10 results about "Mammography" patented technology

The utility model discloses a kind of compressor identification detection structure and breast machine for breast machine.The described compressor identification detection structure includes photoelectric detection module and compressor, the photoelectric detection module is installed in the installation port, the compressor is provided with the lead-in seat compatible with the installation port, the lead-in seat is provided with locking module and detection identification board, the locking module is locked in the installation port when the lead-in seat is inserted into the installation port, the detection identification board is detected by the photoelectric detection module when the lead-in seat is inserted into the installation port;Breast machine includes breast machine host, the breast machine host is provided with riser and bearing table, the bearing table is located below the riser, the riser is provided with installation port, the installation port is provided with the described identification detection structure, and the breast machine host is electrically connected with remote control system. The utility model is applied to the technical field of breast machine.

Examples relate to a method comprising using a non-target tissue model to virtually purify an experimental electrical impedance signal with respect to target tissue. A method comprisingobtain a purifying impedance transfer function based on an electrical impedance model with one or more non-target tissue components and without a target tissue component; andpurifying an experimental electrical impedance signal using the purifying impedance transfer function to obtain a virtually purified signal for the target tissue component. A method comprising: in dependence upon age and / or a measure of obesity, access an impedance transfer function dependent upon non-tumor tissue; modify an experimental electrical impedance signal using the obtained impedance transfer function to obtain a signal less dependent upon non-tumor tissue and more dependent upon tumor tissue impedance.

This invention relates to the field of medical technology and discloses a self-supervised breast cancer classification method, comprising the following steps: S1: Image acquisition and dataset creation: acquiring contrast-enhanced spectral images of different modalities and multimodal contrast-enhanced spectral mammography images; S2: Data preprocessing: preprocessing the multimodal contrast-enhanced spectral mammography images, cropping the images, and performing pixel mean squarening; S3: Image feature extraction: extracting image features using a Vision Transformer model and providing image category predictions; S4: Loss calculation: calculating the distortion entropy of the predicted classification result and the information entropy loss between the category confidences generated by the classification branches of the two spectral images. This invention improves speed while ensuring accuracy, balancing the relationship between accuracy and efficiency, and proposes a breast cancer image classification model with high accuracy, fast speed, strong robustness, and good adaptability.

A medical image acquisition apparatus including: a biopsy apparatus; an imaging table that is used to capture an image of a living body disposed on an imaging surface with an imaging apparatus; a projector that projects the image; and at least one processor, wherein the processor performs control of projecting relevant information associated with each execution stage of a biopsy that is performed on the living body using the biopsy apparatus, from the projector toward the imaging surface of the imaging table for each execution stage.

A breast rest for a mammography device includes a front panel on a patient-facing side of the breast rest and a top panel having a concavity configured to accommodate a breast of a patient. A transition from the front panel to the top panel can be curved. The breast rest can be included in the mammography device. The breast rest can further include side panels. The breast rest can further include an open end to receive a breast test instrument. Multiple sizes and / or shapes of the breast rest can be provided and selected from based on patient data. Breast rests can be manufactured for particular patients having sizes and / or shapes determined from patient data.

A hybrid quantum computing system for the multimodal detection and classification of breast cancer, comprising: an input module configured to receive multimodal medical breast images containing at least one of the following features: mammography images, ultrasound images, and / or histopathological images; a preprocessing module configured to perform noise reduction, normalization, and segmentation of relevant areas on the received multimodal medical breast images; a feature extraction module comprising a hybrid quantum-classical convolutional neural network-based deep feature extractor configured to extract deep features from the preprocessed images, and a handcrafted feature extractor configured to extract handcrafted features encompassing texture features, shape features, and statistical features from the preprocessed images;a quantum-optimized feature fusion module configured to encode the deep features and hand-crafted features into a quantum-inspired representation, apply superposition-based transformations to the encoded features, and apply entanglement-based transformations to the encoded features to generate fused features; a classification module consisting of a hybrid neural classifier configured to process the fused features to predict a diagnostic classification of benign or malignant tissue; and an output module connected to a user interface and configured to provide a diagnostic decision and confidence score based on the predicted diagnostic classification, which are then displayed via the user interface.

Systems and methods for providing a visual representation output descriptive of a preliminary diagnosis can include obtaining radiograph data, processing the radiograph data with a machine-learned model to generate one or more classification outputs, and generating the visual representation output based on the one or more classification outputs. The visual representation output can be generated such that the visual representation output can be provided for display on a plurality of different display types with a plurality of different technical capabilities.

This application relates to an active decompression device, a compression device, and a mammography device. The active decompression device includes a detection and control circuit, an energy source, and a discharge circuit. The detection and control circuit is connected to an external power supply and the discharge circuit, and the discharge circuit is also connected to the energy source. The detection and control circuit is used to acquire the compression force signal of the compressed object, and, when the compression force signal meets the preset force signal requirements, controls the discharge circuit to conduct based on the compression force signal, and detects whether the external power supply is cut off. The energy source is used to supply power to the detection and control circuit when the external power supply is cut off. The detection and control circuit is also used to control the lifting and lowering of the compression plate in the compression device according to the compression force signal. In the embodiments of this application, an additional energy source is designed into the compression device. During the compression process using the compression device, if the external power supply is detected to be cut off and there is compression force, the compression plate can be actively decompressed.

Exemplary systems, methods and Computer-accessible medium according to the exemplary embodiments of the present disclosure can provide a Multi-modal Transformer (MMT), a neural network that synergistically utilizes mammography and ultrasound to identify existing cancers and estimate future cancer risk. MMT aggregates multi-modal data through self-attention and modeling temporal tissue changes by comparing current exams to prior imaging. Thus, exemplary method, system and computer-accessible medium can be provided for detecting cancer. with which it possible to receive, with an artificial intelligence (AI) procedure, a plurality of scanning images associated with multiple modalities for at least one portion of a body, train the AI procedure on a multi-modal image dataset based on the plurality of scanning images, and predict, by the trained AI procedure, an existence of the cancer based on the multiple modalities of the plurality of scanning images.

Methods and systems are provided for automatically assessing a level of BPE of a patient of an imaging system based on one or more medical images of one or more breasts of the patient, using a deep learning (DL) model. The medical images may include contrast enhanced mammography (CEM) images, magnetic resonance (MR) images, or a different type of images. The one or more images may include one or more images of a same breast, where the BPE assessment outputted by the DL model may include a score, such as a percentage of BPE detected in the images. The one or more images may include images of a left breast and a right breast of the patient, where the BPE assessment outputted by the DL model may include whether an asymmetry between BPE assessments of the left breast and the right breast is detected.