234 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 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.

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 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 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.

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.

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 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 provides a YOLO-based aviation product assembly component instance segmentation system, which is characterized in that an improved SAC-YOLO model based on a YOLOv11 algorithm is used, an MSECA module is connected between an SPPF module and a C2PSA module in a backbone network of the SAC-YOLO model, and convolution in a C3k2 module is WTConv convolution; the convolution in the C3k2 module in the neck network is a DSConv convolution; therefore, the SAC-YOLO adopts the technologies of multi-scale feature fusion, special-shaped convolution, attention mechanism and the like, so that the detail features of the complex structure in the assembly scene can be more effectively captured; when the trained segmentation model is used for processing assembly elements with large form difference and slender and tortuous non-rigid structures such as a locking piece and the like observed from the front face and the side face, high segmentation precision can be kept; that is to say, the positioning precision of identifying the assembly component and the locking point is higher, the morphological characteristics of the assembly component and the locking point can be obtained, and the identification and segmentation of the components such as the parts and the locking point of the aviation assembly can be completed.

The invention discloses a pig organ automatic segmentation method and segmentation system based on a CT anatomical structure relationship. The method comprises the following steps: acquiring a whole-body CT scanning image of a live pig; and processing the pig whole body CT scanning image by using a pig multi-organ automatic segmentation model to obtain a pig multi-organ prediction mask, and completing automatic segmentation of the multiple organs of the pig. The live pig multi-organ automatic segmentation system comprises a live pig CT image acquisition module and a multi-organ automatic segmentation module, wherein the live pig CT image acquisition module is used for acquiring a to-be-segmented live pig CT image; the multi-organ automatic segmentation module comprises a visual state space block-based encoder-decoder architecture, a spatial link GRU module, a global organ category encoding module, a global organ category guiding module and a semi-supervised training framework. According to the method, multiple organs in the CT image of the live pig can be segmented efficiently and accurately, the problems of low segmentation speed and low precision of a traditional method are solved, and a more efficient solution is provided for medical image analysis and animal husbandry management of the pig.

The invention relates to a video full-object segmentation system and method based on 3D prior enhancement and a double-branch structure, and the system comprises a visible region segmentation branch and a full-object region segmentation branch which are connected to an image encoder and a prompt encoder. The visible region segmentation branch comprises a first pixel perception memory module, a first mask decoder, a first memory bank and a first memory encoder; the whole object area segmentation branch comprises a three-dimensional space perception memory module, a second pixel perception memory module, a third pixel perception memory module, a second mask decoder, a second memory bank and a second memory encoder. Compared with the prior art, the method has the advantages that powerful priori knowledge of the basic segmentation model is efficiently migrated, the complete region segmentation capability of the target object in the video is greatly improved, the perception capability of the object shielding region can be improved, the segmentation effect of the shielding region is improved, the problem of insufficient early frame memory is greatly improved, and the overall segmentation effect is improved.

The invention provides a ship unloader grab bucket system and method based on multi-modal perception and artificial intelligence semantic segmentation. According to the system, point cloud data and image data are synchronously collected through a laser radar and an RGB camera, feature level alignment and fusion are conducted through an image fusion module, an artificial intelligence semantic segmentation network is combined with attention gating to restrain dust interference, grab bucket boundary information is output, and a position and posture estimation module solves the grab bucket position and posture based on the boundary information and geometric constraints. The path planning module combines reinforcement learning and model prediction control to generate a trajectory instruction, and the compensation module implements hierarchical correction according to the pose deviation and drives an execution device to realize high-precision positioning and stable control under severe working conditions.

This disclosure provides methods, devices, and systems for planning and performing medical procedures. The present implementations more specifically relate to analyzing objects in 3D images. In some aspects, a segmentation system may receive image data representing a 3D image of an anatomy, select a seed location for a target in the 3D image, and infer a segmentation mask associated with the seed location from at least a portion of the received image data based on a neural network model trained to segment a class of objects associated with the target. The system further extracts a polygon mesh from the segmentation mask to produce a 3D model of the target. The system can determine a spatial relationship between an instrument and the target based on a position of the 3D model relative to the 3D image. The system can also estimate a geometry of the target based on the 3D model.

The invention discloses a target segmentation system, method and device based on an attention mechanism. The system comprises an image acquisition device for acquiring a CT scanning image to be segmented; the image processing equipment is used for segmenting an organ abnormal region by adopting an image segmentation model based on an attention mechanism; the image display equipment is used for displaying the CT scanning image and the mark information; the image segmentation model comprises an image enhancement network, which is used for enhancing boundary high-frequency information and texture details of input by using discrete cosine transform (DCT); the main encoder is used for extracting multi-scale global features and deep global semantic information of the CT scanning image; according to the brain area network, local differentiation features are extracted for the focus through a dynamic adaptation strategy by adopting a partition cooperation mechanism; and the decoder is used for recovering space structure and boundary details by adopting a bilinear interpolation up-sampling and multi-scale feature fusion mechanism on the global features and the local differentiation features. The technical problem that the accuracy of image segmentation of lesions such as lungs is poor in correlation is solved.

A plain-scan CT image aortic valve calcification segmentation system based on space-time priori comprises a position priori information extraction module, an encoding module and a decoding module, after an aortic image is automatically segmented in an off-line stage to obtain a segmentation mask, attention weights are generated through preprocessing and encoding, and the aortic valve calcification is subjected to image segmentation; and inputting the aorta image serving as a training set into a segmentation model comprising a position prior information extraction module, a coding module and a decoding module, and performing real-time image segmentation through the trained segmentation model in an online stage. According to the method, calcification point features of different sizes and forms are captured through multi-scale prediction; the aorta segmentation prior is introduced, the sensitivity and positioning precision of the model to aortic valve calcification are improved, coronary artery opening sequence time sequence information is introduced, a double-branch structure is adopted to adapt to structural differences, accurate positioning and recognition of aortic valve calcification lesions are achieved, and the false detection rate and the omission ratio are effectively reduced.

The invention belongs to the technical field of image recognition, and discloses a zero-sample industrial anomaly classification and segmentation system and method based on multi-source expert scoring, and the system comprises a multi-source feature extractor which is used for carrying out the feature extraction of a to-be-detected image through more than two pre-trained feature extraction models, obtaining more than two image source features; the expert scoring module is used for obtaining an abnormal scoring vector in each image source feature according to an expert delegate; and the cascading fusion module is used for carrying out cascading fusion processing on the abnormal score vector of each image source feature to obtain a fused abnormal score vector, and generating a final abnormal score graph according to the fused abnormal score vector, including abnormal classification scoring and abnormal segmentation output. According to the method, the difference of different domains is effectively relieved, the accuracy of system anomaly recognition is improved, the reasoning speed is ensured, the overall performance in anomaly detection is improved, and the method is expected to be widely used in industrial image detection.

The invention relates to the technical field of image processing, in particular to an SAR (Synthetic Aperture Radar) image water body segmentation system and method combined with optical features, and the method comprises the steps: respectively carrying out the feature extraction of an input optical water body image and an SAR water body image through an optical coding branch and an SAR coding branch; the bimodal fusion module is used for fusing the shallow-layer features and the deep-layer features of the two extracted features; the multi-scale fusion module performs multi-scale fusion on the fused shallow features and deep features; and the decoding branch is used for decoding the fusion features to obtain a water body segmentation map. According to the invention, by designing the shallow guidance module, the cross-modal attention module, the multi-scale context fusion module and the like, high-precision segmentation of the water body region in the remote sensing scene is realized, and the recognition precision and the boundary retention capability of the water body category are improved.

Systems and methods are provided for determining fabrics of a geological sample using three-dimensional segmentation. An example method can include receiving three-dimensional (3D) image of a geological sample, adjusting an initial size of the 3D image of the geological sample, and partitioning the resized 3D image of the geological sample into cubes. The example method can include, for each cube, generating orthogonal planes based on a center of mass of each cube and extracting, for the orthogonal planes associated with each cube, one or more features to represent texture of the geological sample. The example method can further include grouping the cubes into one or more clusters based on the one or more features and constructing a volume of the resized 3D image of the geological sample based on the one or more clusters for a texture analysis of the geological sample.

The invention provides a method for constructing an expert operation experience data set and an automatic semantic segmentation system. The method comprises the following steps: selecting representative frames from an original video stream by an expert physician according to a preset labeling protocol, and carrying out manual labeling; a refined semantic segmentation model based on the YOLO-SAM is trained; using the trained semantic segmentation model to segment other frames in the original video stream; and the segmentation result is fused with the original video stream data and the manual annotation data, and a structured expert operation experience data set is constructed. The automatic semantic segmentation system comprises an input module, a YOLO module, an SAM module and an output module, the YOLO module performs target coarse positioning on an image to generate a bounding box with a category label, and provides a bounding box prompt for the SAM module; the SAM module generates a pixel-level high-precision segmentation mask corresponding to each bounding box; a representative frame manually labeled in the method is used for training. According to the method, an expert operation experience data set can be constructed, and automatic labeling, semantic segmentation and the like can be realized.

The invention discloses a thyroid nodule segmentation system capable of dynamically fusing multi-echo features, relates to the technical field of artificial intelligence and ultrasonic image analysis, and realizes accurate identification and segmentation of thyroid nodule multi-echo types through cooperative work of three feature branches of low echo, high echo and equal echo. A special feature extraction module is designed for each branch for a specific echo type, the extraction process of different echo nodule features is optimized, the capturing capability of key features is enhanced, and the adaptability of the model to a complex ultrasonic image is remarkably improved; according to the method, adaptive weight distribution of different echo type features is realized through attention weighted fusion of the multi-modal feature maps, the mechanism generates attention weights by using a double-layer full-connection network, and weighted fusion is performed on the feature maps after Softmax normalization, so that the problem of fixed weight or lack of adaptability in a traditional feature fusion method is solved.