39 results about "Breast lesion" patented technology

The invention relates to a breast tumor lesion part ultrasonic image segmentation method based on an SAM-VMama model. The method comprises the steps of obtaining a breast ultrasonic image set and performing preprocessing; the method comprises the following steps: constructing an SAM-VMama model, wherein the SAM-VMama model comprises a first encoder, a second encoder, a prompt encoder and a mask decoder; obtaining a trained model; obtaining a preprocessed breast ultrasound image to be segmented; and obtaining a segmentation result of the breast lesion tumor. According to the method, the local features are extracted through the first extraction, the global features are extracted through the second encoder, the interactive prompt information processed by the prompt encoder is combined, and the precise segmentation mask is generated through the mask decoder; the method not only retains the flexibility of SAM interactive segmentation, but also improves the long-sequence data processing capability and calculation efficiency through a Mamba structure, effectively adapts to the characteristics of uneven gray scale, multiple artifacts and the like of the ultrasonic image, and achieves the balance of high precision and high efficiency.

The invention discloses a focus intelligent detection and diagnosis system based on multi-modal medical image fusion, and relates to the technical field of medical image processing. A molybdenum target image, an ultrasonic image and a magnetic resonance image of the mammary gland of the patient are acquired through the data acquisition module; a data registration module is used for carrying out partition affine and local refinement registration on the multi-modal image by taking the molybdenum target image as a geometric anchor point and combining parameters such as compression force; extracting registered fusion feature data through a data fusion module; generating a candidate focus set by a focus detection module; finally, the risk assessment module calculates a comprehensive risk score through weighting, correction and consistency verification processes based on a plurality of interpretable feature indexes. And the output module generates a visual diagnosis result. According to the invention, intelligent detection and risk assessment of rechecking of breast lesions are realized, and the diagnosis accuracy and clinical reliability are effectively improved.

The invention belongs to the technical field of focus progress trend prediction, and provides a mammary gland focus progress trend prediction method based on multimode data fusion, and the method comprises the steps: analyzing the local correlation degree between each index and a clinical progress state label and the change acceleration degree of each index in a time window; obtaining the sensitivity degree of each index of each patient in each time window; analyzing the individual physique difference degree of each index according to the sensitivity degree, further analyzing the stage difference degree of each index in each time window, and determining the fluctuation degree of each index of each patient in each time window; based on the sensitivity degree and the fluctuation degree, the fusion weight of each index of each patient in each time window is obtained, fusion of multiple indexes is carried out, and breast focus progress trend prediction is achieved. The method can overcome the defect that a fixed weight index fusion method is insufficient in adaptability in the mammary gland lesion evolution process, and the prediction accuracy of the mammary gland lesion progress trend is improved.

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

The invention discloses a multi-degree-of-freedom precise puncture locator for breast lesions, and relates to the technical field of medical instruments, the multi-degree-of-freedom precise puncture locator comprises a locating piece, a lower control part and an upper control part, after a lower pressing plate is loosened, a middle spring, a lower spring and a lower pressure spring are reset, so that an abutting head abuts against the locating piece, a middle abutting column abuts against a folding rod, and the upper control part is connected with the upper control part; the effect of simultaneously controlling the position of the lower control part in the positioning piece and the height and torsion degree of the folding rod in the vertical cylinder is achieved; the control button is rotated to enable the inclined push block to move, when the inclined push block is far away from the inclined plane block, the push frame is separated from the side frame, the side frame is pulled by the front tension spring to be close to the sliding rod, the front abutting rod loses the force of abutting against the pointer shaft, when the inclined push block is close to the inclined plane block, the push frame pushes the side frame, the side frame and the sliding rod jointly abut against the folding rod, and the front abutting rod abuts against the pointer shaft. And the effect of simultaneously controlling the telescopic and circumferential rotation positions of the upper control part on the folding rod and the rotation angle of the positioning ring on the front frame is achieved.

The invention discloses an automatic prompting lightweight ultrasound image breast lesion segmentation method and system, and the method comprises the steps: obtaining breast lesion ultrasound image data, and carrying out the data preprocessing, and obtaining a preprocessed breast lesion ultrasound image; an encoder module, an automatic prompt module and a decoder module are introduced, and an ultrasonic image mammary gland lesion segmentation model is constructed; and based on the ultrasonic image mammary gland lesion segmentation model, performing segmentation processing on the preprocessed mammary gland lesion ultrasonic image to obtain a mammary gland lesion ultrasonic image segmentation result. Complex structures and weak boundaries can be captured more finely in a breast ultrasound image segmentation task, and the segmentation precision is improved. The lightweight ultrasonic image breast lesion segmentation method and system with the automatic prompting function can be widely applied to the technical field of ultrasonic image segmentation.

The invention discloses a system for multispectral transmission image breast lesion segmentation, and belongs to the technical field of medical image processing and optical imaging. The system comprises an image preprocessing subsystem and a semi-supervised segmentation network subsystem. The preprocessing subsystem adopts modulation and demodulation, frame accumulation and a scattering correction technology based on spatial pyramid matching, so that the signal-to-noise ratio and the contrast ratio of an image are remarkably improved; the segmentation network subsystem proposes a multi-head consistency semi-supervised network model, integrates a multi-head decomposition module and a cross supervision module, and effectively utilizes unlabeled data to carry out uncertainty modeling and consistency constraint. Under the condition that only 20% of labeled data is used, the segmentation Dice coefficient on the self-built mammary gland phantom data set reaches 91.47% and is obviously superior to that of an existing method, and a complete solution from image enhancement to intelligent segmentation is provided for early diagnosis of the breast cancer.

The invention discloses a multifunctional radiofrequency ablation system for treating breast lesions, which comprises a radiofrequency host, an ablation electrode, a pumping device and a controller, the radiofrequency host is electrically connected with the controller, both the ablation electrode and the pumping device are electrically connected with the radiofrequency host, and the pumping device is communicated with the ablation electrode; the ablation electrode comprises a needle rod assembly and a handle assembly, a first channel is formed in the needle rod assembly, a second channel communicated with the first channel is formed in the handle assembly, and a quick-release connecting piece communicated with the second channel is arranged outside the handle assembly. The device integrates the five functions of radiofrequency ablation, cyst fluid suction, sampling biopsy, smoke exhaust and flushing, cyst fluid suction, sampling biopsy, flushing operation and inner cyst wall ablation in the normal saline environment are achieved through the ablation electrode, instruments do not need to be replaced repeatedly in the ablation process, smoke generated in the ablation process can be exhausted, and the ablation efficiency is improved. And the operation efficiency and the ablation accuracy are effectively improved.

The application discloses a breast CEM image lesion recognition system and equipment based on deep learning, and relates to the field of medical image processing. By acquiring a low-energy image and a subtraction image of an original CEM image and performing pretreatment, lesions in a breast region of the image are labeled, and two ResNet50 networks are used to extract preliminary extraction features of the low-energy image and the subtraction image respectively. Double-mode auxiliary feature fusion is performed to obtain a multi-scale pyramid feature layer. A detection head with an attention mechanism is connected after each pyramid feature layer to finally position and classify breast lesions. The application realizes rapid detection, positioning and classification of breast lesions based on a multi-modal CEM image based on a deep learning method, and improves breast lesion recognition efficiency and precision.

The invention discloses an eight-electrode human body impedance spectroscopy measuring device which is used for simply screening human body diseases in the early stage. Aiming at the problems of invasiveness, huge size, complicated operation and the like of the existing breast cancer detection equipment, the device provides a wearable non-invasive detection scheme. The system mainly comprises a main control chip STM32F103ZET6, an impedance measurement chip AD5933, an analog switch chip ADG5409 and eight disposable silver chloride measurement electrodes. The main control chip is communicated with the AD5933 through a pin simulation IIC, a DDS generator in the main control chip is configured to output sine excitation signals of 1 kHz to 100 kHz, and impedance real part and imaginary part data after internal DSP Fourier transformation are read; meanwhile, the main control chip controls the ADG5409 to divide the eight electrodes into four pairs, and multi-channel switching measurement is carried out. Measurement data is transmitted to an upper computer through a UART1 serial port so as to draw a single-channel or four-channel impedance spectrum. The human health condition is judged through the impedance spectrum change trend, the whole system is small in size, easy to carry about and high in measurement precision, and early discovery and high-frequency prevention screening of breast lesions are facilitated.

The invention discloses a breast lesion segmentation method and device for contrast-enhanced ultrasound contrast images, and the method achieves the feature interaction through constructing a double-branch coding-single-branch decoding network and introducing a cross-modal multi-branch deep convolution module, effectively inhibits the background false positive through combining with a channel energy scoring mechanism and a gland priori weighting mechanism, and achieves the segmentation of the breast lesion of the contrast-enhanced ultrasound contrast images. And the focusing capability and the cross-equipment generalization capability of the model to the gland region are improved. The method has the advantages of compact model and high calculation efficiency while ensuring high segmentation precision, and is suitable for clinical real-time diagnosis scenes.

This invention discloses a method for detecting breast lesions in breast ultrasound video by incorporating spatiotemporal contextual information. Addressing the shortcomings of existing methods such as insufficient spatial representation, weak temporal correlation modeling, and high computational cost, this invention constructs a detection network comprising a backbone network, a single-frame cross-scale feature fusion module, a detection head, a cross-batch feature aggregation module, and a multi-frame temporal feature fusion module. The video is divided into consecutive multi-frame temporal segments, and shallow and deep features are enhanced through frequency-domain aware cross-scale fusion to improve spatial representation. A candidate feature memory stores high-quality candidate features, and cross-batch feature aggregation absorbs historical temporal information with low computational overhead. The multi-frame temporal feature fusion module then adaptively weights classification and regression features to highlight key temporal clues. This invention effectively improves the accuracy of breast ultrasound video lesion detection and grading without significantly increasing computational cost, and possesses good real-time performance and clinical application value.

The application discloses a method and device for autonomous scanning of breast lesions by an ultrasonic robot. The method comprises: controlling a probe to scan a breast according to a preset scanning track, and acquiring an ultrasonic image in real time during the scanning; detecting a breast lesion based on the current ultrasonic image; if the breast lesion is detected, fitting the detected breast lesion contour to obtain a current lesion contour; if the area of the current lesion contour is not less than a preset area threshold, determining whether the current lesion contour already exists in a historical scanning record; if not, adjusting the current lesion contour to the center of the ultrasonic image by controlling the probe to move; determining the included angle between the breast curved surface and the probe according to the preset scanning track and the current position of the probe; and controlling the probe to perform lesion fine scanning on the current lesion contour according to the calculated included angle between the breast curved surface and the probe and a preset search range.

This invention provides a guide frame, method of use, and testing method for breast lesion biopsy, belonging to the field of medical auxiliary device technology. It includes a breast positioning support component and a movable guide component. The breast positioning support component is placed on the scanning bed and has a scale plate with grid markings for stable support of the breast and quantification of lesion coordinates and height. The movable guide component is slidably disposed on the side of the scanning bed and includes a lifting adjustment mechanism, an angle adjustment guide frame, and a puncture guide with guide grooves, enabling multi-degree-of-freedom precise adjustment and locking of the puncture path. This invention is made of magnetically compatible materials, eliminating artifact interference; it achieves millimeter-level precise positioning through three-dimensional quantification of coordinates, height, and angle; it can be used in conjunction with a physical breast model for preoperative simulation, significantly improving the success rate and safety of puncture. The structure is simple and the cost is low, making it suitable for large-scale clinical application.

The present invention falls within the field of medical imaging, specifically imaging aimed at identifying breast lesions, specifically, identifying potential breast cancer lesion masses or potential cancer lesion calcifications of the breast. The object of the present invention is a computational method for the improved identification of breast lesions that involves obtaining digital images of a breast section, with at least two digital images obtained by different imaging technologies, their segmentation and consequent correlations, to identify one or more cancer lesions. This allows for improved automation of the identification of breast lesions.

The invention discloses a mammary gland calcification focus dye positioning method and a dye positioning needle, and relates to the technical field of medical instruments.The dye positioning needle is used for being installed on a three-dimensional positioning arm of a molybdenum target host and comprises a needle cylinder, dye is contained in the needle cylinder, one end of the needle cylinder is provided with a liquid outlet end, and a piston is further arranged in the needle cylinder in a sliding mode; the piston is configured to drive the dye to be discharged from the liquid outlet end during sliding; the needle head comprises a base part and a needle tube, the base part is detachably mounted at the liquid outlet end of the needle cylinder, and the needle tube is used for guiding the dye discharged from the liquid outlet end to be injected into the breast so as to mark and position boundary dyeing of a breast focus. The dye positioning needle injects dye into the four boundaries of the innermost side, the outermost side, the uppermost end and the lowermost end of the mammary gland calcification focus under the guidance of the molybdenum target machine positioning system, and dye marking positioning is achieved. And meanwhile, dye mark positioning of the four boundaries is combined with center point positioning of the metal guide wire, so that crossed three-dimensional boundary positioning can be realized.

The application discloses a breast lesion segmentation method and device based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which comprises the following steps: after pre-processing the DCE-MRI image, the breast region is segmented first, and then the breast cancer lesion region is segmented based on the segmented breast region; when the breast cancer lesion region is segmented, a U-shaped network comprising two encoders and a decoder is adopted, the pre-processed contrast-enhanced late-phase image, the pre-processed contrast-enhanced early-phase image and the pre-processed breast prediction image are spliced respectively and input into an encoder for breast cancer lesion region segmentation to obtain a breast cancer prediction image, and an attention module is adopted between the two encoders to comprehensively process the contrast multi-period information. Compared with the prior art, the application can alleviate the class imbalance problem and exclude the interference of the regions outside the breast, and has high segmentation precision for the segmentation of the breast cancer lesion.

The invention discloses a breast focus recognition and positioning method, and relates to the technical field of medical image processing, and the method comprises the steps: carrying out the breast focus image recognition and detection of a target patient through a molybdenum target technology, carrying out the statistics of the position features of a main detection focus with fuzzy boundary, and if the target patient is detected as a fat-type breast tissue dominant region, carrying out the recognition and positioning of a breast focus. If not, marking the distribution position of the fat-type mammary gland tissue as a central position to obtain the central position characteristics of the auxiliary measurement tissue, marking the main measurement focus position characteristics and the central position characteristics of the auxiliary measurement tissue to obtain a plurality of marked lines, and setting the included angles formed among the marked lines to be the same, and according to the distance value of the marked line and the angle value formed between the main measurement focus position feature and the nipple, the position inclination degree of breast focus recognition continuous examination on the target patient is preset, breast focus continuous examination is carried out on the target patient through the high-frequency sound wave imaging technology, and a focus proofreading and positioning result is obtained. The focus identification and positioning accuracy can be improved.

The invention discloses an ABUS-based nodule elastography detection method, device and equipment and a medium, and the method comprises the following steps: controlling a probe in an ABUS to carry out flat scanning along the surface of a mammary gland, obtaining a cross section two-dimensional ultrasonic image, and generating a coronal plane image; detecting and identifying the position of a focus in the coronal plane image, and tracing a cross section image sequence containing the focus; detecting and segmenting the focus, and determining the specific position of the focus in the sequence; a probe in the ABUS is controlled to move to the section position corresponding to the maximum section of the focus, the probe in the ABUS is controlled to apply pressure to the surface of the tissue, so that the tissue deforms, the displacement change of the tissue under the pressure effect is recorded, and a strain elasticity image is generated; and evaluating the relative hardness of the lesion area according to the strain elasticity image. The method has the advantages that the ABUS is used for achieving elastography of breast nodules, the relative hardness of a focus area is evaluated, the method can be used for breast lesion screening, positioning and tracking return visit, and the performance and application of ABUS equipment are expanded.

A digital breast tomosynthesis (DBT) method includes: disposing a breast of an examination subject between an X-ray irradiator and an X-ray detector; acquiring first image data by emitting X-rays toward the breast of the examination subject; analyzing the first image data to estimate a type of breast lesion by using a pre-trained AI image diagnosis portion; determining an angular range for performing DBT according to the estimated type of breast lesion; performing the DBT in which the X-ray irradiator rotates around the breast within the determined angular range and emits X-rays toward the breast at every predetermined angular interval, to acquire second image data; and reconstructing the second image data to generate a tomosynthesis image, wherein a first dose, which is a total X-ray dose in acquiring the first image data, is smaller than a second dose, which is a total X-ray dose in performing the DBT.

The invention discloses a wearable device and system for breast health monitoring. The wearable device comprises a cover, a wearable device and a monitoring device, the wearing piece is arranged on the outer side of the sleeve cover and is used for a wearer to wear the wearable equipment; the flexible bag is arranged in the sleeve cover; the monitoring assembly is arranged on the side, close to the skin of the wearer, in the flexible bag, after the flexible bag expands, pressure is applied to the monitoring assembly so that the monitoring assembly can form pressure on the breasts of the wearer, and therefore the breast health state of the wearer is monitored. According to the technical scheme, on the basis of the expandable flexible bag structure, the monitoring assembly is integrated, structural adaptation and the health monitoring function are deeply fused, and daily high-frequency, precise and intelligent breast lesion early-stage auxiliary monitoring can be achieved.

The invention discloses a breast ultrasound image segmentation method fusing wavelet sub-band features and multiple attention mechanisms in the technical field of image processing, and the method specifically comprises the steps: S1, carrying out the preprocessing and enhancement processing of an original breast ultrasound image, and obtaining an enhanced breast ultrasound image; s2, performing wavelet transform on the enhanced breast ultrasound image to obtain enhanced high-frequency sub-band features; s3, performing weighted fusion on the enhanced high-frequency sub-band features and shallow layer features output by a decoder to generate an aligned feature map; s4, inputting the aligned feature map into a training breast ultrasound image segmentation model, and performing segmentation test on a test set; according to the invention, the limitation of traditional wavelet sub-band isolation processing is broken through; the two attention mechanisms are respectively combined with wavelet characteristics to collaboratively model horizontal and vertical characteristics so as to capture weak edges of breast lesions and enhance high-frequency channel response through low-frequency semantic guidance, and structures and boundaries are considered; and the segmentation integrity and accuracy are improved.

This application provides a deep learning-based breast radiomics classification method and system. The method involves preprocessing breast image data to obtain preprocessed breast image data, extracting regions of interest (ROIs) for breast lesions from the preprocessed breast image data based on lesion annotation information, performing dual-path feature extraction on the ROIs to obtain radiomics features of morphology and texture, and semantic deep features with lesion semantic representation information, and constructing a deep learning feature fusion model for breast images. Based on this model, the radiomics features and semantic deep features are fused to obtain a fused feature vector for the breast images. Finally, lesion category analysis is performed on the breast images using lesion samples and the fused feature vector to obtain the classification results for the breast lesions. Using this method, breast images can be classified based on their physical interpretability and deep semantic representation capabilities.

The invention relates to the field of big data management, and discloses a breast lesion area recognition system and method based on big data, which are used for converting a medical image analysis problem into a biophysical process simulation and feature comparison problem. The breast lesion area identification method based on the big data comprises the steps of processing breast DCE-MRI time sequence data, generating driving source data, extracting image data at a specific time point as a steady-state driving source of a reaction-diffusion system, generating a morphological field representing tissue biochemical characteristics through numerical solution, and identifying the breast lesion area based on the big data. A standard form field cloud library is constructed by using large-scale normal mammary gland data. According to the method, the biophysical process is simulated by adopting the reaction-diffusion theory, the problems of data dependence and black box of a machine learning model are avoided, and the interpretability and accuracy of lesion recognition are improved.

The present application relates to a kind of breast lesion edge detection method and device based on IoU oriented soft label.The method comprises: obtaining the breast ultrasound image containing label frame, the breast ultrasound image is input into deep learning model, generate all prediction frame and the classification confidence of each prediction frame, and all prediction frame and the label frame are matched with hungarian, according to whether match successfully, prediction frame is divided into positive sample frame and negative sample frame, based on the IoU value of each positive sample frame and matched label frame, positive sample loss is calculated, based on the classification confidence of all negative sample frame, negative sample loss is calculated, the total loss is obtained by weighted summation to positive sample loss and negative sample loss, with total loss to deep learning model is supervised training, obtain the trained deep learning model, utilize the model to detect any breast ultrasound image, obtain breast lesion.The method can realize the accurate detection of breast edge fuzzy lesion.

The present application belongs to the technical field of lesion progress trend prediction, and provides a breast lesion progress trend prediction method based on multi-mode data fusion. The present application obtains the sensitivity degree of each index of each patient in each time window by analyzing the local correlation degree between each index and the clinical progress state label and analyzing the change acceleration degree of each index in the time window. According to the sensitivity degree, the individual physical difference degree of each index is analyzed, and then the stage difference degree of each index in each time window is analyzed to determine the fluctuation degree of each index of each patient in each time window. Based on the sensitivity degree and the fluctuation degree, the fusion weight of each index of each patient in each time window is obtained, multiple index fusion is carried out, and the breast lesion progress trend prediction is realized. The present application can overcome the insufficient adaptability of the fixed weight index fusion method in the breast lesion evolution process, and improve the prediction accuracy of the breast lesion progress trend.

The application discloses a marker and a pushing device, and relates to the technical field of medical devices. The marker is a spring coil, and the spring coil is provided with a plurality of reflecting surfaces. The plurality of reflecting surfaces are divided into a dense reflecting part and a sparse reflecting part. The dense reflecting part is used for forming a dense bright band image during development, and the sparse reflecting part is used for forming a spaced bright band image during development. The application provides a marker for breast lesion positioning, which enhances the development of the marker under ultrasound, and the marker can quickly recover to an unfolded state after being released to a target position, and is firmly combined with tissue and is not prone to displacement.

The present application relates to the technical field of magnetic resonance imaging, and discloses a breast lesion multi-modal magnetic resonance imaging system based on multi-nuclear atom information fusion, which comprises: synchronous acquisition of hydrogen nucleus, sodium nucleus and phosphorus nucleus signals and conversion into a quantitative concentration field, which is mapped to a hydrogen nucleus spatial system after a resolution matching operation; subsequently, a non-lesion reference background is extracted to generate a sodium abnormal distribution field and a phosphorus abnormal distribution field, and an initial lesion profile and its directional extension weight are generated; then, the abnormal layer center positions of the two abnormal distribution fields are calculated along the outer normal to obtain local layer position eccentricity, and the directional extension weight is used for weighted calculation to combine into a boundary correction amount; finally, a target lesion three-dimensional surface is reconstructed based on the boundary correction amount, and a medical image model is output by fusing the local layer position eccentricity on the surface. The present application effectively solves the multi-nuclear metabolic layer position eccentricity problem in the invasion front, and comprehensively improves the precision of lesion three-dimensional profile boundary reconstruction and metabolic distribution calibration.

A system for annotating mammography images of a subject using thermal images and mammography images of the subject using thermal and mammography images of the subject by (i) identifying a first breast region in the thermal image and a second breast region in the mammography image, (ii) identifying a block of pixels (Pt) with a high temperature region associated with a breast lesion within the first breast region, (iii) estimating a location (l) of the breast lesion corresponding to the identified block of pixels (Pt), (iv) determining a block of pixels (Pm) corresponding to the location (l) of the block of pixels (Pt) within the second breast region and (v) generating a report with an annotated mammography image with markings of a determined block of pixels (Pm) on the mammography image of the subject corresponding to the block of pixels (Pt) on the thermal image.

The invention discloses a metabolic marker combination for distinguishing benign mammary gland diseases and breast cancer and application thereof, and relates to the technical field of biomedicine, the metabolic marker combination for distinguishing benign mammary gland diseases and breast cancer comprises arginine, triglyceride 56: 5, glycine ursodeoxycholic acid and L-prolyl-L-threonine. The metabolic marker combination provided by the invention has extremely high sensitivity and specificity when being used for distinguishing benign breast diseases and breast cancer, is simple to operate, convenient in sample acquisition, relatively low in cost, non-invasive and high in subject compliance, and can realize accurate screening of breast cancer; and early warning and diagnosis on a functional level can be realized in an earlier stage of breast cancer occurrence, and a basis is provided for clinical early intervention. The method effectively solves the problems that the specificity is insufficient and the false positive rate is high when benign and malignant lesions of mammary glands are distinguished in the existing iconography examination.