236 results about "Lesion feature" patented technology

According to the medical image segmentation method and device based on spatial perception and frequency domain information, the precision and robustness of medical image segmentation are effectively improved by combining frequency domain information guidance and a multi-head state spatial perception technology. Frequency domain transformation is performed on a medical image, high-frequency and low-frequency components of the image are separated by using a multi-scale decomposition technology, and low-frequency features are extracted to obtain global information. By introducing a learnable noise filtering mechanism, noise and irrelevant background information in a frequency domain are suppressed, so that the model can be focused on a lesion area more accurately. A multi-head perception visual state space module is designed on a bottleneck layer, lesion features of different scales are captured through a multi-scale adaptive feature fusion mechanism, and the capability of segmenting small-size lesions and complex structures is enhanced. A context focusing attention mechanism is introduced into jump connection, fusion of global information and local details is further enhanced, and the accuracy of a segmentation result is ensured; and finally, recovering a high-resolution segmented image through a decoder.

The invention relates to the technical field of ophthalmology medical diagnosis, and discloses a three-dimensional reconstruction-based fundus focus intelligent labeling method and diagnosis system. The method comprises the following steps: receiving multi-modal image data streams such as fundus color photos, OCT images and FFA images of an ophthalmological patient; performing spatial registration and feature fusion by using a pre-trained lesion feature fusion model to generate a three-dimensional lesion probability distribution diagram and a lesion category confidence matrix; constructing an adaptive annotation threshold model to generate a multi-modal annotation instruction set; based on the focus development chain model, focus development is simulated, and instruction set parameters are optimized and labeled; and iteratively optimizing through a distributed reinforcement learning framework, and outputting the focus labeling action sequence to an ophthalmology diagnosis platform. According to the method, multi-modal image information can be integrated, the diagnosis accuracy and efficiency are improved, personalized diagnosis is realized, resources are reasonably utilized, and powerful support is provided for ophthalmic disease diagnosis.

The invention provides a construction and training method and device for an eye fundus image multi-task model, and belongs to the field of image processing, and the method comprises the steps: S1, collecting and sorting a public eye fundus image data set, constructing an image text pair according to a real label, and carrying out the two-stage training of a multi-modal large language model, the multi-mode large language model comprises a visual encoder, a visual projector and a large language model; s2, inputting the image data # imgabs0 # into a visual encoder in the trained multi-modal large language model to obtain an enhanced visual feature # imgabs1 #, and extracting a visual feature # imgabs3 # from the # imgabs2 # through a visual projector; and S3, embedding the text input # imgabs4 # to obtain a text feature # imgabs5 #, splicing the text feature # imgabs5 # with the visual feature # imgabs6 #, and inputting the spliced text feature # imgabs5 # and the visual feature # imgabs6 # into a large language model to generate a prediction text A. According to the method, a wide range of fundus image data is collected for training, multilevel lesion features in the fundus image are fully utilized, and the performance of the model for executing a fundus disease auxiliary diagnosis task can be effectively improved.

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

The invention discloses a heterogeneous double-flow fusion method and system for diabetic retinopathy grading. The method comprises the following steps: obtaining an output result of diabetic retinopathy grading by utilizing a heterogeneous double-flow architecture; processing an input fundus image into images with different resolutions; extracting global context features from the low-resolution image by using a lightweight visual Transform model distilled by composite knowledge, and extracting local focus features from the high-resolution image by using a convolutional neural network model; performing interactive fusion on the global context features and the local focus features of the double-branch architecture through a symmetric bidirectional cross attention fusion module to obtain enhanced fusion feature representation; and finally, inputting the fusion features into a classifier, and outputting a severity grading result of the lesion. The method aims at improving the accuracy and robustness of hierarchical diagnosis through deep analysis of global information and local details, and can be applied to the medical fields of clinical computer-aided diagnosis, eye image analysis and the like.

The invention discloses a colorectal cancer focus segmentation method based on improved TransUNet. The method comprises the following steps: collecting related pathological image data of a colorectal cancer patient; enhancing and expanding the data set by adopting a data enhancement technology, and adjusting and processing the image data; the method comprises the following steps: constructing a model, integrating a PEMA module at multiple positions of the model, introducing an EUCB up-sampling module into a decoder part, replacing standard convolution of the decoder part with lightweight dynamic convolution, and using a composite loss function MediBoundFusion Loss; the preprocessed training data set is input into the improved TransUNet network model to be trained; and the colorectal cancer focus is segmented by adopting the improved TransUNet network model after training is completed. The key problems that focus features are fuzzy, boundaries are difficult to define, forms are irregular, and effective features are difficult to extract due to low contrast of early cancerous tissues can be solved.

The invention discloses a CT image analysis method and system based on a neural network, and relates to the technical field of CT image analys.The method comprises the steps that an original CT image is obtained after user authorization, a Laplace operator is adopted to strengthen a focus boundary, and a circular region of interest is intercepted to remove edge sensitive information; extracting edge and texture information in the standardized image; focus area features are focused step by step; executing characteristic distillation balance based on category sample distribution, and outputting a focus characteristic graph with local perception enhancement and sample balance characteristics; segmenting the lesion feature map into serialized units, embedding position codes, inputting the serialized units into a plurality of layers of encoders, and fusing an image structure and text indication information through a dynamic adjustment mechanism; performing linear classification on the global semantic vector to output a diagnosis result, generating a focus thermodynamic diagram, and superposing the focus thermodynamic diagram to an original image for visualization; and performing dynamic optimization based on doctor feedback. The accuracy of feature analysis is improved; the overall operation efficiency of the system is improved.

The invention discloses a multi-scale adaptive lesion detection method based on mammary gland ultrasound. The method comprises the following steps: pre-processing an image; the input module is used for extracting features through convolution blocks to clearly display boundaries, and then deconvolution blocks are used for refining boundary features to complete image expression; a trunk module; in the neck network, the CA generates feature maps in two directions, the feature maps in the two directions are spliced, features are extracted through convolution operation, and attention weights in the two directions are further generated; and the detection head network performs positioning prediction, classification prediction and loss calculation, and finally outputs a result. According to the method, the precision and richness of feature extraction are improved, and the clear recognition capability of the model on different tissue boundaries is enhanced; the capacity of capturing multi-scale lesion features is improved, and the lesion features are better captured; important areas such as small calcification points, changes of cyst walls and boundaries of fibroadenoma can be highlighted, and the accuracy and specificity of detection are improved.

The invention discloses an artificial intelligence-based periodontal state evaluation method, which comprises the following steps of: acquiring multi-modal tooth data which comprises a tooth surface image and a local blood vessel network image of an oral cavity; preprocessing the multi-modal tooth data, and performing cross-modal registration by using a registration module to obtain a registered tooth surface image and a registered blood vessel network image; extracting multi-dimensional features of the registered blood vessel network image through a first feature extraction module, wherein the multi-dimensional features comprise spatial morphological features, topological connectivity features and hemodynamic features of blood vessels; extracting macroscopic morphological features and lesion local features of the registered tooth surface image through a second feature extraction module; performing cross-modal fusion on the multi-dimensional features, the macroscopic morphological features, the local lesion features and the clinical data to obtain fusion features; and outputting a periodontal state evaluation result based on the fusion features, thereby facilitating improvement of periodontal state evaluation accuracy and periodontal disease early warning capability.

The invention relates to the technical field of disease diagnosis, and comprises a disease diagnosis method and system based on neural network cognitive diagnosis, and the method comprises the following steps: obtaining medical image data, calculating the gray level change rate, gradient direction distribution and edge continuity of a lesion region, counting the lesion tissue damage area, and analyzing the pathological tissue damage degree. And obtaining a lesion distribution consistency index. According to the invention, through comprehensive calculation of the medical image data and the pathological tissue slice data, fine-grained description of a focus area is realized, association among different lesion features is realized, and calculation of a lesion propagation path matching rate is realized, so that identification of lesion diffusion conditions is more accurate, and development trends of diseases in different tissue structures can be effectively predicted; the calculation of the cross-modal feature error is combined with the adjustment of the distribution weight of the lesion region, misdiagnosis caused by modal difference and extraction of abnormal signals in a lesion diffusion range are reduced, so that screening of potential diseases is more targeted, and the accuracy of disease recognition is improved.

The invention provides a lightweight medical image segmentation network (ABUNet) based on asymmetry and an implementation method thereof, and the method comprises the following steps: S1, in a coding stage, proposing a feature subtraction convolution block (FSCB), and implementing O (C2 / N)-level parameter compression (N is a group number) by using channel feature difference operation; in a lightweight scene, the FSCB can effectively reduce feature redundancy, directly highlights key features of a lesion area, and is superior to traditional feature operation based on addition and multiplication; s2, in a decoding stage, a feature addition convolution block (FACB) is designed, a multi-branch feature fusion mechanism is adopted, and the alignment precision of different feature representations is improved under the condition that the calculation cost is not increased; and S3, in a bridging stage, a multi-scale deep convolutional block (MSDB) is constructed, and the multi-scale context modeling capability of the model is remarkably enhanced by utilizing heterogeneous kernel parallel computing, so that more accurate lesion feature extraction is realized. And S4, in a network integration stage, an FSCB module is integrated into an encoder part of a U-shaped architecture, an FACB module is integrated into a decoder part, and an MSDB module is used for processing grouping characteristics in a bridging module to construct an asymmetric model ABUNet. The asymmetric architecture overcomes the limitation of symmetry of a traditional encoder-decoder, and effectively balances high segmentation precision and calculation efficiency.

The invention relates to the technical field of focus positioning, in particular to a hysteromyoma focus accurate positioning method combining image analysis and data fusion, which comprises the following steps: acquiring multi-modal uterus image data, and carrying out artifact suppression and spatial registration on an original image; performing three-dimensional reconstruction on the preprocessed multi-modal image data set to generate a three-dimensional uterus model comprising anatomical structure features; lesion feature segmentation is carried out in the three-dimensional uterus model, and morphological parameters and textural features of a myoma lesion are extracted; carrying out hemodynamic analysis to obtain blood perfusion parameters and blood vessel distribution characteristics of the focus area; the blood perfusion parameters and the blood vessel distribution characteristics are fused, the three-dimensional uterus model is combined, and a focus biomechanical characteristic distribution diagram is constructed; and generating a real-time navigation path according to the biomechanical characteristic distribution diagram, and outputting a positioning report. According to the method, personalized ablation energy suggestions are made, the ablation treatment efficiency and success rate are effectively improved, the ablation energy is dynamically adjusted, and the treatment efficiency and safety are ensured.

The invention discloses an Alzheimer's disease early warning method based on white matter lesion omics characteristics, and relates to the field of wisdom medicines.The method comprises the steps that magnetic resonance imaging data of a historical subject in the period from the mild cognitive impairment period to the period before diagnosis of Alzheimer's disease are obtained, and manual labeling of white matter and white matter lesion areas is carried out; a manual annotation data set is obtained; training a deep learning model for white matter lesion recognition based on the manual annotation data set; inputting to-be-identified magnetic resonance imaging data into the deep learning model, and extracting lesion features of the white matter; performing standardization and feature alignment on the extracted lesion features, and inputting the lesion features into a deep clustering model to form clustering results for different white matter lesion feature types; and an early risk assessment model is constructed based on the clustering result and the Alzheimer's disease transformation risk tag corresponding to the clustering result, and the Alzheimer's disease transformation risk level of the subject is output, so that the problems that multiple lesion features are difficult to quantify and details are difficult to identify are solved.

According to the method, the de-noising diffusion model TCG-DiffDRC is innovatively and specially applied to classification of the diabetic retinopathy. According to the model, a triple condition guidance strategy is designed, deep mining of lesion features is achieved through three independent branches, overall features of an image are extracted through an improved ResNet network in a global feature branch, a global descriptor is generated through a class activation graph CAM, a lesion detail branch is based on an interpretable neural transducer (INTR) model, and the lesion detail branch is based on an explained neural transducer (INTR) model. And fine features of the lesion are extracted through a multi-head attention mechanism of Transform. And then the extracted features are sent to a diffusion model for training, image reconstruction is refined step by step, and finally efficient classification of diabetic retinopathy is realized. The performance of experimental results on a challenging APTOS2019 data set proves the superiority of the TCG-DiffDRC model, the accuracy of the TCG-DiffDRC model reaches 86.3%, the Kappa value reaches 75.8%, the effectiveness of the TCG-DiffDRC model in a medical image classification task is proved, and the TCG-DiffDRC model is superior to the current most advanced method.

The invention provides a pseudo-CT cross-modal conversion method and system, and the method comprises the steps: carrying out the registration and alignment of a CT image and an MR image, employing an LLM to analyze a clinical text report, extracting the description of an anatomical structure, lesion features and a spatial relation, generating a structured condition vector, carrying out the fusion of text semantic embedding and MR image features, and obtaining a pseudo-CT image. The adversarial diffusion model is driven to generate a high-fidelity pseudo CT, the training process of the diffusion model is divided into two stages, only the condition generation capacity of the diffusion model is optimized in the first stage, the semantic alignment capacity of the diffusion model and the LLM is optimized in the second stage at the same time, finally, the MR image to be converted is input into the trained diffusion model, and an accurate pseudo CT image is rapidly output.

The invention discloses a focus recognition system and method for a diabetic retinopathy image, and relates to the technical field of medical image processing, and the system comprises a fundus image collection module, an image feature processing module, a focus feature extraction module, a network model training module and a focus model recognition module. By integrating the fundus image collection module, the image feature processing module, the lesion feature extraction module, the network model training module and the lesion model recognition module, full-process automation from original fundus image acquisition to lesion intelligent recognition is realized, and the efficiency of diabetic retinopathy lesion recognition is remarkably improved; in addition, errors possibly caused by manual intervention are reduced, the accuracy and consistency of recognition results are ensured, specifically, the fundus image collection module can automatically obtain images from ophthalmology equipment or a hospital image storage system, format unification and index construction are carried out, and great convenience is provided for follow-up processing.

The invention discloses a medical image artifact identification and elimination method based on a big data technology, and relates to the technical field of medical image processing, and the method comprises the steps: carrying out the preprocessing of collected image data based on gray normalization, and then carrying out the semantic segmentation and ROI positioning of the image content; and performing lesion segmentation on the positioned image content, selecting lesion features for quantification and fusion, performing model verification, and performing distributed deployment on the verified lesion segmentation model. According to the method, the problems of high missed detection rate of small nodules and high missed diagnosis risk of malignant lesions are solved through the lesion detection model, the false positive rate is reduced, the recall rate of the malignant lesions is improved, and through the lesion segmentation model, the segmentation adaptability to lesions of different sizes is improved, clear segmentation boundaries are obtained, surgical planning is assisted, and boundary positioning errors are reduced.

The invention discloses an alimentary canal lesion analysis method and system based on image recognition, and belongs to the technical field of image recognition analys.The alimentary canal lesion analysis method comprises the steps that an image collection module is used for collecting images of an alimentary canal of a current patient, and data segmented, extracted, recognized and counted by an image processing module is transmitted to the image collection module and a lesion calculation module; a lesion calculation module is used for sequentially calculating and outputting a preliminary evaluation value WP, an associated part influence value G and a comprehensive lesion risk value BF, and based on the comprehensive lesion risk value BF, a result display module is used for performing result display and analysis. According to the method, multiple factors are integrated, the accuracy and comprehensiveness of analysis are improved, finally, the factors of the lesion in the space dimension and the time dimension are integrated, and the comprehensive risk degree of the lesion of the digestive tract is comprehensively and accurately evaluated.

The invention relates to the technical field of medical image processing, in particular to an embolism focus segmentation method and system based on medical image processing. The system comprises a medical image feature registration module, an image frequency band fusion enhancement module, a deep network lesion segmentation module and a lesion segmentation image fusion optimization module, an embolism CT image and an embolism MRI medical image can be acquired, and feature point minimization registration and image frequency band fusion are performed to generate an embolism fusion image; performing histogram equalization processing on the embolism fusion image to generate an embolism comparison standard image; constructing a corresponding deep network lesion segmentation model, and performing network lesion segmentation processing, up-sampling and element-by-element splicing fusion to generate an embolism lesion feature fusion image; and performing morphological optimization operation on the lesion boundary corresponding to the embolism lesion feature fusion image to obtain an embolism lesion segmentation result. According to the invention, accurate segmentation of the embolism focus in the medical image can be realized.

The invention relates to a lesion three-dimensional stripping method of a medical image. The method comprises the following steps: acquiring an ovary ultrasonic medical image of a patient in each direction, preprocessing the ovary ultrasonic medical image and inputting the preprocessed image into a three-dimensional image processing system; performing three-dimensional reconstruction on the ovary ultrasonic medical images in each direction to obtain three-dimensional images of the ovary and adjacent tissues; slicing processing is carried out along a certain direction of the three-dimensional image to obtain a plurality of first slice images, image feature extraction is carried out, features matched with preset lesion feature items are obtained through feature comparison and marked, lesion areas are reserved, and areas outside the lesion areas are deleted; and recombining to construct a first three-dimensional image of the focus, identifying the first three-dimensional image of the focus, when the first three-dimensional image belongs to a specified category or the size of the first three-dimensional image is greater than a preset threshold value, acquiring an ovary CT medical image, performing three-dimensional reconstruction of the focus again, and acquiring and outputting a second three-dimensional image of the focus and the category and size of the focus. The three-dimensional construction of the ovary lesion can accurately assist a doctor in accurately diagnosing the disease of a patient.

Systems and methods for delineating tumour boundaries using a three-dimensional (3D) image stack, such as photoacoustic image slices, comprising a plurality of images. Multiple maximum intensity projections (MIPs) are derived from the stack, and level set segmentation is performed on those MIPs, to derive a boundary that bounds a region of interest (i.e., a tumour boundary). Structural and functional information is then derived for the region of interest, to analyse the tumour tissue.

The invention relates to the technical field of medical images, in particular to a medical image intelligent detection and auxiliary diagnosis system based on deep learning, and the system comprises a data access module which is used for obtaining medical image data and clinical text data of a patient; the data fusion module is used for generating a focus feature vector and a text feature vector, and performing cross-modal alignment and fusion to generate a fusion feature vector; the diagnosis analysis module is used for executing focus detection, focus segmentation and focus classification tasks, generating a diagnosis result and generating a diagnosis label based on the diagnosis result; the decision generation module is used for mapping the generated execution result to a preset medical knowledge base and performing deep reasoning to generate an auxiliary diagnosis decision; the decision auditing module is used for performing confidence scoring on the auxiliary diagnosis decisions and selecting the auxiliary diagnosis decision with the highest confidence score as the final auxiliary decision; and the data visualization module is used for carrying out visualization processing on the auxiliary decision and the diagnosis result.

The invention relates to a diabetic retinopathy fundus photography grading report system combined with a clinical guide, based on an artificial intelligence deep learning technology, and belongs to the field of fundus lesion analysis. The system accurately identifies and segments various lesions such as microhemangioma, bleeding, exudation and the like and symbolic structures such as optic discs, macular regions and the like by automatically analyzing fundus photographic images. The system adopts ICDR international standards to grade diabetic retinopathy, and provides diagnosis and treatment suggestions for lesion characteristics in combination with clinical guidelines of American ophthalmology institute in 2019. Through big data training, the system can generate detailed reports in real time, the early screening rate is remarkably improved, misdiagnosis and missed diagnosis are reduced, and the diagnosis speed and accuracy are improved. The system comprises a plurality of modules, such as an image pre-classification module, a deep learning focus recognition module, an ICDR grading module and a report generation module, efficient and accurate diabetic retinopathy diagnosis and grading are cooperatively achieved, and the clinical management level is improved.

The invention discloses a thyroid ultrasound diagnosis report intelligent generation method based on a multi-mode large language model, and relates to a thyroid ultrasound diagnosis report intelligent generation method. The objective of the invention is to solve the problems of lack of term standardization and insufficient complex focus feature analysis in the prior art. According to the method, a full-flow technical system of double-flow coding, cross-modal alignment, dynamic man-machine cooperation and multi-dimensional evaluation is constructed. Multi-scale feature fusion of a thyroid global form and a nodule ROI region is realized through ResNet-50 and ConvNeXt double-flow coding networks, image-text semantic alignment is optimized by adopting a CLIP symmetry loss function, and training resource consumption is reduced in combination with an LoRA parameter fine tuning technology. A dynamic man-machine collaborative closed-loop mechanism is innovatively introduced, model parameters are iteratively optimized through doctor correction data, and a four-dimensional clinical evaluation system comprising ROUGE-L, BLEU-4, CIDEr and expert blind evaluation is established. The invention belongs to the technical field of medical artificial intelligence auxiliary diagnosis.

The invention relates to an endometrial benign and malignant disease auxiliary prediction model construction method and system. The method comprises the following steps: acquiring first historical image information of a non-target part and second historical image information of a target part; preprocessing is carried out respectively, and a corresponding pre-training sample data set and a training sample data set are constructed respectively; constructing a convolutional neural network model, and inputting the pre-training sample data set into the convolutional neural network model for comparative learning pre-training; and inputting the training sample data set into the pre-trained convolutional neural network model for training until the training is completed, thereby obtaining the endometrial benign and malignant disease auxiliary prediction model. By utilizing a contrast learning technology, the recognition capability of a convolutional neural network model on tiny lesion features is enhanced, the generalization capability of the model is improved, the sensitivity and specificity of diagnosis are remarkably improved, the misdiagnosis rate is reduced, the recognition precision of the model is greatly improved, and misdiagnosis and unnecessary biopsy possibilities are reduced; and perception deviation and visual fatigue of an endoscope physician can be relieved.

The invention discloses a chest image diagnosis method and system based on multi-modal sign collection, and relates to the technical field of medical image.The method comprises the steps that a chest image of a patient is obtained, electrocardiosignals and blood oxygen saturation data are synchronously collected, and an associated radiology report is obtained; the image lesion features and the frequency domain rhythm template are combined for processing, motion artifacts are eliminated through a frequency domain decoupling equation, and refined image features are output; inputting the refined image features and the text pathological semantic features into a bidirectional attention mechanism to generate fusion features, and splicing the oxyhemoglobin saturation data and the text pathological semantic features into a sign-text vector; and inputting the fusion feature and the sign-text joint vector into a multi-task loss function, and outputting a structured diagnosis report. According to the method, accurate elimination of motion artifacts is achieved through a frequency domain decoupling equation, and coupling calculation is conducted on an electrocardio rhythm template and image lesion features in a frequency domain space.

The invention provides an artificial intelligence-based lumen lesion feature recognition method and system, and the method comprises the steps: obtaining an original image frame sequence collected by an electronic endoscope and a collection timestamp of each frame, and forming a pseudo-time image sequence set; inputting the pseudo time image sequence set into a pre-constructed candidate region extraction model, and outputting a focus candidate region set; performing vascular structure enhancement on the lesion candidate region set, constructing a graph neural network, extracting structural features through the graph neural network, outputting a lesion confidence score in combination with the region features, and finally generating an enhanced structured candidate region set in combination with the lesion candidate region set, the structural features and the lesion confidence score; performing trajectory aggregation on the structured candidate region set based on structural feature similarity and time continuity to form a cross-frame focus trajectory set; and calculating a space center position and a time range of a focus track according to the cross-frame focus track set, and generating a structured report.

A lesion layer-by-layer classification method for a gastric cancer pathological diagram comprises the following steps: 1) image preprocessing and effective Patch cutting are carried out, and an area containing enough tissue information is extracted to improve a model training effect; 2) cancer pathological features are extracted by using cross-gastric-cancer-level supervised contrast learning, and the confusion influence between adjacent levels of pathological features is reduced; 3) scoring the Patch by using a gating attention mechanism to obtain importance distribution of the key lesion area; 4) constructing a multi-level classification structure of a Patch level based on a cancer severity priority; 5) predicting a pathological picture by using the constructed model; and 6) performing post-processing on a model prediction result to generate a thermodynamic diagram and a visual image of the canceration region to assist doctors in diagnosis. According to the method, canceration regions of different levels can be distinguished more accurately, and the interpretability and robustness of an intelligent pathological diagram diagnosis system in practical application are remarkably improved.

The invention relates to the field of fruit tree disease and insect pest detection, in particular to a fruit tree disease and insect pest detection method and system based on machine vision, and the method comprises the steps: obtaining an image metabolome feature matrix and a preliminary difference pixel based on a preprocessed multispectral image of an original machine vision image obtained by a camera, carrying out the topological skeleton extraction, and carrying out the dimension fusion; outputting a core focus feature set; each discrete feature is used as a network node, a mutual information value between any two discrete features is calculated, and a focus area is obtained; and based on a lesion region containing lesion boundary coordinates, area and morphological parameters, extracting the ROI of the lesion region from the multispectral image, and carrying out disease and pest identification matching to obtain a detection result. According to the method, the essential attributes of the lesion are comprehensively captured by fusing the multi-dimensional features of the spectrum, the texture, the space coordinates and the morphological topology, a multi-dimensional fusion feature system is formed, and the problems that similar pest and disease damage forms are difficult to distinguish, and early lesion features are weak and difficult to recognize are effectively solved.

The invention discloses a knee osteoarthritis dynamic grading prediction and intervention system and method based on a large model, and relates to the field of medical image analysis, and the method comprises the steps: obtaining a knee joint MRI image sequence and knee joint angle time sequence data of a patient; processing the MRI image sequence by using a pre-trained articular cavity segmentation model to obtain a joint fluid volume quantized value; aligning the knee joint angle time sequence to an image frame acquisition time point to generate a synchronous angle sequence; whether the average value of the liquid amount change rate sequence is lower than a preset stable threshold value or not is judged by calculating the liquid amount change rate sequence and the angle change quantity sequence, and if yes, a low-risk signal is output; otherwise, calculating a correlation coefficient between the liquid amount change rate and the angle change amount, outputting a moderate or severe gonitis prediction signal according to the value of the correlation coefficient, and further judging the mild or moderate risk of the gonitis according to the amplitude characteristics of the liquid amount change rate sequence; according to the invention, early gonitis lesion features can be identified, and the accuracy of grading prediction is improved.