparison is carried out on tumor feature images, and metabolism difference data are generated. And performing category correction according to the metabolic state mapping data so as to identify the osteosarcoma category. According to the invention, intelligent analysis of tumor features is realized, the image processing flow is optimized, and the processing efficiency is improved.

The invention discloses a lung cancer CT image data segmentation method and device, equipment, a medium and a product, and relates to the field of image segmentation, and the method comprises the steps: constructing and training a multi-scale global optimization network; the multi-scale global optimization network comprises a multi-scale dynamic fusion module, a bottleneck layer and a global attention fusion module; the multi-scale dynamic fusion module utilizes parallel multi-branch cavity convolution and a dynamic convolution kernel, combines a channel attention mechanism and a space attention mechanism, enhances the multi-scale feature capture capability of a tumor area, and outputs an enhanced feature map; the bottleneck layer smooths the enhanced feature map; the global attention fusion module is combined with a channel attention mechanism, a channel shuffling mechanism and a space attention mechanism to capture a global dependency relationship in the smoothed feature map; and inputting the to-be-detected lung cancer CT image data into the optimal multi-scale global optimization network for tumor region segmentation, and determining a tumor region so as to improve the tumor segmentation effect and improve the generalization ability of the model.

The present invention discloses a method for detecting anti-tumor by bioelectrical impedance based on pattern recognition, which includes the following steps: S1: Use a bioelectrical impedance analysis instrument to measure the impedance of the tumor area to be detected, and obtain the current, voltage and impedance values at different depths and frequencies; S2: Design a feature extraction module to extract the key features in the impedance data at different frequencies of each channel; S3: Input the extracted key features into a multi-level feature fusion module to fuse the impedance features at different channels and frequencies to form new fused features; S4: Input the fused features into an adaptive classification module and adjust the model parameters by combining an adaptive optimization method. The present invention can automatically process and classify data through a pattern recognition algorithm, and at the same time, by combining a feature extraction module, a multi-level feature fusion module and an adaptive classification module, it can effectively improve the accuracy, sensitivity and reliability of tumor detection.

The invention relates to a deep learning-based rapid screening system for multiple pathological types of lung cancer. In order to solve the problems of shortage of pathologists, strong screening subjectivity, misdiagnosis and missed diagnosis, lung cancer tissue cases are collected and converted into all-digital slices, and after preprocessing, the slices are subjected to pathologic type labeling and classification. Feature extraction is carried out by utilizing patches in existing pre-trained advanced medical vision neural network slices, and then the patches are classified by utilizing a multi-layer perceptron based on extracted pathological image features, so that automatic identification of lung cancer pathological types is realized. Meanwhile, a thermodynamic diagram is drawn through model probability distribution, visualization of a tumor area is achieved, and the method aims at improving rapidity, normalization and consistency of lung cancer pathology screening, effectively controlling pathology screening quality and providing powerful support for precise treatment of lung cancer patients. The system is simple and convenient to operate and high in accuracy, and has a wide clinical application prospect.

The invention provides a brain tumor classification method and device based on a magnetic resonance image, and a medium. The method comprises the following steps: obtaining a magnetic resonance image to be processed; preprocessing the magnetic resonance image to obtain a preprocessed first image; the first image is input into a trained implicit high-dimensional state space hybrid network, a classification result corresponding to the first image is obtained, the implicit high-dimensional state space hybrid network comprises a convolutional embedding layer, a backbone network and a classification module which are cascaded, the backbone network is composed of a plurality of stages, and the classification module is used for classifying the first image; each stage includes a number of stacked state space multiplicative interaction blocks. The backbone network adopts a multi-layer state space multiplicative interaction block stacking structure, and through selective long-range aggregation and implicit high-order feature interaction, the computational complexity is reduced, and meanwhile, the perceptual ability to a tumor region and a boundary thereof is enhanced; on the premise of ensuring the light weight of the model, the accuracy and generalization ability of the brain tumor multi-classification task are remarkably improved.