40 results about "Palpable mass" patented technology

The invention belongs to the technical field of medical image intelligent diagnosis, and provides a breast cancer focus benign and malignant discrimination method based on a gated multi-expert mechanism. The method comprises the following steps: firstly, carrying out standardization and semantic preprocessing on a mammary gland X-ray image, a BI-RADS imaging report and structured clinical data, embedding age, mammary gland density and focus position information into a text template in a natural language form, and realizing unified expression of multi-modal input; secondly, extracting image features by utilizing a ResNet network and a simplified CLIP model, obtaining a text semantic vector by adopting a Bio-ClinicalBERT model, and establishing two sub-paths of a lump expert and a calcification expert in a Transform structure; further, an expert weight is dynamically generated through a gating routing mechanism, and soft routing fusion is executed; and finally, outputting benign and malignant results of the breast cancer focus by the binary classification module. According to the method, deep fusion and dynamic collaboration of the mammary gland X-ray image, the BI-RADS text and the clinical information are realized, and the accuracy and interpretability of breast cancer discrimination can be remarkably improved.

The invention provides a breast lump ultrasonic image classification method and system based on deep learning, relates to the technical field of ultrasonic image classification, realizes integrated processing of various ultrasonic image types through a unified feature extraction model, improves the diagnosis efficiency, and improves the diagnosis accuracy. Meanwhile, the influence of uneven probe pressure, equipment noise interference and motion artifacts on the imaging quality is quantified based on an image quality score generated based on the physical basic characteristics, and the knowledge enhancement intensity is dynamically adjusted according to the influence, so that the pathological sensitive characteristics are accurately compensated under the clamping of a medical knowledge graph; a hierarchical decision-making mechanism is established through a two-stage cascade classification network, subtype deep analysis is started for malignant lesions on the premise that rapid diagnosis is ensured, and the recognition rate is improved; and a two-factor dynamic calibration system is formed by combining model cognitive factors based on decision logic self-consistency to provide support for efficient and accurate classification of breast lump ultrasonic images.

The invention provides a breast mass detection method and device for enhancing feature extraction, and relates to the field of image recognition, and the method comprises the steps: S1, constructing an AE-YOLO network through an ADC module, a VEB module and a YOLOv8 network; s2, obtaining a training image set, training the AE-YOLO network through the training image set, and obtaining a trained AE-YOLO network; and S3, carrying out breast mass detection through the trained AE-YOLO network. The invention provides an AE-YOLO network. The architecture of the AE-YOLO network is established on the basis of an original YOLOv8 architecture; the improvements enhance the fine-grained sampling ability of breast mass features and the ability of the model to capture global information and utilize redundant features. The AE-YOLO network is enabled to have better performance in breast mass detection, and higher detection performance is realized.

The invention relates to the technical field of ultrasonic and molybdenum target detection, in particular to an intelligent breast lump diagnosis system based on ultrasonic-molybdenum target multi-modal image and serum P16 protein detection, and the intelligent breast lump diagnosis system is characterized in that an ultrasonic image, a molybdenum target image and multi-modal data of serum P16 protein detection are fused; and optimal integration of cross-modal features is realized through a dynamic weight strategy. The ultrasonic image provides morphological and hemodynamic information of the lumps; the molybdenum target image reveals the characteristics of calcification density, calcification cluster heterogeneity, structure distortion depth and the like; serum P16 protein detection prompts malignant risks from the molecular level, and especially has important value in early recognition of triple negative breast cancer. A feature association graph is constructed through a graph neural network (GNN), the weight is dynamically adjusted in combination with a Bayesian probability integration formula, and optimal fusion of different modal features is ensured. The limitation of a single mode is made up, and the sensitivity and the specificity of diagnosis are also remarkably improved.

Systems, apparatuses, and methods are provided for patient simulators and assemblies for patient simulators. Patient simulators may include shoulder assemblies, breast assemblies, and axilla assemblies. A shoulder assembly may be configured to control positioning and orientation of a simulated arm of a patient simulator. A shoulder assembly may include a first portion connected to a simulated torso of the patient simulator and a second portion. The first portion may include a mechanical joint configured to rotate around a first axis and a second axis. A breast and axilla assembly may include an axilla portion, a breast portion, and one or more simulated masses. The breast portion may include an outer breast portion and an inner breast portion. The one or more simulated masses may be disposed within the inner breast portion or the axilla portion.

A handheld ultrasound device configured to detect a breast mass, comprises: a transducer cover having a contact area to be in contact with a user's breast and located on one end of the handheld ultrasound device; a contact part whose outer surface is located on a portion of the contact area; an ultrasound transducer unit including two or more ultrasound transducers each configured to irradiate an ultrasound signal through the outer surface of the contact part and receive the reflected ultrasound signal; a controller configured to output a control signal to control the ultrasound transducer unit and generate ultrasound image information based on the received ultrasound signal; and a communication unit configured to transmit the ultrasound image information to an external device, wherein the controller turns on only one ultrasound transducer within one cycle and generates ultrasound image information based on an ultrasound signal received from the turned-on ultrasound transducer.

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.

Disclosed herein systems, processors, or computer-readable media configured with instructions to: receive transmission and / or reflection images of a tissue of a subject, wherein the images are generated from acoustic signals derived from acoustic waveforms transmitted through the tissue; provide a set of prognostic parameters associated with a user selected region of interest; wherein the set of prognostic parameters comprises sound propagation metrics characterizing sound propagation within a tissue; wherein the set of prognostic parameters corresponds to inputs into a tissue classifier model; wherein the set of prognostic parameters comprises a plurality of subsets of related feature groupings; and determine a type of tissue of the subject based on said plurality of subsets of related feature groupings using the classifier model, wherein the type of tissue is a cancerous tumor, a fibroadenoma, a cyst, a nonspecific benign mass, and an unidentifiable mass.

The present invention provides a method and system for breast mass ultrasound image classification based on deep learning, which relates to the technical field of ultrasound image classification. The present invention realizes integrated processing of multiple ultrasound image types through a unified feature extraction model, thereby improving diagnostic efficiency. At the same time, the image quality score generated based on physical basic features quantifies the impact of probe pressure unevenness, equipment noise interference and motion artifacts on imaging quality, and dynamically adjusts the knowledge enhancement strength accordingly so that pathological sensitive features can be accurately compensated with the support of medical knowledge graphs. A two-level cascade classification network establishes a hierarchical decision-making mechanism to initiate subtype deep analysis of malignant lesions under the premise of ensuring rapid diagnosis, thereby improving the recognition rate. Further, through the image reliability factor reflecting the credibility of equipment acquisition, a model cognitive factor based on the self-consistency of decision logic is combined to form a dual-factor dynamic calibration system to provide support for efficient and accurate classification of breast mass ultrasound images.

To provide a lump palpation model which allows a user to experience the simulated sensation of breast cancer lumps by gently pressing and stroking the model with fingers, and which can be handily used while taking a bath, etc.SOLUTION: A lump palpation model 1 for allowing a user to experience the simulated feel of lumps present inside a body through tactile sensation is provided, the lump palpation model comprising a main body 2 having a mounting surface 21 for mounting the model and a touching surface 20 to be touched for a simulated experience, and simulated lumps 3 simulating lumps embedded in the main body 2, and being configured to float on water. The mounting surface 21 has an adhesive surface that can be adhered to a wall or the like.SELECTED DRAWING: Figure 2

A handheld ultrasound device configured to detect a breast mass includes: a transducer cover having a contact area to be in contact with a user's breast and located on one end of the handheld ultrasound device; a contact part having a contact area to be in contact with a user's breast and located on one end of the handheld ultrasound device; an ultrasound transducer unit including one or more ultrasound transducers each configured to irradiate an ultrasound signal through the outer surface of the contact part and receive the reflected ultrasound signal; a movement speed sensing unit configured to sense a movement speed of the contact area and generate movement speed information; a controller configured to output a control signal to control the ultrasound transducer unit and generate ultrasound images based on the received ultrasound signal; and a communication unit configured to transmit the ultrasound images to an external device.

The application discloses a breast lump segmentation method and system, comprising the following steps: step 1, obtaining the medical image of the breast lump of a patient, pre-processing the medical image, and labeling the pre-processed remote sensing image to make a data set; step 2, constructing an MM-3DUNet model for segmenting the breast lump, training the MM-3DUNet model by using the data set, and obtaining an optimized MM-3DUNet model; and step 3, segmenting the breast lump of the medical image by using the optimized MM-3DUNet model. The application reduces the calculation complexity of the model, and improves the calculation efficiency and segmentation accuracy.

It would be advantageous with the close quarters in breast procedure rooms to create a Gamma Probe system incorporating multi modalities, sensitivities, and multiple ranges for utilization in breast intervention procedures for the diagnosis and verification for breast cancer. Systems for specimen radiography assist radiologists and surgeons in the detection and classification of abnormal lesions in medical images gleaned from breast biopsies / lumpectomies and a Gamma Probe system with a sensor to detect radioactive, metallic / magnet, or RFID waves to help locate a lump or abnormality which were placed by an Interventional Radiologist. Radio guided surgery (RGS) uses gamma probes to localize radioactive targets. The three common RGS radioisotopes: technetium-99m (99mTc), indium-111 (111In), and iodine-125 (125I). The aspects of the disclosed embodiments pertain to a gamma probe that is able to simultaneously detect 2-3 isotopes automatically without actuating a physical switch.

The invention relates to the technical field of breast ultrasonic image recognition, in particular to an ultrasonic puncture part anomaly detection method and system based on image processing. Acquiring all similar pixel points of the reference pixel points in the breast ultrasound image of the patient; and calculating a spatial coherence coefficient according to the distance distribution between the reference point and each similar point, the number of the similar points and the regional distribution characteristics. And identifying a high-echo region in combination with gray level distribution of the pixel points. For each high echo region, detecting a mark inflection point and a non-mark inflection point; calculating an abnormal extension coefficient according to the position distribution of the mark points and the non-mark points; and obtaining an abnormal factor, and screening abnormal inflection points. The clustering abnormal inflection point forms an abnormal clustering cluster based on an abnormal extension coefficient and an abnormal factor; and performing contour fitting on the abnormal inflection points in each cluster to obtain a suspected lump area. According to the method, the suspected lump area can be accurately identified, so that the detection efficiency and accuracy of related personnel are improved.

The invention discloses a breast lump segmentation method and system, and the method comprises the following steps: 1, obtaining a medical image of a breast lump of a patient, carrying out the preprocessing of the medical image, and carrying out the marking of a preprocessed remote sensing image, and making a data set; step 2, constructing an MM-3DUNet model used for segmenting the breast lumps, and training the MM-3DUNet model by adopting the data set, so as to obtain an optimized MM-3DUNet model; and step 3, carrying out breast lump segmentation on the medical image by adopting the optimized MM-3DUNet model. According to the method, the calculation complexity of the model is reduced, and the calculation efficiency and the segmentation precision are improved.

The present invention provides a breast mass image segmentation method and system based on pruned U-Net++, which relates to the technical field of ultrasonic image segmentation. The breast mass image segmentation model of pruned U-Net++ constructed in the present invention is based on the U-Net++ network, uses skip connections to fuse the feature expressions of each branch U-Net, and introduces residual connections to replace the dense connections in the U-Net++ method, and uses this model to segment the obtained original breast ultrasound images. The pruned U-Net++ breast ultrasound image segmentation model constructed in the present invention can avoid the problem of model overfitting caused by the small data set problem in medical image data when segmenting the masses in breast ultrasound images, and improves the generalization ability of the ultrasound image breast mass segmentation model; at the same time, the scale of the model parameters is much smaller than that of the prior art, reducing the complexity of the network model, shortening the calculation time of network training, reducing the memory occupation of network training, and improving the model training efficiency.

This invention discloses a lightweight automatic MRI segmentation method for breast masses based on MobileSAM. The method inputs the breast MRI image to be detected into a breast mass MRI image segmentation model, outputting the localization and prediction results of the breast mass. The model uses the MobileSAM lightweight image coding network as its backbone, and introduces an adapter to achieve adaptation and efficient fine-tuning of the breast image domain. A separable hollow pyramid fusion module is designed in the decoding branch, employing multi-branch deep separable hollow convolution to aggregate multi-scale context, enhancing the feature discrimination of small masses and blurred boundaries. Furthermore, a coordinate-channel-space joint attention gating module is introduced, using decoded semantics as a guide to filter skip features, strengthening the spatial location and detail reconstruction of the mass boundary. This invention achieves end-to-end automatic segmentation without interactive prompts, significantly improving the accuracy and robustness of breast MRI mass segmentation while maintaining lightweight design and low training costs.

This invention discloses a method for constructing a lung weight index based on chest CT images and its application. The method includes: Step 1, low-dose CT image acquisition and preprocessing; Step 2, automatic segmentation of the lung and lung lobes; Step 3, calculation of the average density of lung lobes and conversion to physical density; Step 4, calculation of lung lobe volume; Step 5, calculation of lung lobe weight and whole lung weight; Step 6, construction of the lung weight index; Step 7, risk assessment based on the lung weight index. This method can be applied to practical scenarios such as lung cancer screening, health checkups, and image-assisted diagnosis. This invention comprehensively reflects the state of lung tissue, blood vessels, and interstitium through the lung weight index, providing a more holistic quantitative description. Furthermore, it does not require nodule detection as a necessary prerequisite, making it suitable for individuals with few nodules, small volume, or those who have not yet formed a definite mass, thus better meeting the practical needs of early lung cancer screening.

A method to form images of internal parts of objects that are of such a nature that images are smeared out due to scattering. The method consists in using isolated beams of radiation along specific, chosen directions, then separating radiation that have not suffered scattering events, from the radiation that have suffered scattering events. The directions may be fixed either by having a multiplicity of radiation emitters along several directions or by moving one or more radiation emitter, or a combination of these. Collimators may also be used to select the direction of radiation propagation. The invention also discloses transparent windows pressed against the object, which serves to keep the incident radiation along a known direction. The radiation that suffered scattering may be also used to make a separate images of the internal parts of the object.

The invention discloses a method and a system for distinguishing benign and malignant ovarian lumps based on RegNet of a vaginal ultrasound image in combination with triple ROI constraints. According to the method, RegNetY-032 is adopted as a backbone network, an intelligent group level processing strategy and a four-level enhancement mechanism are combined, and it is ensured that a model focuses on ROI through triple constraints of input level ROI mask, feature level ROI constraint and ROI outer weight penalty loss. The device domain self-adaption technology based on the gradient inversion layer is further achieved, and the problem of imaging difference between different ultrasonic devices is effectively solved. According to the system, multi-angle image fusion is carried out by adopting a self-attention mechanism, the area of a subject under a working characteristic curve ROC in external verification reaches 0.992, the accuracy reaches 95.9%, and the system has good cross-equipment generalization ability and clinical application value.

The present invention relates to the technical field of puncture models, and specifically discloses an ultrasonic-guided puncture training model for the imaging department, which includes a puncture needle and an ultrasonic probe, and further includes: a model component, which is arranged on one side of the display device and is used to simulate the puncture of the abdominal model; an auxiliary component, which is arranged on the model component and is used to assist the further puncture simulation of the model component. Through the model component of the present invention, the degree of abdominal effusion and the different positions of the upper abdomen, middle abdomen or lower abdomen where the effusion is located can be randomly combined for simulation, and after the puncture depth reaches the position where the effusion is located, a prompt can be given, which is beneficial to improving the training accuracy of the training personnel during the puncture drainage of abdominal effusion; through the auxiliary component, the puncture training of abdominal masses can be simulated, and it can be combined with the model component to randomly simulate the different positions where the abdominal masses are located.

A portable ultrasonic device for detecting a mass in a breast according to an embodiment of the present invention comprises: a contact part which includes a contact region in contact with the breast of a user and is positioned at one end of the portable ultrasonic device; a transducer cover, the outer surface of which is located in at least a part of the contact area; an ultrasonic transducer unit including N (N is a natural number of 1 or more) ultrasonic transducers that emit an ultrasonic signal through the outer surface of the transducer cover and receive the reflected ultrasonic signal; a movement speed detection unit that detects a movement speed at which the contact region is in contact with the breast and moves, and generates movement speed information; a control unit that outputs a control signal for controlling the ultrasonic transducer unit and generates ultrasonic image information on the basis of the received ultrasonic signal; and a communication unit that transmits the ultrasound image information to an external terminal.

It would be advantageous with the close quarters in breast procedure rooms to create a Gamma Probe system incorporating multi modalities, sensitivities, and multiple ranges for utilization in breast intervention procedures for the diagnosis and verification for breast cancer. Systems for specimen radiography assist radiologists and surgeons in the detection and classification of abnormal lesions in medical images gleaned from breast biopsies / lumpectomies and a Gamma Probe system with a sensor to detect radioactive, metallic / magnet, or RFID waves to help locate a lump or abnormality which were placed by an Interventional Radiologist. Radio guided surgery (RGS) uses gamma probes to localize radioactive targets. The three common RGS radioisotopes: technetium-99m (99mTc), indium-111 (111In), and iodine-125 (125I). The aspects of the disclosed embodiments pertain to a gamma probe that is able to simultaneously detect 2-3 isotopes automatically without actuating a physical switch.

It would be advantageous with the close quarters in breast procedure rooms to create a Gamma Probe system incorporating multi modalities, sensitivities, and multiple ranges for utilization in breast intervention procedures for the diagnosis and verification for breast cancer. Systems for specimen radiography assist radiologists and surgeons in the detection and classification of abnormal lesions in medical images gleaned from breast biopsies / lumpectomies and a Gamma Probe system with a sensor to detect radioactive, metallic / magnet, or RFID waves to help locate a lump or abnormality which were placed by an Interventional Radiologist. Radio guided surgery (RGS) uses gamma probes to localize radioactive targets. The three common RGS radioisotopes: technetium-99m (99mTc), indium-111 (111In), and iodine-125 (125I). The aspects of the disclosed embodiments pertain to a gamma probe that is able to simultaneously detect 2-3 isotopes automatically without actuating a physical switch.

The application discloses a breast lump detection system based on multi-view images, comprising: a data import module for loading multi-view images of a breast and pre-processing data; a feature extraction module for extracting image features of a main view and an auxiliary view; a region extraction module for obtaining a candidate region of a lump in the main view and ROI features, a Patch position and Patch features in the auxiliary view; an information fusion module for fusing the ROI and the Patch by weighted fusion to obtain enhanced ROI visual features; and a lump detection module for classifying and position regression according to the enhanced ROI visual features to obtain a lump detection result of the main view. The application simulates the thinking of a doctor analyzing multi-view images, constructs a lump detection system based on multi-view images, effectively fuses the information of multiple views, and improves the detection rate of breast lumps and reduces the false positive rate.

The invention discloses a sampling device for breast tumor screening, which is applied to the field of sampling instruments, and adopts double ultrasonic probes which are vertically distributed to detect a lump, a horizontally arranged annular ultrasonic probe to detect the position of the lump, and a vertically arranged rectangular ultrasonic probe to detect the depth of the lump. Compared with the arrangement of a single probe in the prior art, the annular ultrasonic probe and the rectangular ultrasonic probe achieve three-dimensional detection of the lumps, so that accurate positioning of the lumps is achieved, puncture deviation, improper puncture and even penetration of the lumps are avoided, the multi-degree-of-freedom mechanical arm and the Y-shaped support are used for supporting the biopsy needle body, and the biopsy efficiency is improved. Puncture errors caused by handheld operation are avoided, the success rate of one-time puncture is effectively improved, the needle core of the biopsy needle body is magnetized, the needle core is detected through a magnetic detector, it can be ensured that the needle core punctures the middle position of a lump, and sampling failure caused by penetrating through the lump is prevented.

A device and a method for screening for the presence of lumps in breast tissue as well as the characterization of any discovered lumps including, but not limited to, location, size, shape, hardness, and movability.

The invention provides an intraoperative positioning system based on ultrasonic breast tumor rotary atherectomy, and relates to the technical field of medical image intelligent assistance. The system comprises a training data acquisition module, a target detection model construction module, a model training module and a target detection module instruction starting module. The training data acquisition module is used for acquiring effective training data based on an ultrasonic breast tumor rotary cutting operation video to construct a basic data set; the target detection model construction module constructs a target detection model based on an improved YOLOV11 network, and detects and tracks the positions of a lump and a cutter groove in an ultrasonic video stream in real time; the model training module is used for training a target detection model; the target detection module is used for detecting an ultrasonic breast tumor rotary cutting operation video frame and detecting and tracking the positions of a lump and a knife groove in an ultrasonic video stream in real time; the instruction starting module judges whether a rotary cutter starting instruction is sent out or not based on the positions of a lump and a cutter groove in the ultrasonic video stream.

A portable ultrasonic device for detecting a mass in a breast according to an embodiment of the present invention comprises: a contact part which includes a contact region in contact with the breast of a user and is positioned at one end of the portable ultrasonic device; a transducer cover, the outer surface of which is located in at least a part of the contact area; an ultrasonic transducer unit including N (N is a natural number of 1 or more) ultrasonic transducers that emit an ultrasonic signal through the outer surface of the transducer cover and receive the reflected ultrasonic signal; a movement speed detection unit that detects a movement speed at which the contact region is in contact with the breast and moves, and generates movement speed information; a control unit that outputs a control signal for controlling the ultrasonic transducer unit and generates ultrasonic image information on the basis of the received ultrasonic signal; and a communication unit that transmits the ultrasound image information to an external terminal.

The present invention provides a breast cancer mass diagnosis system based on a dynamic ensemble selection algorithm, which relates to the field of medical technology. In the present invention, a training module is used to obtain a base classifier pool according to a training set by combining homogeneous classifier generation and heterogeneous classifier generation; a generation module is used to obtain a dynamic ensemble data set corresponding to a test set according to the test set and the base classifier pool; an acquisition module is used to obtain the dynamic ability region of any sample in the dynamic ensemble data set according to a dynamic selection set and the dynamic ensemble data set; a classification module is used to select a preset number of classifiers according to the base classifier pool and the dynamic ability region by using a complementary index, and obtain the diagnostic classification results corresponding to the current sample in the dynamic ensemble data set in each selected classifier. A base classifier pool of the DES algorithm is generated by combining homogeneous classifier generation and heterogeneous classifier generation; and by proposing a dynamic ability region (DROC) algorithm, the diagnostic accuracy of breast cancer masses is improved.