155 results about "Computer-aided diagnosis" 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 liver and gall lesion early screening system and method based on image fusion, and relates to the technical field of medical image processing and computer-aided diagnosis, and the method comprises the following steps: reconstructing a multi-modal image space-time coordinate system under a unified event time baseline, generating a respiratory displacement field and a magnetic sensitive pulse fingerprint, and constructing an artifact suspicion map; and performing anti-fact playback based on the artifact suspicion chart, performing frame-by-frame playback on the image acquisition sequence, quantifying artifact superposition tracks with consistent directions, and solidifying an artifact anchor point set. According to the method, space-time coordinates are constructed based on a unified event time baseline, a breathing displacement field and magnetic sensing pulse fingerprints are introduced, anti-fact playback, distortion kernel inference and residual decoupling are combined, artifact recognition and fusion intervention are achieved, and artifact closed-loop elimination is completed by judging threshold-driven fusion regulation and time reversal phase gating, so that the artifact recognition accuracy is improved. And the fused image authenticity is improved.

The invention discloses a brain tumor MRI image semantic segmentation method, and belongs to the technical field of medical image processing and computer-aided diagnosis. According to the invention, the problem of low segmentation precision obtained based on the existing brain tumor MRI image semantic segmentation technology is solved. The invention provides a semantic segmentation model which combines a U-Net framework with an LWD module, an MFDF module and various filters, the LWD module can keep information as much as possible in a down-sampling process, the MFDF module extracts operators by constructing new directional gradient features, and combines the operators in different directions by using dual-channel filtering, so that the semantic segmentation of the U-Net framework is realized. And the constructed multi-directional filter can respectively extract low-frequency and high-frequency characteristic direction information. The MFDF module transmits detail features from the coding module to the corresponding decoding module, so that spatial information including feature boundaries and textures is recovered, the precision of model segmentation is improved, and the method has good adaptability to the randomness of brain tumor shapes, sizes and boundaries. The method can be applied to brain tumor MRI image segmentation.

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

The invention discloses a thyroid ultrasound image diagnosis method based on deep learning, and the method comprises the following steps: collecting a thyroid ultrasound original image set, and carrying out the preprocessing; performing focus segmentation on the standardized thyroid ultrasound image set; performing morphological constraint and boundary refinement; calculating the blood flow direction, blood flow velocity and blood flow power of each thyroid focus area and neighborhood; generating a preliminary fusion feature map based on a feature adaptive deep learning network, and fusing the preliminary fusion feature map with the thyroid focus blood flow feature vector set; obtaining a thyroid focus detection list through thyroid focus benign and malignant discrimination branches; and generating thyroid focus structured diagnosis data records based on the thyroid focus detection list, and writing the thyroid focus structured diagnosis data records into a computer-aided diagnosis system. According to the method, deep learning and multi-modal blood flow features are fused, intelligent diagnosis of the thyroid focus is achieved, and the method has the advantages of being high in precision, high in interference resistance and structured in result.

The embodiment of the invention provides an image processing and computer-aided diagnosis method, electronic equipment, a storage medium and a program product, and the image processing method comprises the steps: obtaining a to-be-processed medical image which comprises the information of a plurality of target parts; performing first feature extraction on the medical image to obtain medical image features of the medical image; performing second feature extraction on the medical image features to obtain part identification information of each target part corresponding to the plurality of target parts in the medical image features; taking the part identification information of each target part as guidance, performing part feature extraction based on the medical image features, and obtaining part features of each target part; and predicting text features of the text based on the part features of the target parts and the detection results corresponding to the target parts to obtain prediction detection results of the multiple target parts. Therefore, the target part in the medical image can be accurately positioned and detected without depending on an external mask.

The invention discloses a variable convolution UNet tooth image segmentation method and system fusing an attention mechanism, and belongs to the technical field of medical image processing and computer-aided diagnosis. A tooth image data set is obtained and preprocessed; using the preprocessed data set to train a DSC-UNet segmentation model; the DSC-UNet segmentation model introduces a multi-modal feature fusion module and a deformable convolution module into a UNet framework; and segmenting an input tooth image by using the trained DSC-UNet segmentation model, and outputting a pixel-level segmentation result of the tooth structure. By constructing a multi-level feature interaction mechanism and a deformable convolution module, the capturing capability of the network on tooth edge details and the segmentation robustness of special-shaped teeth are remarkably improved, more reliable technical support is provided for an intelligent dental diagnosis and treatment system, and the method is particularly suitable for high-precision segmentation of complex tooth structures in oral panoramic X-ray films and CBCT images.

The invention discloses a mild cognitive impairment auxiliary recognition system and method based on eye movement characteristics, and relates to the technical field of computer-aided diagnosis, and the system comprises a data obtaining module which is used for obtaining eye movement data of a patient in the emotion classification task process; the data preprocessing module is used for performing data preprocessing on the acquired eye movement data; the auxiliary recognition module is used for inputting the preprocessed eye movement data into an auxiliary recognition model based on a double-flow fusion network, the auxiliary recognition model comprises a spatial feature extraction module, a time sequence feature extraction module and a multi-modal fusion classification module, key features of the eye movement hotspot map are extracted through the spatial feature extraction module, and the key features of the eye movement hotspot map are extracted through the time sequence feature extraction module; the time sequence features of the dynamic eye movement sequence are extracted through the time sequence feature extraction module, the features extracted by the two feature extraction modules are spliced and fused through the multi-modal fusion and classification module, and final accurate classification and recognition results of the Alzheimer's disease, the mild cognitive impairment and the health are output.

The invention relates to the technical field of MRI image processing, and discloses a multi-MRI sequence deletion interpolation method based on a variational auto-encoder. According to the method, the FSL software is used for performing registration processing on the multi-mode MRI image, so that the consistency of the image in space, position and voxel pitch is ensured, and the precision of subsequent analysis is improved. In combination with the minimum and maximum value normalization and slice screening technology, the data preprocessing process is optimized, the data quality is improved, and redundancy is reduced. An end-to-end pre-trained variational auto-encoder model is adopted, an accurate potential space mapping relation is constructed, and the image reconstruction capability is enhanced. And fine training is carried out by using the SSIM loss after weight adjustment and the perception loss, so that high-quality modal missing interpolation is realized, and the integrity and availability of the medical image are further improved. According to the method, the multi-modal medical image processing capability can be effectively improved, and accurate technical support is provided for medical image analysis and computer-aided diagnosis.

The invention provides an audio-based mild cognitive impairment auxiliary diagnosis method and device, and relates to the technical field of computer-aided diagnosis. The method comprises the following steps: acquiring original dialogue audio; performing audio and acoustic parameter feature extraction according to the original dialogue audio to obtain a multi-resolution spectrogram feature and a side language feature; based on a multi-scale double attention mechanism of a hierarchical attention weight generation strategy, performing dynamic feature fusion according to the multi-resolution spectrogram features to obtain optimized spectrogram features; performing optimization fine tuning on a pre-trained auxiliary diagnosis model by using the optimized spectrogram features and the secondary language features to obtain an optimized auxiliary diagnosis model; and obtaining an actual dialogue audio, and performing cognitive disorder detection by using the optimized auxiliary diagnosis model to obtain an auxiliary diagnosis result. The invention provides an efficient and accurate mild cognitive impairment auxiliary diagnosis method based on audio.

The invention relates to the field of computer-aided diagnosis, cross-modal fusion, image morphological operation and feature extraction related to computer vision and artificial intelligence, in particular to a method for predicting mild cognitive impairment in the progress stage of Alzheimer's disease, which comprises the following steps: S1, respectively inputting MRI (Magnetic Resonance Imaging), Clinical Table and PET (Positron Emission Tomography) data into respective encoders, and extracting initial image features and table features; s2, for the MRI branch and the PET branch, inputting respective image features and table features into a mixed channel attention module together, and calculating a mixed channel attention feature; s3, performing element-by-element multiplication on the mixed channel attention features obtained in the step S2 and original image features of corresponding branches to realize feature enhancement of channel dimensions; and S4, inputting the feature enhanced in the step S3 into a space attention module, and calculating a space attention feature. Structural MRI, functional PET and clinical table data are integrated for predicting the conversion from MCI to AD.

The invention discloses an Alzheimer's disease diagnosis classification method based on a multi-modal nerve image, which is used for auxiliary diagnosis of Alzheimer's disease. The method comprises the following steps: firstly, respectively extracting image features of sMRI and PET brain images by using a self-attention vision converter, extracting global information, improving the feature discrimination capability, and enabling a model to capture a remote dependency relationship more easily, and secondly, realizing complementary fusion of the two image features by using an interactive attention fusion network. And finally, using a Stacking ensemble learning framework as a classifier. According to the method provided by the invention, the pathological information of sMRI and PET brain images is combined, the Alzheimer's disease classification accuracy is improved, a doctor can be assisted in diagnosis, and the method has a wide application prospect in the field of computer-aided diagnosis.

The invention discloses an Alzheimer's disease computer-aided diagnosis system based on a unified multi-modal mechanism visual language model. The Alzheimer's disease computer-aided diagnosis system comprises a multi-modal data input module, a feature extraction and projection module, an abnormal mark token integration module, a unified multi-modal attention mechanism module, an anomaly detection module and a diagnosis result generation module. The method has the outstanding advantages of improving the early diagnosis precision of the Alzheimer's disease, enhancing the model robustness and improving the clinical practicability.

The invention discloses an intelligent auxiliary diagnosis and grading system for gastrointestinal tumor endoscopic images, which belongs to the technical field of medical image processing and computer-aided diagnosis and comprises a multi-modal image preprocessing and segmentation module, a lesion feature extraction and diagnosis module, a grading and depth evaluation module and a closed-loop feedback optimization module. The system receives white light, a narrow band and an amplified endoscopic image, adaptive segmentation is performed to obtain a lesion area, mucous membrane morphology, capillary and gland features are extracted, Paris classification, Vienna classification and infiltration depth evaluation are realized, and segmentation parameters are subjected to closed-loop optimization according to classification confidence. And intelligent auxiliary support is provided for early diagnosis and treatment decision of gastrointestinal tumors.

Provided in the embodiments of the present disclosure are a target object identification method, an object identification model training method, a method for detecting a visible lymph node in a CT image, a computer aided diagnosis method, an electronic device, a storage medium, and a program product. The target object identification method comprises: determining an image to be subjected to identification; and inputting said image into an object identification model for object identification, so as to obtain a target object in said image, wherein the object identification model is trained by means of a target sample object, which is identified from a sample image, and a sample label of the sample image, the target sample object is determined from among a plurality of candidate sample objects by means of object type identification results and object position detection results of the plurality of candidate sample objects, the object position detection results are obtained by means of performing position detection on object positions of the plurality of candidate sample objects, and the plurality of candidate sample objects are obtained by means of performing object identification on the sample image.

The invention relates to the technical field of computer-aided diagnosis, and discloses a block classification aided diagnosis system based on a self-prompt mechanism, which comprises the following steps: dividing a medical ultrasonic image into a plurality of image blocks, carrying out convolution and flattening processing to obtain a feature vector, extracting a preliminary global feature through a plurality of visual conversion blocks, and carrying out self-prompt on the image blocks; a fusion feature is obtained through a plurality of prompt attention conversion blocks, a self-prompt mechanism is introduced into the prompt attention conversion blocks, a learnable space prompt vector and a channel prompt matrix are introduced into the self-prompt mechanism, and a space mask is generated by calculating cosine similarity of the space prompt vector and a preliminary global feature. After spatial screening operation is carried out on the initial global features, channel guiding operation is applied through a channel prompt matrix; and performing weighted adjustment on the fused features to obtain final features, and obtaining benign and malignant classification results through a classification head. The feature modeling capability of the local fine-grained discriminative region in the image can be effectively improved, and the auxiliary diagnosis accuracy is improved.

The embodiment of the invention provides a cancer computer-aided diagnosis method and system, an esophageal cancer computer-aided diagnosis method and a gastric cancer computer-aided diagnosis method.The tumor detection method comprises the steps that a CT image processing task is received, the CT image processing task carries a plurality of CT images corresponding to a target detection area, and the target detection area corresponds to the CT images; the CT image processing task is used for detecting whether a tumor exists in the target detection area; the multiple CT images are input into a CT image processing model, a detection result corresponding to the target detection area is obtained, the CT image processing model generates the detection result corresponding to the target detection area based on the multi-scale feature information corresponding to the multiple CT images, and the detection result corresponding to the target detection area is obtained. The detection result comprises detection annotation information, detection category information and a detection guidance text. The accuracy of subsequently generated detection results is improved by obtaining the multiple pieces of scale feature information corresponding to the CT image. The detection result comprises the position information of the abnormal object, the information of the to-be-detected object and the guidance text, the detection result is enriched, multi-dimensional detection information is provided for the user, and the use experience of the user is improved.

The invention provides an intelligent assessment method for cervical lateral deviation based on multi-modal fusion and deep learning, and solves the technical problems of single data source, weak feature generalization ability and large assessment result error caused by only analysis of a static single frame in existing cervical lateral deviation assessment. The method comprises the steps of obtaining multi-modal data, performing preprocessing, attitude normalization and feature weighted fusion to obtain fused multi-scale features, performing mapping to generate a heat map, and performing peak retrieval on the heat map to output key points; tracking time sequence tracks of the key points, correcting abnormal key points, and collecting continuous multi-frame key points for key point detection to obtain target key points; and constructing a cervical vertebra trunk coupling mechanical model, dynamically optimizing a gravity line reference axis, correcting pelvic inclination attitude deviation, and optimally solving the optimal offset through a Lagrange multiplication method and a least square method. The method can be widely applied to the technical field of medical image analysis and computer-aided diagnosis.

The invention discloses a lightweight dynamic multi-scale segmentation method for a colon polyp. The lightweight dynamic multi-scale segmentation method comprises the following steps: S1, image input and data enhancement; s2, backbone network feature extraction: constructing a U-shaped encoder-decoder structure, wherein an encoder extracts multi-scale features through a dynamic multi-scale convolution module; s3, local and global feature enhancement: capturing local texture details through a regional detail aggregator, and obtaining global semantic information through a global context integrator; and S4, detection and segmentation: integrating the local details and the global semantic information by a decoder by adopting a hierarchical later fusion strategy, and outputting a final segmentation result. According to the method, the dynamic multi-scale convolution module is introduced, and the dynamic kernel selection and structure re-parameterization technology is used, so that the multi-scale feature extraction capability is greatly improved while the light weight of the model is kept; a regional detail aggregator and a global context integrator are introduced, so that local details and global semantics are effectively coordinated, and the segmentation precision of the polyp boundary is remarkably improved; compared with the prior art, the colon polyp segmentation method has the advantages that higher accuracy and intersection-union ratio are achieved on colon polyp segmentation tasks, and meanwhile, the colon polyp segmentation method has lower calculation complexity and parameter quantity and is suitable for real-time deployment of a clinical computer-aided diagnosis system.

The invention relates to the field of medical image processing and computer-aided diagnosis, in particular to an OCT (optical coherence tomography) retina layer and hydrops segmentation method based on a graph attention dual-decoder network, which comprises the following steps of: preparing and preprocessing data, acquiring OCT image data and generating corresponding segmentation labels and boundary labels; feature coding: processing the input image through a shared encoder network, and extracting and outputting feature maps of multiple levels; carrying out dual-path decoding and interaction, carrying out region segmentation and boundary segmentation by using two parallel decoders, and carrying out cross-path feature interaction through an attention mechanism during decoding; a graph attention mechanism is introduced into a region segmentation path, and information modeling is carried out on the deep features; and calculating a composite loss function according to the output of the decoder, and optimizing network parameters. According to the method, high-precision segmentation of the lesion OCT image is realized through a double-decoder multi-task architecture, cross-level space attention interaction and image attention global reasoning.

This invention relates to a grading method for diabetic retinopathy based on a local-global interactive dual-branch network, belonging to the fields of deep learning, medical image analysis, and computer-aided diagnosis. It includes: inputting the generated initial feature sequence into a global scanning module; using dilated reparameterized convolution to capture multi-scale structural prior information; inputting the feature map with structural prior information into a core dual-branch LoGo module; the local flow extracting fine-grained lesion features through depthwise separable convolution; the global flow generating spatially variable convolution kernels based on the global context through contextual hybrid dynamic convolution; and performing bidirectional cross-modulation through an adaptive multi-scale feature interaction aggregation module to sharpen local details for global semantics and filter local noise from the global context, ultimately outputting a disease severity grade. This invention explicitly simulates the cognitive strategies of clinicians, significantly improving grading accuracy while maintaining computational efficiency.

A storage medium, a diagnosis support apparatus and a diagnosis support method that enable presenting a recognition result suitable to a status of use of a CAD (computer-assisted diagnosis / detection) function are provided. A diagnosis support apparatus performs recognition processing of a breast image showing a projection image or a section image of a breast of a subject, using one or more recognizers from among a plurality of recognizers each including a neural network, and selects a recognizer to be used for the recognition processing or a recognizer that is to output a recognition result of the recognition processing, from among the plurality of recognizers, according to examination information relating to an examination of the subject.

The invention provides a helicobacter pylori fluorescence image fine tuning detection method and device and a readable storage medium thereof, and belongs to the technical field of medical image processing and computer-aided diagnosis. The method aims at solving the problems that in the prior art, the false positive is high due to the fact that only a fluorescence signal is relied on, and a model is poor in generalization for different data environments. The method comprises the following steps: outputting a detection frame of a suspected target from a fluorescence image block through a detection module; determining a target analysis area containing a detection frame and a surrounding tissue area, and inputting the analysis area into a classification module to judge true and false positive; and when the classification result is confirmed to be wrong, only performing fine tuning training on a classifier in the classification module by utilizing target analysis area data corresponding to the error and combining a group of preset representative sample features. According to the method, the false positive rate is remarkably reduced by using surrounding organization information, and the generalization ability and scene adaptability of the model are improved through an efficient fine adjustment mechanism.

The invention discloses an Alzheimer's disease classification method based on sMRI and PET brain images. The Alzheimer's disease classification method is used for auxiliary diagnosis of the Alzheimer's disease. The method comprises the following steps that firstly, a feature extraction network is used for extracting image features of sMRI and PET brain images, and the feature extraction network enhances the spatial position information capturing capacity and the redundant information inhibiting capacity of the network by embedding a coordinate attention mechanism and space-channel reconstruction convolution; secondly, realizing complementary fusion of two image features by using a parallel interaction network based on proxy cross attention; and finally, classifying by using a full connection layer as a classifier. According to the method provided by the invention, the pathological information of sMRI and PET brain images is combined, the Alzheimer's disease classification accuracy is improved, a doctor can be assisted in diagnosis, and the method has a wide application prospect in the field of computer-aided diagnosis.

The invention discloses an ultrasonic shear wave elastography-based intrameniscus tearing quantitative evaluation method and system, and relates to the technical field of computer-aided diagnosis. The method is used for solving the problem of insufficient quantification of meniscus tearing detection. The method comprises the following steps: firstly, fixing a knee joint at a preset buckling angle, exciting shear waves along an annular fiber direction and a vertical direction, and collecting ultrasonic radio-frequency signals of an affected side meniscus and an uninjured side meniscus in different load states; then, a three-dimensional anisotropic elastic atlas is constructed based on the shear wave propagation velocity field, and spatial elastic distribution characteristics of the tissue are obtained; thirdly, calculating an elastic continuity index, a spatial variation coefficient and an abnormal region morphological parameter, and quantitatively representing the continuity and local tearing characteristics of the tissue structure; and finally, inputting the multi-dimensional features into a multi-branch neural network, fusing the features through a cross-branch attention mechanism, and outputting a meniscus tearing quantitative evaluation value, thereby realizing intelligent and objective evaluation of the tearing degree, and providing a basis for clinical diagnosis and postoperative rehabilitation.

Embodiments described herein include systems for implementing annotation-efficient deep learning in computer-aided diagnosis. Exemplary embodiments include systems having a processor and a memory specially configured with instructions for learning annotation-efficient deep learning from non-labeled medical images to generate a trained deep-learning model by applying a multi-phase model training process via specially configured instructions for pre-training a model by executing a one-time learning procedure using an initial annotated image dataset; iteratively re-training the model by executing a fine-tuning learning procedure using newly available annotated images without re-using any images from the initial annotated image dataset; selecting a plurality of most representative samples related to images of the initial annotated image dataset and the newly available annotated images by executing an active selection procedure based on the which of a collection of un-annotated images exhibit either a greatest uncertainty or a greatest entropy; extracting generic image features; updating the model using the generic image features extracted; and outputting the model as the trained deep-learning model for use in analyzing a patient medical image. Other related embodiments are disclosed.

This invention discloses a multi-label classification method for medical images based on spatiotemporal integration and adaptive normalization, belonging to the field of medical image processing and computer-aided diagnosis technology. The invention proposes an improved scheme. Firstly, it constructs a dynamically balanced data input stream through frequency inverse sampling and cosine annealing strategies. Secondly, it introduces group normalization to replace batch normalization in the feature extraction network, solving the training convergence problem under limited GPU memory. During training, it employs a focal loss function to mine hard-to-classify samples. In the inference stage, the invention constructs a spatiotemporal integration mechanism based on multi-view space transformation and multi-stage model weight fusion, combined with an adaptive threshold decision algorithm based on maximizing F1 scores to output diagnostic results. This invention effectively improves the recognition accuracy of key pathological features such as cardiac hypertrophy and edema, while significantly improving the recall rate of small lesions, exhibiting high robustness and clinical auxiliary value.

The application relates to a light wavelet enhancement fusion medical image segmentation method, and belongs to the technical field of medical image processing and computer-aided diagnosis. The core of the method is to construct a light WEF-Net network, which comprises an encoder, a bottleneck layer and a decoder. The encoder adopts a dual-domain perception module, extracts complementary features from the frequency domain and the time domain through wavelet transformation and convolution, and simultaneously extracts complementary features from the frequency domain and the time domain. The bottleneck layer is designed with a feature aggregation Kolmogorov-Arnold transformation module, which is used for efficiently fusing multi-scale semantic information and enhancing the nonlinear modeling capability. The network level also integrates a sawtooth rolling feature fusion module, which enhances the global continuity of the features through the channel rolling and spatial scanning mechanism to improve the integrity of the boundary segmentation. Experiments show that the method realizes excellent performance on multiple public medical image datasets, while maintaining low parameter quantity and low computational quantity, and significantly improves the segmentation precision.

The invention discloses a computer-aided diagnosis method and system for Alzheimer's disease analysis, and relates to the technical field of medical information processing, and the key points of the technical scheme are as follows: the method comprises the following steps: obtaining a T1 weighted brain magnetic resonance image of a subject; dividing the two-side sea horse tooth-shaped gyrus areas; image omics features are extracted from the subregions; selecting a group of discriminative feature combinations containing texture features from the right dentate loop and first-order statistical features from the left dentate loop through a multi-layer screening strategy; and inputting the feature combination into a pre-trained machine learning model to generate a diagnosis classification result. According to the method, high-precision and high-sensitivity diagnosis of the Alzheimer's disease is realized by using non-invasive standard MRI data and capturing the microstructure change of dentate gyrus, and the diagnosis result has strong correlation with key plasma biomarkers and cognitive level, so that the method has important clinical application value.

The invention provides a breast lump segmentation method based on channel-guided double-pooling multi-scale space attention, and aims to solve the problems of small area, low contrast, fuzzy boundary and the like of breast lumps in an X-ray image, the network extracts full-view image features and compresses space dimensions through a deep residual encoder; and realizing fusion of low-level details and high-level semantic features in the decoder by means of jump connection. A double-pooling gating mechanism is built in the decoder, channel guide weights are generated in parallel through global average pooling and maximum pooling, lesion significant features are screened in a self-adaptive mode, and redundant backgrounds are restrained; the multi-scale space attention module captures multi-scale space information through multi-branch large-kernel separable convolution, generates a space attention graph, combines the space attention graph with channel weights element by element, and accurately focuses a lesion area and a boundary. Experiments show that the Dice coefficients of the method on INbreast, CBIS-DDSM and private In-home data sets respectively reach 90.94%, 80.60% and 84.50%, the method is superior to a mainstream method, the segmentation precision and generalization ability are improved, and reliable support is provided for early screening and computer-aided diagnosis of breast cancer.