613 results about "Pulmonary nodule" patented technology

An intelligent pulmonary nodule grading method and system based on multi-modality feature fusion. The method comprises: using clinical multi-modality data of a physical examination crowd to acquire an ROI and a VOI of a pulmonary nodule on the basis of a chest CT examination image and an examination report, designing a multi-task feature extraction network based on an attention mechanism, and acquiring radiomics features and deep-layer image features from the ROI and the VOI; designing a cross-modality feature fusion method based on graph representation learning, designing a multi-modality information extraction method, acquiring a specific feature representation and graph structure of each modality, and then performing feature representation and graph structure fusion; and providing a pulmonary nodule grading GCN model optimization and clinical verification method based on self-supervised learning, so as to implement fine grading of pulmonary nodule malignancy having only slight differences. A new way is provided for the design of a fine-grained classification algorithm; and in the aspect of solving a clinical problem, provided is a classification algorithm optimization method and clinical verification scheme in which generative self-supervised learning and comparative self-supervised learning are organically combined.

Provided is an intelligent grading method and system for pulmonary nodules based on multi-modal feature fusion, including: obtaining ROI and VOI of pulmonary nodules based on chest CT examination images and examination reports by utilizing clinical multi-modal data from physical examination population, designing a multi-task feature extraction network based on attention mechanism, to obtain radiomics features and deep image features from the ROI and VOI; designing a cross-modal feature fusion method based on graph representation learning, designing a multi-modal information extraction method, obtaining specific feature representations and graph structures of modalities, and then fusing the feature representations and the graph structures; and proposing an optimization and clinical verification method of pulmonary nodule grading GCN model based on self-supervised learning, to realize fine grading of pulmonary nodule malignancy with slight differences, thereby providing a new approach to design of fine-grained classification algorithms.

The invention discloses a pulmonary tuberculosis micro focus detection method and system based on multi-scale convolution. The method comprises the following steps: acquiring lung CT image data; the method comprises the following steps: performing feature extraction through a convolutional layer of a neural network detection model of a pulmonary nodule focus, inputting an extracted feature map into a Faster and efficient multi-scale attention fusion module, and then inputting outputs of different levels in a feature pyramid in a backbone network into a corresponding ADown module, a Conv module and an UpSample module in a multi-scale convolution module; and multiple pieces of feature information are obtained through multi-scale convolution, a prediction frame is finally generated, and a detection result is obtained. A YOLOv10 model framework is improved, the feature extraction performance is improved by using a C2f-Faster-EMA module, a multi-scale convolution module is added into a feature fusion network to process features of different scales, cross-multi-scale context feature information is captured, multiple pieces of feature information are obtained, and the information interaction capability is improved.

The invention discloses a lung CT image segmentation method based on Transform and U-Net, and the method comprises the steps: firstly obtaining a pulmonary nodule CT scanning image, carrying out the preprocessing, then constructing a segmentation model MCSTU-Net, enabling the model to extract abundant features through the fusion of a convolutional neural network and a Swin Transform module, and enabling the segmentation model to obtain a segmentation model MCSTU-Net. Enhancing and fusing feature information from different levels through a multiple attention fusion module; dividing the data set into a training set, a test set and a verification set, training and verifying the segmentation model, and finally performing focus segmentation on the pulmonary nodule CT scanning image by using the trained and verified model. According to the model and the method constructed by the invention, lesion tissues in the lung CT image can be segmented more accurately, the effectiveness of each module is verified through an ablation experiment, and doctors are assisted to diagnose and treat lung diseases.

The invention belongs to the technical field of medical treatment, and discloses a pulmonary nodule segmentation method and system based on a diffusion model, and the method employs a potential space fusion method based on anatomical constraint, integrates the anatomical priori knowledge of lung tissue contour and the like through a variational auto-encoder (VAE), and combines a KL divergence constraint strategy. And the sensitivity of the model to a tiny focus is effectively improved. Secondly, a multi-modal collaborative optimization FSUNet architecture is constructed, and a cross-scale feature pyramid fusion mechanism is utilized to cooperate with a channel attention gating and dynamic noise scheduling algorithm, so that the expression ability of multi-dimensional features is enhanced. And finally, designing a Dice-Focal joint loss function to suppress complex background noise interference, and meanwhile, based on a forward diffusion-based noise sample generation strategy, remarkably reducing the dependence of the model on annotated data. Experimental results on an LIDC-IDRI data set show that the diffusion segmentation method based on multi-scale feature fusion not only shows excellent performance, but also shows excellent performance on an FPS index at the same time.

The invention discloses a method for establishing a pulmonary nodule malignant risk prediction model by integrating iconography indexes and expired gas VOCs (Volatile Organic Compounds) data, and belongs to the field of precancerous lesions of lung cancer and early prediction of lung cancer. In order to improve the accuracy of precancerous lesions of lung cancer and early prediction of lung cancer, the method comprises the following steps: acquiring a CT image of a patient with pulmonary nodules detected in lung CT, then processing the CT image of the patient with pulmonary nodules, and extracting image data of iconography indexes; collecting exhaled air VOCs of the pulmonary nodule patient, performing exhaled air VOCs index calibration and abundance analysis to obtain exhaled air data, and then performing multi-omics data arrangement in combination with image data to obtain a multi-omics data set; formulating a lung nodule malignant risk degree standard; and constructing a multi-omics deep learning model, and training the multi-omics deep learning model by using the data set of the pulmonary nodule malignant risk prediction model to obtain a pulmonary nodule malignant risk prediction model integrating iconography indexes and expired gas VOCs data.

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

The invention discloses a CT image pulmonary nodule detection and identification method based on an improved YOLOv8 model, and belongs to the technical field of target detection, and the method comprises the steps: obtaining a lung CT image from an original mhd file in an LUNA16 data set, and generating a lung CT image original data set; s2, lung parenchyma segmentation is carried out on the lung CT image original data set generated in the step S1, the lung CT image original data set is optimized, and a region of interest is reserved; the YOLOv8 model is improved; and S3, extracting image features of the training set of the original data set of the lung CT image by using the improved YOLOv8 model obtained in the step S3 to obtain a trained CT image pulmonary nodule detection and recognition model, and testing on a test set. According to the CT image pulmonary nodule detection and identification method based on the improved YOLOv8 model, the generalization ability of the detection model and the CT image pulmonary nodule identification precision are effectively improved, and the size and the position of the pulmonary nodule in the CT image can be detected more accurately.

The invention discloses a pulmonary nodule puncture and ablation positioning device, and belongs to the technical field of pulmonary nodule puncture. Comprising mounting plates which are symmetrically arranged, vertical rods are arranged on the mounting plates, one ends of the vertical rods are fixed to the mounting plates, a transverse rod is arranged at the other ends of the vertical rods, the two ends of the transverse rod are fixed to the other ends of the two vertical plates respectively, and the transverse rod is sleeved with a sliding sleeve; a plurality of protruding edges are arranged on the outer surface of the transverse rod in the length direction, a plurality of grooves matched with the protruding edges are formed in the inner side face of the sliding sleeve and are connected to the protruding edges in a sliding mode, first sliding rods are symmetrically arranged on the sliding sleeve and are parallel to the mounting plate, and the first sliding rods are connected to the sliding sleeve in a sliding mode. One end of each first sliding rod is fixed to the sliding sleeve, the two first sliding rods are each provided with a sliding ring, and the sliding rings are arranged on the first sliding rods in a sleeving mode. According to the technical scheme, positioning and puncture path guiding are provided for puncture operation, and the puncture effect is improved.

The invention discloses a contrast learning-based X-ray image domain adaptive pulmonary nodule detection method, which comprises the following steps of: training a model by using a source domain image, copying the model to obtain a teacher model and a student model, and sending the source domain image into the student model through strong data enhancement; the target domain image is sent to the student model and the teacher model through strong data enhancement and weak data enhancement. For a source domain image, supervision loss is calculated through a label, and for a target domain image, non-supervision loss is calculated through a pseudo label. The hierarchical contrast learning module calculates region-level and pixel-level contrast losses respectively, and the adversarial learning module obtains adversarial classification losses through region-level features from a source domain and a target domain. And finally, combining loss functions to carry out model optimization, updating student model parameters, and updating teacher model parameters through an exponential moving average strategy. According to the method, a unique hierarchical contrast learning strategy is adopted, so that the problem of low-quality false labels is solved, and the accuracy of the X-ray image domain adapting to pulmonary nodule detection is effectively improved.

The invention provides a minimally invasive method for determining whether pulmonary nodules are malignant or not by measuring methylation levels of different gene combinations such as CDO1, PTGER4, HOXA9, SHOX2, SP9 and ZNF781. The disclosed methods also enable the detection of lung cancer. The invention also discloses a polynucleotide and a kit which can be used for measuring the methylation level of CDO1, PTGER4, HOXA9, SHOX2, SP9 and ZNF781.

The invention discloses a pulmonary nodule boundary detection method based on semantic segmentation. The method comprises the following steps: generating an edge prior image set; obtaining a multi-scale boundary alignment feature map set and a deformation offset parameter set; a post-gating deformation offset parameter set is obtained, the multi-scale boundary alignment feature image set is updated, cross-scale feature fusion is executed, and a fusion feature image set is generated; establishing a node set and an edge set by utilizing node features in the fusion feature graph set and a spatial adjacency relationship, and initializing a node feature vector set; forming a fusion feature map set enhanced by multi-hop reasoning; and outputting a pulmonary nodule region mask prediction image set and a pulmonary nodule boundary probability prediction image set. According to the method, fracture between pseudo boundaries and slices is effectively inhibited, so that a three-dimensional structure prediction result is more coherent, and high-readability and high-consistency boundary segmentation output is provided for a clinical CAD (Computer Aided Design) system.

The invention provides an interventional operation planning method and system based on anatomical structure modeling, and the method comprises the steps: carrying out the anatomical structure modeling of an interventional operation target focus and surrounding tissues and organs, and obtaining an anatomical structure three-dimensional model; based on the anatomical structure three-dimensional model, acquiring initial puncture entry candidate points of the interventional operation; screening the initial puncture entry candidate points of the interventional operation to obtain final puncture entry candidate points of the interventional operation; and carrying out joint visualization on the anatomical structure three-dimensional model, the plurality of final puncture entry candidate points of the interventional operation and the quantitative information of the puncture indexes of the puncture entry candidate points so as to provide puncture entry point simulation and reference for the interventional operation and complete interventional operation planning. According to the method, interpretable reference is provided for decision making of interventional operation planning based on multi-dimensional quantitative indexes and visual risk prompts, the pulmonary nodule interventional operation process can be optimized, and then the risk of the interventional operation is reduced.

The invention discloses a pulmonary nodule risk prediction method, system and device based on a large model, and a medium, belongs to the technical field of artificial intelligence, and aims to solve the technical problems of insufficient generalization ability of a network model and poor pulmonary nodule multi-task prediction effect in the prior art. The method comprises the following steps: acquiring sample data of multiple modalities including medical images, gene detection and demographic data; single-mode features are obtained through an exclusive feature extraction module, and then the single-mode features are input into a plurality of expert networks to obtain single-mode experts; obtaining a multi-modal feature after the single-modal feature passes through a cross-modal attention module, and inputting the multi-modal feature into a plurality of expert networks to obtain a plurality of multi-modal experts; the three task MoE modules are all provided with task gating networks in a matched mode, and the task MoE modules select the most appropriate expert set according to the calculated probability that each expert is activated; acquiring to-be-predicted data, inputting the to-be-predicted data into the selected expert set, and outputting malignant, growth and wettability risk prediction results of the pulmonary nodules by the task model.

The invention belongs to the technical field of benign and malignant diagnosis model systems for solid pulmonary nodules, and particularly relates to a benign and malignant diagnosis deep learning model system for solid pulmonary nodules. Comprising an overall architecture system of the model, a data set introduction system, a data set preprocessing system, a lung CT image segmentation system, a lung nodule detection system, a lung nodule category system, an experiment parameter setting system, an evaluation index system and an experiment setting and result system. A lower half branch of the network is mainly designed, the lower half branch network extracts multi-scale feature information of a lung CT image through an Inception module, and convolutional attention modules such as ECA and CBAM are introduced to enhance the extraction effect of the lower half branch network on position and shape features of the lung CT image.

The invention provides a pulmonary nodule display method and device, electronic equipment and a storage medium, and the method comprises the steps: segmenting a three-dimensional reconstruction image of the chest of a target patient to obtain a preoperative segmentation image, the three-dimensional reconstruction image being obtained based on preoperative CT data, and the preoperative segmentation image comprising the position information of a pulmonary nodule; segmenting the video image of the intraoperative lung tissue of the target patient collected by the thoracoscope in real time to obtain an intraoperative segmented image; feature matching is conducted on the preoperative segmented image and the intra-operative segmented image, the lens pose of the thoracoscope is determined, the preoperative segmented image is mapped into a two-dimensional reference image based on the lens pose, and the two-dimensional reference image comprises the position information of the pulmonary nodule; and carrying out image registration on the two-dimensional reference image and the intra-operative segmented image, and carrying out pulmonary nodule marking on the intra-operative segmented image to obtain a video image for displaying pulmonary nodules in real time. The position of the pulmonary nodule in the operation is accurately displayed in real time in a non-invasive mode, and the operation efficiency is improved.

The invention relates to a pulmonary nodule minimally invasive needle biopsy excision device, and relates to the technical field of medical instruments. The system comprises a mechanical arm electrically connected with CT equipment, navigation auxiliary equipment, a puncture assembly detachably connected with the mechanical arm and used for puncturing human tissue, a sampling assembly for sampling tissue at a nodule and a marking assembly for marking the sampling position; the puncture assembly comprises a mounting base detachably connected with the mechanical arm and a puncture needle arranged on the mounting base, and the end of the puncture needle is sharp. The puncture needle is arranged in a hollow mode, the sampling assembly comprises a rotary cutting cylinder rotationally arranged in the puncture needle and a power part for driving the rotary cutting cylinder to rotate, a first rotary cutting opening is formed in the puncture needle, a second rotary cutting opening is formed in the rotary cutting cylinder, and the first rotary cutting opening movably corresponds to the second rotary cutting opening. The biopsy sampling device has the advantages that the sampling amount of each biopsy sampling is conveniently increased, the puncture sampling frequency is reduced, and meanwhile the radiation dosage is reduced.

Systems and methods for performing one or more medical imaging analysis tasks on PCCT (photon-counting computed tomography) images are provided. Image acquisition parameters of a PCCT image acquisition device are determined for acquiring PCCT images. One or more PCCT images of an anatomical object of a patient acquired using the PCCT image acquisition device configured with the image acquisition parameters are received. One or more medical imaging analysis tasks analyzing the anatomical object are performed based on the one or more PCCT images using one or more machine learning based models. Results of the one or more medical imaging analysis tasks are output.

The invention discloses a pulmonary nodule detection method, system and device based on Mama and global multi-scale feature fusion, and the method comprises the steps: carrying out the improvement of a Center Net algorithm, constructing a pulmonary nodule detection model, replacing an original feature extractor in the Center Net with a feature extractor based on Mama, and carrying out the construction of a pulmonary nodule detection model; adding a multi-scale feature aggregation module for fusing features from different scales; detecting the to-be-detected lung CT image by using the pulmonary nodule detection model, wherein the method comprises the following steps: performing feature coding on the to-be-detected lung CT image; performing feature decoding on the encoded multi-scale feature map; inputting the multi-scale feature maps of different decoding stages into a multi-scale feature aggregation module, and aggregating features with different functions; bounding box regression prediction is carried out, the pulmonary nodules are positioned through central point prediction, offset prediction and size prediction, and a final detection result is obtained through combination; compared with a traditional method, the method can improve the automatic and accurate detection capability of the pulmonary nodules in the CT image.

The invention discloses a three-dimensional pulmonary nodule detection method and system based on morphological adaptation convolution, and the method comprises the steps: obtaining a chest CT image sequence, and carrying out the preprocessing of all chest CT images in the chest CT image sequence; according to the three-dimensional pulmonary nodule detection method and system based on form adaptive convolution, scale factors are introduced into a 3D dynamic convolution layer and cooperate with the offset, so that the position of a convolution kernel sampling point can be adaptively adjusted according to the form and size of the pulmonary nodule, and the problem that the traditional fixed convolution is difficult to adapt to the size difference and form heterogeneity of the pulmonary nodule is solved; stable detection of pulmonary nodules with different sizes, especially tiny pulmonary nodules, is realized; space coordinate information and multi-scale features are combined through a channel space attention module, extraction of key information such as pulmonary nodule edge texture and density gradient is enhanced, interference of blood vessel, trachea and CT artifacts is inhibited, and the problem that small nodules and background noise are difficult to distinguish is solved.

The invention discloses a pulmonary nodule image segmentation method and device based on optimization down-sampling and feature fusion, and relates to the technical field of image processing, the method comprises the steps: obtaining a data set containing a plurality of chest CT images, cutting the chest CT images in the data set into a preset-size region of interest containing pulmonary nodules, and obtaining a plurality of pulmonary nodules; constructing to obtain a training set, a verification set and a test set; the method comprises the following steps: constructing an SPDD module for realizing space-to-depth downsampling and a DPFM module for realizing dual-branch fusion, and creating a DFNet model based on the constructed SPDD module and DPFM module; and training, verifying and testing the DFNet model based on the training set, the verification set and the test set, and evaluating the performance of the DFNet model. The pulmonary nodule segmentation capability of the model can be effectively improved.

The present invention relates to a method which allows for determining if the lung nodules of a patient are benign or cancerous. Furthermore the method also allows to determine if a patient is at high risk of developing lung cancer and if it should be subject to medical intervention.

The present application relates to a lung-cancer screening model construction apparatus, a lung-cancer screening apparatus, and a device and a medium. The lung-cancer screening model construction apparatus comprises: a sample acquisition and data collection module, which is used for acquiring benign pulmonary nodule patient samples, and collecting baseline parameters of the benign pulmonary nodule patient samples, wherein the baseline parameters comprise radial growths of pulmonary nodules and serum thymidine kinase 1; a model training module, which is used for performing model training by using the baseline parameters as independent variables and using, as dependent variables, risks of lung cancer newly diagnosed within three years after baseline visits; and a model evaluation and verification module, which is used for performing prediction efficiency evaluation and internal verification on the trained model, so as to obtain a risk prediction model for newly diagnosed lung cancer of benign pulmonary nodule patients within three years. The present application can be used for predicting the risk of progression from benign pulmonary nodule to lung cancer in a large-sample Chinese population.

The embodiment of the invention provides a pulmonary nodule automatic labeling method based on multi-modal semantic affinity iterative optimization, and belongs to the technical field of data processing, and the method specifically comprises the steps: obtaining a lung CT image and a text report; performing structured semantic analysis on the text report; performing visual feature extraction on the lung CT image; based on the key semantic information, performing semantic guidance processing on the depth visual feature representation to generate semantic enhanced visual guidance features; based on the semantic enhanced visual guidance features, calling a segmentation basic model, and generating a plurality of candidate segmentation masks by introducing an active variation mechanism; for each candidate segmentation mask, calculating a semantic matching degree score of the candidate segmentation mask; and comparing the semantic matching degree scores of all the candidate segmentation masks, and selecting the candidate segmentation mask with the highest score as a final pulmonary nodule labeling result to be output. Through the scheme of the invention, the labeling precision, adaptability and interpretability are improved.

The present invention discloses a dandelion flavonoid derivative, its preparation method, and application. The dandelion flavonoid derivative has the following molecular structure: The dandelion flavonoid derivative provided by the present invention has anti-inflammatory and lung nodule-removing effects, is simple to prepare, and has good biocompatibility, making it a promising new drug for eliminating lung nodules.

The invention discloses an intraoperative pulmonary nodule positioning method and system based on fusion of a CT image and an endoscope video. The method comprises the following steps: firstly, constructing a three-dimensional lung model containing a pulmonary nodule position based on preoperative high-resolution CT data; generating a real-time lung surface model by using an endoscope video stream through a three-dimensional reconstruction technology during the operation; a feature point extraction and matching algorithm is adopted, and a non-rigid registration technology is combined to realize accurate registration of the preoperative model and the intraoperative dynamic model; projection display of pulmonary nodules in a real-time endoscope visual field is established through space coordinate mapping, and a dynamic compensation algorithm is developed to solve the problem of tissue deformation caused by respiratory movement and instrument operation. Meanwhile, the virtual nodule position is overlaid to an endoscope video picture through the augmented reality technology, and visual intraoperative navigation is provided for surgeons. The method effectively solves the technical bottleneck that a traditional method depends on a metal marker and cannot adapt to intraoperative tissue deformation and the like, and has clinical practical value.

The invention provides a context feature aggregation pulmonary nodule segmentation method combining edge perception and multi-scale semantic guidance, and the method comprises the steps: obtaining a to-be-segmented pulmonary nodule CT image, inputting the to-be-segmented pulmonary nodule CT image into a trained EGP-Net network model, and obtaining a segmentation result; wherein in the EGP-Net network model, multilayer features of an encoder are sent to a global pyramid guide module to reconstruct and selectively introduce deep global semantics back to a shallow layer, and low-layer features are merged into an edge guide network to extract and reinforce fine-grained boundary information; the attention feature fusion module performs weighted attention fusion on global semantics and local edge features, and the multi-scale context decoder fuses multi-scale information through lightweight reconstruction such as sub-pixel convolution to generate a fine segmentation map. Experimental results show that the EGP-Net network model is superior to an existing advanced method in segmentation precision and boundary consistency, and has good clinical auxiliary diagnosis and precision medical application potential.

The invention relates to a pulmonary nodule distinguishing processing method based on a CT image, and the method comprises the following steps: carrying out the CT scanning of a user, obtaining lung image data, carrying out the three-dimensional reconstruction of the lung image data, and obtaining a three-dimensional image; performing pulmonary nodule attention recognition based on the three-dimensional image through a preset recognition algorithm to obtain a pulmonary nodule image; segmenting the pulmonary nodule image to obtain an internal structure and pulmonary nodule characteristics of the pulmonary nodule; performing risk assessment on the pulmonary nodule based on the internal structure and the pulmonary nodule characteristics to obtain a risk assessment level; if the risk assessment level exceeds a predetermined risk level, classifying the pulmonary nodules corresponding to the risk level exceeding the predetermined risk level to obtain a classification result of the pulmonary nodules; the technical problem that artificial recognition is difficult due to the fact that pulmonary nodules are diverse in form and low in contrast ratio is solved.

The invention provides a pulmonary nodule segmentation method and device based on ABVM-UNet, and the method comprises the steps: S1, collecting a lung CT image data set, carrying out the enhancement processing of a pulmonary nodule region in an image, and highlighting the texture and edge features of a nodule; s2, constructing a main branch and a target enhancement auxiliary branch based on a Gabor convolutional neural network to realize enhancement response to pulmonary nodule texture and edge features; s3, using a branch information interaction fusion module BIIF to realize fusion and transmission of auxiliary branch enhancement features; s4, constructing a U-shaped architecture network ABVM-UNet based on Vision Mama, and realizing effective fusion of the local features of the image and the global context information; s5, multi-stage segmentation loss is realized by using depth supervision, and the ABVM-UNet model is trained and optimized; and S6, carrying out pulmonary nodule segmentation on the lung CT image by using an ABVM-UNet model. According to the method, the edge and texture features of the pulmonary nodule are effectively extracted by introducing the ABVM-UNet model, so that accurate segmentation of the pulmonary nodule is realized.

The invention discloses a pulmonary nodule benign and malignant classification method based on a cross-modal space-time fusion network, and the method comprises the steps: firstly obtaining CT image data, and carrying out the preprocessing of the data, and obtaining an ROI image; then, extracting spatial features in the ROI image through a spatial feature extraction module, and obtaining text features; obtaining fused CT image spatial-temporal features and clinical text features through a time residual fusion module; inputting the output passing through the time residual fusion module into a cross-modal attention fusion network, and performing preliminary fusion to obtain a final cross-modal attention graph; a multi-head self-attention fusion block is used to carry out further fusion processing on the cross-modal attention map, and feature representation based on final fusion spatio-temporal data and clinical data is obtained; and finally, outputting a benign and malignant classification result through an FC layer. Firstly, features are extracted from CT image data of follow-up visit, space-time fusion is carried out, then the features are combined with text clinical data, and the classification accuracy and robustness are effectively improved.