377 results about "Segmentation system" patented technology

The invention provides a remote sensing image building semantic segmentation system based on a visual language model. The remote sensing image building semantic segmentation system comprises an initial building mask generation module and a pseudo label optimization and model iteration enhancement module. The initial building mask generation module comprises a query text set construction unit, a feature extraction unit, a cross-modal attention interaction unit, an initial building instance generation unit and an instance fusion and mask generation unit; and the pseudo-label optimization and model iteration enhancement module comprises a pseudo-label screening unit, a model iteration training unit and a dynamic parameter adjustment unit. According to the method, the quality of the building segmentation pseudo tag generated by the VLMs can be improved, the performance of a remote sensing image weak supervision semantic segmentation algorithm is improved, the problem that the building tag generated by the VLMs pre-trained by the natural scene image cannot be directly applied to the remote sensing image is solved, and the dependence of the remote sensing image building semantic segmentation on manual annotation is reduced.

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

The invention discloses a field unstructured road real-time segmentation system and method for agricultural machinery autonomous navigation, particularly relates to the field of agricultural machinery autonomous navigation, and is used for solving the problems of high precision and low delay of real-time segmentation of a driving area and an obstacle in a field unstructured road environment. The image stability under complex illumination is improved through brightness balance, and texture and contour features are extracted through lightweight multi-scale coding; texture deflection and contour focusing are normalized into probability distribution, direction consistency is analyzed based on modal competition fusion, direction embedding is dynamically adjusted, and deviation is avoided; guiding branch activation of a BiSeNet decoder by the direction perception tensor, and outputting a driving area probability graph and an edge heat graph; and continuous frame consistency filtering is used for eliminating instantaneous fluctuation, a stable segmentation mask and a boundary position vector are generated, and the path stability and operation continuity of navigation control are ensured.

The invention discloses a medical image fuzzy boundary segmentation system and method based on COD frequency domain guidance. In order to solve the problem of boundary fuzzy segmentation of a medical image under a complex background, frequency domain feature processing and an improved camouflage target detection (COD) algorithm are innovatively combined, and a progressive boundary optimization segmentation framework FEA-Net is designed. According to the framework, four key modules are designed: a semantic edge enhancement module (SEEM) realizes boundary preliminary positioning through space-channel self-attention; on the basis of SEEM, a frequency domain feature guide enhancement module (FTEM) further improves boundary prediction precision by using Fourier transform and adaptive frequency selection; an adaptive edge feature module (AEFM) highlights key boundary features; a Context Attention Aggregation Module (CAAM) implements feature fusion. Experiments show that evaluation indexes of the method on data sets of Kvasair-SEG, ISIC2018 and KPIs2024 are remarkably improved, particularly, obvious advantages are shown when boundary fuzzy areas are processed, and reliable support is provided for accurate segmentation of medical images.

The invention relates to the technical field of beef carcass segmentation, in particular to a beef carcass computer vision-assisted segmentation system, which comprises a vision acquisition and preprocessing module, a feature extraction module, a segmentation decision module, a dynamic target tracking module, an execution control module and a temperature monitoring and feedback module, the visual acquisition and preprocessing module is used for acquiring image data of beef carcasses; the feature extraction module extracts feature points and edge information; the segmentation decision module generates a segmentation path; the dynamic target tracking module corrects the generated segmentation path; the execution control module is used for cutting beef carcasses; and the temperature monitoring and feedback module monitors the temperature of the cutting equipment and the carcass cutting part in real time. According to the invention, the error and the labor intensity of manual operation are reduced, and the automation level and the overall benefit of the beef carcass segmentation process are improved.

The invention discloses a brain lesion nuclear magnetic image Transform diffusion segmentation method and a brain lesion nuclear magnetic image Transform diffusion segmentation system based on symmetry prior. The invention provides a novel prior-guided diffusion denoising brain lesion segmentation Transform model, long-distance coding features are extracted from an image, local information related to medical image prior features is supplemented, and frequency domain screening fusion coding components are further introduced. A U-shaped denoising model is designed based on a transform network structure, and in order to maintain accurate lesion-related semantic details, a prior feature coding branch is introduced. A priori feature coding branch considers modal-related voxel distribution characteristics and spatial symmetry in a modal, so that a priori guidance attention mask is learned, and attention weights of a pixel level and a patch level are dynamically adjusted. In order to eliminate image noise and a low contrast effect, in a decoding part, a three-branch wavelet frequency spectrum mixed attention module is designed for fusing and screening frequency spectrum feature embedding, and a stable segmentation result is further generated.

The invention discloses a remote sensing video segmentation method and segmentation system based on text guidance, belongs to the crossing field of remote sensing image processing and computer vision, and relates to a remote sensing video segmentation method and segmentation system. The invention aims to solve the problems that the existing remote sensing video segmentation technology is poor in flexibility, cannot interact with natural languages, is insufficient in generalization ability for new categories or complex targets, and cannot meet the requirement of quickly and accurately extracting semantic information in a dynamic remote sensing scene. The method comprises the following steps: 1, acquiring a video frame sequence, and acquiring a key frame based on the video frame sequence; 2, obtaining an initial segmentation mask; 3, obtaining an optimized mask; 4, calculating a minimum bounding rectangle of the optimized mask, obtaining a bounding box of the minimum bounding rectangle, and obtaining an expanded bounding box; and 5, inputting the expanded bounding box and the video frame sequence obtained in the step 1 into an improved SAM2 video segmentation model, and outputting a frame-by-frame segmentation result of the region of interest by the improved SAM2 video segmentation model.

The invention relates to the technical field of image processing, in particular to a clinical focus auxiliary segmentation system based on a nuclear magnetic resonance image. The system comprises an MRI image processing module, a lesion auxiliary segmentation module, a probability segmentation correction module and a lesion boundary smoothing module, a corresponding clinical nuclear magnetic resonance image set of a patient can be obtained, image position alignment and gray level adjustment processing can be carried out, and meanwhile a corresponding clinical image lesion segmentation model is constructed to carry out multi-scale fusion auxiliary segmentation. Generating a clinical focus region segmentation fusion image; obtaining a focus confidence probability corresponding to each pixel point in the segmentation image through the clinical focus region segmentation fusion image, and carrying out probability segmentation boundary correction on the clinical focus region segmentation fusion image to obtain a clinical focus region segmentation correction result image; and performing focus edge shape smoothing processing on the clinical focus region segmentation correction result map to generate a clinical focus edge shape segmentation optimization result. According to the invention, high-precision segmentation of the focus in the MRI image can be realized.

The invention discloses a weak supervision image semantic segmentation system and method based on an attention mechanism, and the method comprises the following steps: obtaining to-be-segmented image data and an image-level label, and extracting a multi-level semantic feature map; fusing the multi-level feature map with the space and the channel dimension to generate an attention feature map; combining the attention feature map with an image-level label to obtain a target area positioning map; inputting the attention feature map and the target area positioning map into a double-edge reconstruction network to generate a high-quality pseudo-label map; carrying out progressive decoding structure convolution on the pseudo label graph and the multi-level semantic feature graph to generate a segmentation prediction graph; constructing a supervision signal based on the segmentation prediction map and the pseudo-label map to obtain a supervision training result; dynamically updating the supervised training result to generate an updated pseudo-label graph; and continuously iterating based on the updated pseudo-label graph until the loss function is converged, and outputting an image semantic segmentation result. According to the invention, weak supervision image semantic segmentation based on the attention mechanism is realized.

Embodiments are disclosed for performing universal segmentation to mask objects across multiple frames of a video. The method may include determining an image segmentation mask which masks an object of a frame of a video sequence using the frame and an image segmentation module of a segmentation system. The method further includes determining a mask propagation mask which masks the object of the frame of the video sequence using the frame, a representation of a previous frame of the video sequence, and a mask propagation module of the segmentation system. The method further includes determining a frame mask which masks the object of the frame of the video sequence based on a comparison of the image segmentation mask and the mask propagation mask. The method further includes displaying the frame mask of the video sequence.

The invention provides an urban landscape semantic segmentation system adopting an EGLiteSeg model, and belongs to the field of image processing. Comprising three main parts: an encoder; an polymerizer; and a decoder. The EGLiteSeg framework adopts a lightweight encoder-decoder pipeline to carry out image processing, and an encoder of the EGLiteSeg framework is provided with a five-level DeSTDCNet framework. In the first four stages, depth separable convolution with the stride being 2 is utilized, and the AECA module automatically adjusts the size of a kernel according to the number of input channels so as to enhance the multi-scale feature adaptability. For context aggregation, the framework uses GDSPPM. The module processes features through deep convolution to enhance distinguishability, and then performs multi-scale pyramid pooling to capture hierarchical contextual information. The multi-scale features are connected together and fed into a decoder. The decoder is composed of two key components: a universal attention fusion module (UAFM) and a partition head. The method not only has high semantic segmentation accuracy, but also has superiority in real-time performance, and meets actual requirements.

The invention discloses a zero sample object instance segmentation system and method in a robot environment, and belongs to the technical field of robot visual perception, and the method comprises the steps: firstly carrying out the Vibriis color mapping preprocessing of a depth image, and then generating an initial object mask candidate through an SAM (Segment Anything Model) model; meanwhile, a pre-trained Vision Transform is used as a feature description model of a scene to process the image, an attention map of the last layer is extracted, and a feature weighting mechanism is constructed based on the information entropy of the attention map; a similarity matrix of the image blocks and the background blocks is calculated to remove non-object masks, a K-Medoids clustering algorithm is adopted for each remaining object mask candidate to obtain representative sampling points, and finally the points serve as prompt information to be input into an SAM model to obtain an accurate object instance segmentation result. According to the method, the zero sample generalization ability of the visual basic model is fully utilized, accurate segmentation of the unseen object can be realized without additional training, and the method has good universality and practical value.

The invention discloses a diffuse large B-cell lymphoma segmentation system based on deep learning, and the system comprises a data collection module which is used for collecting PET and CT images of a patient; the double-branch encoder module is used for processing PET and CT images respectively; the cross-modal feature cross fusion module is used for extracting positioning clues from the CT anatomical features by taking the PET metabolic features as queries; taking CT structure characteristics as query, and screening PET high-metabolism regions; the global-attention suppression module is used for extracting global context features, performing down-sampling, remodeling input features into a low-dimensional matrix, then performing self-attention calculation, generating a sparse weight matrix for the input features based on a full connection layer, and then performing sparse attention calculation; and finally, fusing the two attention calculation results and outputting enhanced features. And the multi-scale jump connection module is used for fusing the encoder features and the decoder features and recovering spatial details.

The present disclosure provides a computer-implemented method and system for granular level segmentation of users based on online activities on a webpage in real-time. The computer-implemented method and system corresponds to a user segmentation system. The user segmentation system receives a first set of data associated with a plurality of users. The user segmentation system fetches a second set of data. The user segmentation system obtains a third set of data. The user segmentation system analyzes the first set of data, the second set of data and the third set of data using one or more machine learning algorithms. The user segmentation system creates one or more segments based on analysis performed on the first set of data, the second set of data and the third set of data. The user segmentation system initiates one or more marketing campaigns for the one or more segments.

The invention discloses a frame supervision video instance segmentation system based on time sequence feature fusion, and the system comprises a feature extraction network which is used for extracting features # imgabs0 # and # imgabs1 # which cover time-space similarity information from a current frame picture and an adjacent frame picture of a video, t represents a current moment, and delta represents a time interval between two adjacent frame pictures; and the space-time prediction self-attention network is used for generating a memory feature Mt + delta according to the features # imgabs2 # and # imgabs3 #, and performing weighted fusion on the memory feature Mt + delta and the feature # imgabs4 # to obtain a fusion feature # imgabs5 # instance mask prediction network, and the space-time prediction self-attention network is used for performing instance mask prediction according to the feature # imgabs6 # and the fusion feature # imgabs7 #. An IFIM module is introduced into a feature extraction network, and a channel screening module and a space-time prediction self-attention module are combined, so that the video instance segmentation precision is effectively improved.

The invention relates to the technical field of image segmentation, and discloses a street view image semantic segmentation system for a complex city scene, and the system comprises a double-edge energy ratio calculation module which obtains a double-edge energy ratio through calculation; the priori mask generation module is used for generating a priori mask; the segmentation network construction module is used for outputting logarithmic probabilities and probabilities of various categories through a trunk segmentation network; the total loss construction module is used for constructing total loss; the mutual suppression optimization module is used for calculating to obtain a preliminary semantic segmentation result; and the semantic segmentation output module is used for calculating to obtain a final semantic segmentation result. According to the method, a unified process of double-edge energy ratio driving direction convolution, separation loss and reasoning period mutual suppression is adopted, repeated boundaries and false objects caused by reflection and transmission mixing in a complex city scene containing glass and a water surface are reduced, pixel-level boundary positions are stabilized, and road space, building facades and traffic element division are consistent.

Systems, apparatus, articles of manufacture, and methods are disclosed to process images using segmentation. An example apparatus includes interface circuitry, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to generate a scaled frame from an input video frame, segment, with a neural network, the scaled frame to generate a scaled segmentation map based on the scaled frame, the scaled segmentation map to associate pixels of the scaled frame with ones of a plurality of segments in the scaled frame, and generate an output video frame based on the input video frame and an upscaled version of the scaled segmentation map.

Systems, apparatuses, and methods are described for segmenting a video content item (e.g., movie, TV-show) into a collection of scenes. Video frames may be grouped into shots, and visual relationships between image portions within each shot are identified by a self-attention model. The output may be further processed by a gated state space model to identify visual relationships between features in different shots. Multiple instances of the self-attention model and the gated state space model may be used to focus on different aspects of the video content item, for finding the relationships. An aggregated output may be provided to a prediction model and processed by the prediction model to determine scene boundaries. The determined scene boundaries or segmented scenes may be used for various user applications such as ad insertion, chapter selection, content searching, browsing, etc.

The invention discloses a collaborative data model adaptation-based adaptive medical image segmentation method during testing, and aims to solve the problem that the image segmentation performance of an existing segmentation method needs to be improved. According to the technical scheme, a collaborative data model adaptation-based self-adaptive medical image segmentation system during testing is firstly constructed, a prompt update model is arranged in a prompt update module of the segmentation system, an image segmentation module is composed of image segmentation models, and batch normalization layers are arranged in the two models; a source domain model and a medical image are adopted to test a segmentation system, a batch normalization layer is used as a bidirectional bridge to realize collaborative self-adaption of data adaption and model adaption, and low-level distribution alignment and high-level semantic feature adaption are realized in a Fourier space; and segmenting the medical image by using the adapted segmentation system. By adopting the method, error accumulation and disastrous forgetting can be avoided, data adaptation and model adaptation are coordinated, the image segmentation effect can be improved, and the Dice coefficient is improved.

The invention relates to the technical field of medical image processing, and particularly discloses an intelligent segmentation system for a kidney tumor in a CT image, and the system comprises an image preprocessing module, an ROI automatic detection module, a kidney tumor segmentation module, a segmentation result post-processing module, a visual interaction module, and a model updating module. The system improves image quality through normalization and filtering, uses a target detection network to position a kidney area, realizes precise tumor segmentation based on an integrated channel attention mechanism and a U-Net network guided by structure priori, optimizes a boundary in combination with a conditional random field, adopts a multi-plane fusion strategy to improve three-dimensional consistency, and calculates tumor volume. The system supports federal learning update, improves the cross-mechanism generalization ability, has the advantages of high segmentation precision, strong boundary reducibility, good structural rationality, strong clinical deployment and the like, and is suitable for kidney tumor auxiliary diagnosis and quantitative analysis.

The invention belongs to the technical field of image recognition, and discloses a farmland remote sensing image intelligent segmentation system and method based on dynamic feature learning, and the system carries out the automatic extraction of farmland features based on visible light, multispectrum, synthetic aperture radar (SAR) image data and historical segmentation results. High-precision geometric adaptive segmentation of farmland boundaries is realized through multispectral band recombination, multi-temporal feature fusion, dynamic deformable convolutional network operation and the like. And a standardized farmland distribution diagram and a standardized statistical report containing factors such as field area statistics, crop type distribution and a boundary topological relation are dynamically generated according to a farmland drawing specification and a preset agricultural thematic standard, so that a whole-process intelligent technical support is provided for businesses such as farmland resource supervision, agricultural subsidy accounting and planting structure analysis.

The invention discloses a camouflage target segmentation system based on multi-scale boundary fusion. The camouflage target segmentation system comprises a data division module for acquiring a training data set; the feature extraction module performs feature extraction based on a backbone network to obtain multi-level hierarchical feature information; the edge priori module combines the low-level features and the high-level features, performs enhancement by using a parameter-free attention mechanism, and obtains camouflage target edge information; the multi-scale edge enhancement module extracts multi-scale edge feature information by using global average pooling processing, and obtains output features in combination with camouflage target edge information; the multi-scale detail fusion module uses a Hadamard product to be combined with the output features to obtain enhanced information; and the up-sampling module combines the enhanced information with low-level features based on PFNet, and takes a prediction result of the last-level focusing module as a final segmentation result. According to the method, the defects of an existing camouflage target detection technology in the aspects of boundary detail capture and multi-scale feature fusion are overcome, and the precision and robustness of camouflage target detection are remarkably improved.

Systems, methods and computer readable storage mediums are provided. Data comprising raw images and their corresponding pixel-level semantic labels can be obtained. The data can be preprocessed to remove noisy samples or to augment the data with the technique of data augmentation. The data can be split into training and validation sets. A model and loss functions can be designed so first and second level semantic information is leveraged to boost first-level semantic segmentation. The model can comprise main and auxiliary branches. The first level semantic information can be utilized in the main branch and the second level semantic information can be utilized by the auxiliary branch. The model can be trained by the training set to iteratively minimize the loss functions. Model parameters can be updated as the loss function is minimized. The auxiliary branch can be removed after the model is trained. Results can be predicted using the trained model on samples in the validation set. Predicting results can be configured to generate pixel-level semantic segmentation labels.

This application relates to the field of artificial intelligence technology and discloses a semantic segmentation system and method for satellite remote sensing images. The system includes: an encoding module configured to extract multi-scale features of the target image; a feature fusion module configured to fuse the original feature maps at various scales through linear attention calculation to obtain a first fused feature, and then split it to generate multiple mixed feature maps with the same scale as the original feature maps; a collaborative decoding module configured to extract spatial domain features and frequency domain features from the original feature maps and the mixed feature maps of the same scale, and perform cross-attention calculation to iteratively fuse spatial domain information and frequency domain information to generate a first tensor; and an output module configured to process the first tensor to obtain the segmentation result of the target image. This system can improve the recognition accuracy of detailed textures and small targets in remote sensing images, and improve the accuracy of semantic segmentation of remote sensing images.

The invention discloses a post-earthquake building segmentation system and method based on an MDDFAN-Net multi-scale dynamic fusion network, relates to a remote sensing image processing technology, and solves the technical problems that the multi-scale feature extraction capability is insufficient, the feature fusion capability is weak, the boundary quantization effect is poor, and the background has great influence on target segmentation. The system comprises an input module, a segmentation module and an output module, the input module is used for processing a to-be-segmented post-earthquake satellite remote sensing image and then inputting the processed post-earthquake satellite remote sensing image into the trained segmentation module, an MDDFAN-Net network is arranged in the segmentation module, the segmentation module carries out building damage segmentation according to the input data, and the output module outputs the segmented post-earthquake satellite remote sensing image. The output module is used for outputting the segmentation effect for checking; according to the method, global and local features are extracted at the same time through double branches, fragile edge local information of the building is extracted through curved convolution, the global features are extracted through standard convolution branches, and complex and multi-scale dynamic building edges are better captured.

The invention relates to the technical field of digital image processing, in particular to a real-time video semantic segmentation system and optimization method fusing an edge size model, and the system comprises an edge end lightweight model belonging to terminal equipment, an edge server large model belonging to an edge server, a dynamic frame distribution module and a self-adaptive cache module. An optical flow change rate and scene complexity are analyzed through edge detection and entropy calculation by a dynamic frame distribution module, then a dynamic distribution strategy is output, the dynamic distribution strategy is formed by processing a low-change frame by a small model and processing a key frame by a large model, a high-confidence segmentation result of the large model is cached by a self-adaptive cache module, and a high-confidence segmentation result of the large model is obtained. The method can be used for reuse of small models in similar scenes so as to reduce calling frequency of large models, flexibly deploy resources of the large models and the small models during operation and solve the real-time performance-precision contradiction in edge computing scenes, and compared with a single model scheme, the method has the advantages that precision is improved, and load is reduced.

The invention discloses a rapid semantic segmentation system and method for a pneumonia DR image. The rapid semantic segmentation system comprises a context graph construction module, a context information aggregation module, a detail and context information fusion module and a decoding fusion feature segmentation module. According to the method, when a to-be-segmented DR image is processed, a context information aggregation module gradually collects and integrates long-distance context features and detail features outside an image block based on a context graph neural network model of an end-to-end training mode; then, based on a detail and context fusion module, effectively fusing context information and detail features of the image blocks; and the fusion feature is segmented by a decoding fusion feature segmentation module so as to generate an accurate segmentation mask. According to the method, accurate segmentation and rapid evaluation of the pneumonia focus in the DR image are realized, and powerful assistance and support are provided for diagnosis of doctors.

The invention discloses a global and local feature fusion-based semi-supervised video target segmentation method and a global and local feature fusion-based semi-supervised video target segmentation system. According to the implementation scheme, the method comprises the following steps: firstly, acquiring required data sets including image and video data sets and corresponding segmentation labels, expanding the image data set, and sampling the video data set to obtain a training data set for training a segmentation model; and secondly, designing a segmentation model, constructing a loss function to train the segmentation model, and finally performing video target segmentation by using the trained segmentation model. According to the invention, a local-global branch fusion module is designed to carry out global context modeling and local detail enhancement on features used for non-local matching, so that the distinguishing capability of the model for distinguishing similar features is improved; therefore, the network can effectively solve the problem of wrong feature matching caused by similar interferents in a semi-supervised video target segmentation task. On this basis, a target scale self-adaptive down-sampling layer is further combined, and information of the same target under different scales is captured through the layer, so that the self-attention calculation complexity is reduced while the feature distinguishability is improved, and the balance between the model segmentation performance and the reasoning prediction efficiency is realized.

The invention belongs to the technical field of image analysis, and particularly relates to a railway track image abnormal region segmentation system combined with fractal dimension feature extraction, which comprises a preprocessing unit, a multi-scale fractal feature processing unit, an abnormal region processing unit and a segmentation unit, the preprocessing unit is used for suppressing noise, retaining a main structure and outputting a denoising result; the multi-scale fractal feature processing unit is used for obtaining a fused fractal dimension based on a denoising result, and forming a feature vector by the fused fractal dimension and a denoising result gradient; the abnormal region processing unit is used for obtaining processed candidate abnormal regions by using a spectral clustering method; and the segmentation unit is used for performing intersection operation on the processed candidate abnormal region and a track mask to obtain a track abnormal region. According to the invention, high-precision segmentation of the abnormal region in the track image is realized.

The invention discloses a video polyp segmentation system and method based on a self-supervised task diffusion model, and the system comprises a processing module which is used for obtaining a polyp video, carrying out the data set labeling of the polyp video, and obtaining N polyp image frames and a segmentation mask corresponding to each polyp image frame; the conditional diffusion model is used for carrying out coding and noise processing on the segmentation mask corresponding to the last polyp image frame to obtain a noise segmentation mask; the multi-task diffusion model is used for generating multi-scale spatial features and multi-scale temporal features, and integrating and inputting the multi-scale spatial features and the multi-scale temporal features to the feature pyramid network to obtain multi-level spatial-temporal features; the MLP layer is used for splicing and fusing the multi-level spatial-temporal features and the noise segmentation masks to obtain fused features; and the de-noising decoder is used for carrying out reverse de-noising processing on the fusion features, outputting a prediction segmentation mask and determining a prediction segmentation result according to the prediction segmentation mask. According to the application, the accuracy of polyp diagnosis and polyp segmentation is improved.