174 results about "Lesion feature" patented technology

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 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 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 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 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.

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 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 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 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.

The invention discloses a retinal vessel segmentation and lesion detection method based on incremental learning. The method comprises the specific steps that firstly, an original image of a data set STARE is acquired, and preprocessing such as segmentation and data enhancement is carried out on an original retina image; then, a VGAT-Net-IL network model is constructed, the network takes a coding-decoding symmetric structure as a trunk, a visual cortex mechanism is simulated through an adaptive receptive field module to dynamically adjust a receptive field, and local details and global features of the retinal vessels are cooperatively extracted; meanwhile, a dynamic bimodal attention module is innovatively integrated, variable convolution is introduced into the dynamic bimodal attention module to adaptively adjust a sampling position, a blood vessel region is precisely focused in combination with a space and channel attention mechanism, and after the dynamic bimodal attention module, a Bayesian semantic association module is introduced to generate features containing semantic association; in order to solve the problem that old knowledge is easy to forget when a model learns new lesion features, an incremental learning technology training model is introduced. According to the method, the retinal vessel segmentation precision and the lesion detection capability are improved, and a reliable image analysis basis is provided for retinal disease diagnosis.

The invention discloses a dynamic multi-scale medical image segmentation method based on morphological boundary condition gating, and relates to the field of image processing. The problems that an existing single-scale convolutional neural network is insufficient in performance when coping with diversified lesion features, weight generation of the existing single-scale convolutional neural network only depends on global context statistics, and the influence of a local lesion area, especially boundary information, on branch selection is not fully considered are solved. The method comprises the following steps: acquiring a cerebral hemorrhage CT image, preprocessing the cerebral hemorrhage CT image, performing slicing along a z axis to generate a cross section image, and performing image enhancement on the cross section image; inputting the preprocessed cross section image into a segmentation network; in combination with a dynamic segmentation loss function, different penalty weights are distributed according to the size of a bleeding area, a GAN network is introduced, through joint discrimination of a global discriminator and a local discriminator, consistency constraint is simultaneously carried out on a segmentation result in the aspects of an overall structure and local details, and the method is also suitable for the application fields of medical image segmentation and the like.

The invention provides a pneumonia medical data analysis and processing method, system and device and a storage medium, and belongs to the field of medical image processing. Multi-scale features of the lung CT image are extracted layer by layer through convolution, global pooling operation down-sampling is carried out based on the multi-scale features, and saliency features of lung lobe and lesion areas are captured from local details to global semantics; performing convolution and multi-scale pooling on the saliency features to generate a feature map fusing global and local information; and recovering the feature map through layer-by-layer up-sampling to obtain a segmentation result of the lung lobe and the focus. Quantitative indicators are calculated based on regions and pixels of the segmentation results of the lung lobes and lesions. And performing model processing based on the quantitative index, the segmentation result of the lung lobe and the focus and clinical data of the lung lobe and the patient to obtain pneumonia focus characteristic data. And a doctor can conveniently make an objective and reasonable treatment scheme according to a result of lesion segmentation and feature data calculation based on the pneumonia lesion feature data.

The invention discloses a diabetic retinopathy image classification method based on a multi-feature fusion network model, and the method comprises the steps: S1, obtaining an initial fundus image data set, and constructing a sample training set based on the initial fundus image data set, a first enhanced image data set, and a second enhanced image data set; s2, constructing a retinopathy image classification model, and training the retinopathy image classification model based on the sample training set to obtain a trained retinopathy image classification model; the retinopathy image classification model comprises a first multi-feature fusion enhancement module, a second multi-feature fusion enhancement module, a third multi-feature fusion enhancement module, a fourth multi-feature fusion enhancement module and a classification module; the classification module performs classification based on the input data to obtain a retinopathy image classification result. By designing a plurality of multi-feature fusion enhancement modules and double attention modules, hierarchical fusion of lesion features is realized, the recognition precision of tiny lesions is improved, dynamic calibration of a lesion area feature map is realized, and finally, high-precision and robust DR automatic classification is realized.

The invention provides an early-stage intelligent diagnosis method for rheumatoid arthritis based on a multi-modal medical image and deep learning, and the method comprises the steps: carrying out the image cleaning and standardization, automatic bone joint positioning and segmentation, and quality control of a multi-modal original data set, and obtaining a joint slice library; performing intra-modal self-supervised pre-training and cross-modal alignment representation, and then performing focus level detection and quantification to obtain a lesion feature vector of each joint; performing joint diagram construction and quality perception multi-modal fusion to obtain fusion feature representation, and performing weak supervision multi-instance learning and multi-task loss calculation based on the fusion feature representation to obtain an uncalibrated model; and carrying out model calibration and explainable output to obtain an intelligent diagnosis model. According to the method, the diagnosis time point can be advanced to the reversible inflammation stage, interpretable evidence of patient-level decision and joint-level quantification is provided, the engineering feasibility of small samples and multi-center generalization is considered, and the method has high clinical transformation potential.

The invention relates to the technical field of electric digital data processing, in particular to an ophthalmology department clinical nursing data preprocessing method and system. The method comprises the following steps: realizing stage clustering of pathological images through a K-Medoids algorithm based on predefined typical images; carrying out deformable image registration on the images in the same stage, and extracting key focus features through group difference analysis and structural pattern recognition; generating a weighted mask by using the lesion features, and realizing local enhancement processing of the original image; further extracting non-image modal data corresponding to the image and carrying out structured coding; and finally, constructing a time axis taking the image acquisition time as a reference, and aligning and fusing the image modality and the non-image modality to generate multi-modality comprehensive information. The system supports co-processing and feature enhancement of multi-source data, provides more accurate input for subsequent intelligent analysis and nursing intervention, and improves clinical aid decision-making efficiency and reliability.

The invention provides a dual-order optimization self-adaptive diabetic mesh screening model generation method and lesion recognition equipment, and relates to the technical field of diabetic mesh screening, and the method comprises the following specific steps: collecting a plurality of fundus images and corresponding medical record data, carrying out fine processing and detailed labeling, presetting a machine learning detection model for training, and carrying out the recognition of the fundus images and the corresponding medical record data. The fundus image and the medical record data of the samples in the training set are used as input features, the corresponding label content is used as an output label, and dual-order optimization and self-adaptive adjustment are adopted to enhance the recognition capability of the model on lesion features; screening a model according to a category consistency coefficient between lesion types, a feature deviation coefficient between lesion features and a logic consistency coefficient of logic rules, and screening a dual-order optimization adaptive sugar mesh screening model by using samples in a test set. Clinical diagnosis can be accurately assisted, the missed diagnosis and misdiagnosis rate is effectively reduced, the problem that the feature recognition precision of a traditional model is insufficient is solved, it can be ensured that prediction conforms to clinical logic, and the reliability of the model is improved.

The invention relates to the technical field of oral cavity digital image processing, and provides an implant perimucosa red and swollen area detection method based on deep learning, and the method comprises the steps: S1, collecting three-dimensional model data of an implant site of a patient, and exporting a standardized visual angle rendering screenshot with a visual enhancement effect by using a matched software rendering function; s2, constructing a deep convolutional neural network based on a YOLOv8 architecture, training a model by adopting a transfer learning strategy, and realizing automatic extraction of implant perimucosa red and swollen focus features; s3, through a feature fusion module in the deep convolutional neural network, automatically retrieving suspected red and swollen sites on the feature maps with different resolutions, performing coordinate correction on the candidate region, and generating an accurate detection frame; and S4, automatically executing batch prediction on the test set based on the trained model, and outputting a detection frame and a quantitative index. And automatic identification and spatial positioning of the red and swollen mucosa area around the implant are realized, so that a visual basis is provided for clinical precise probing and diagnosis and remote early warning.

The application provides an ophthalmic image diagnosis method and system based on multi-modal imaging cooperation, and relates to the technical field of medical image diagnosis. First, the OCT, fundus camera and ultrasonic original image data of an ophthalmic examination object are acquired, and multi-modal dynamic correlation mapping results are obtained through dynamic correlation and trend correlation processing. Cross-modal lesion feature progressive mining and interactive verification are performed to obtain a cross-modal lesion correlation feature set. Multi-modal cooperative diagnosis reasoning and weight feedback optimization are used to generate an ophthalmic disease reasoning result containing disease types, lesion dynamic distribution and reasoning confidence. Finally, an ophthalmic diagnosis report with dynamic labeling and confidence explanation is generated. The application comprehensively utilizes the advantages of various image technologies to improve the accuracy of ophthalmic image diagnosis.

The invention relates to the technical field of medical image analysis, in particular to a dynamic DR digestive tract radiography automatic tracking and focus marking system which comprises a dynamic DR imaging module, an image real-time processing module, a contrast agent motion tracking module and a focus intelligent marking module. A deep learning algorithm is utilized to segment a contrast agent flowing area in real time and construct a dynamic three-dimensional model of a digestive tract, a contrast agent movement track is analyzed and tracked in combination with time-space domain features, and meanwhile, based on morphological anomaly detection and comparison with a focus feature library, focus positions such as stenosis, ulcers and space-occupying lesions are automatically marked; the problems of image blurring and tracking offset caused by alimentary canal peristalsis are solved by adopting a self-adaptive registration optimization algorithm of dynamic DR images and motion characteristics of alimentary canal contrast agents; by means of the method, the problems that traditional digestive tract radiography depends on manual interpretation of dynamic DR images, the focus tracking efficiency is low, and marking is prone to omission are solved.

The present application relates to a kind of breast lesion three-dimensional reconstruction methods, comprising the following steps: step S1, breast region self-rough scanning is carried out, and the depth image of breast region is acquired, and the three-dimensional model of breast and spatial position are determined;Step S2, fine scanning is carried out to lesion, and AI algorithm is used to realize the segmentation extraction of lesion feature in ultrasonic image;Step S3, with time, ultrasonic rotation angle or ultrasonic moving distance as interval, the image and position information of lesion feature during fine scanning are sequentially recorded;Step S4, the three-dimensional reconstruction of lesion is realized by three-dimensional reconstruction algorithm based on lesion boundary point cloud feature;Step S5, according to the recorded center position of lesion feature, the three-dimensional spatial position of lesion is calculated by weighted average method;Step S6, by breast center coordinates and lesion center coordinates, the relative position relationship of both based on mechanical arm coordinates is determined, and breast lesion three-dimensional reconstruction method is realized.The method can help doctor to determine the shape, size and its position in breast of lesion.

The present application relates to the technical field of lesion recognition, in particular to an automatic lesion recognition ultrasonic system for real-time monitoring, which comprises a delay path discrimination module, a gray level trend detection module, a texture feature screening module, a feature fusion sorting module and a region highlight labeling module. Based on continuous ultrasonic frames, the collected deep tissue echo path is analyzed, and the echo arrival time of each pixel point in the continuous frame is detected. The present application supports multi-type data fusion judgment through a comprehensive judgment process supported by multi-dimensional parameter collaborative screening, penetration behavior, gray level trend and texture aggregation. The regional abnormal priority sorting mode improves the hierarchy of lesion feature discrimination, provides partition directional recognition for local structural abnormalities and early micro-variation, and converts the feature judgment result into a high confidence region label by a weight aggregation method. The image output process automatically completes the real-time visual presentation of the high-risk area, improving the clarity and pertinence of the lesion region presentation.

The invention belongs to the technical field of medical image analysis, and provides a multi-feature fusion endoscope image quality online evaluation method and system. The method comprises the following steps: extracting stable anatomical regions such as a gastric horn through a U-Net semantic segmentation model, completing image confidence analysis and judging a trigger confidence signal; if triggering, performing gastral cavity inflation state analysis to obtain an image stretching coefficient; positioning a target focus, and extracting morphological regularity and texture fluctuation values to construct a current morphological parameter set; and performing deformation attribution analysis to obtain a form and texture residual error value, constructing an inflation-deformation mapping model after triggering a mapping signal, and dynamically correcting a current parameter to obtain a standard form parameter set. The system comprises a credible analysis module, a state recognition module, a two-dimensional analysis module, a mapping judgment module and a model construction module. Interference of the inflation state on image lesion features is eliminated, and image quality and lesion feature accuracy are improved.

The present application relates to the technical field of medical image processing, and particularly relates to a combined narrowband and fluorescence imaging method and system for bronchoscopy lesion detection. The method provided by the present application realizes selection and extraction of features with discriminative ability in narrowband imaging images and autofluorescence imaging images based on deep learning, and realizes fusion of advantages of different imaging technologies for lesion detection through decision fusion, thereby improving the speed and accuracy of lesion recognition. Meanwhile, the method provided by the present application can better adapt to different scenes and lesion characteristics, has better generalization ability, improves the specificity and sensitivity of lesion detection, and is beneficial to improving the diagnostic accuracy and efficiency.

The invention relates to the technical field of image classification, and discloses a myocardial ring slice prediction method and system, a terminal and a storage medium, and the method comprises the steps: carrying out the global average pooling processing of a myocardial ring slice through an extrusion-excitation model, outputting a channel scalar value, and carrying out the channel-feature matching, and outputting a channel-feature weight; performing enhancement or suppression processing on the channel-feature weight according to the feature type of the myocardial ring slice, and then performing feature fusion to obtain a feature fusion image; and performing enhancement processing on the feature fusion image to obtain an enhanced data set, and obtaining a prediction classification result of the target object through a 3D residual network. Based on the single data of the heart image, the extrusion-excitation model adapts to the weights of different feature channels, the ability of capturing the complicated three-dimensional structure and lesion features of the cardiac muscle is improved in combination with the optimized 3D residual network, and efficient feature extraction and accurate image classification are achieved.