9 results about "Breast density" 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 discloses an intelligent breast cancer detection system based on deep learning, and relates to the field of deep learning and intelligent detection. Comprising an image acquisition and preprocessing module, a multi-modal data set construction module, a multi-task classification module, a breast cancer intelligent detection module, an evaluation and feedback module, a user efficient interaction module and a multi-task classification module. Richer prediction information is provided for benign and malignant detection of the breast cancer, and the clinical application effect of the intelligent breast cancer detection system can be improved. According to the invention, the performance of a breast cancer detection system is greatly improved, especially the accuracy and efficiency of focus positioning, feature extraction and classification are improved, and the practicability of the system in clinical auxiliary diagnosis is greatly improved.

The invention relates to a method (100) for determining breast density (Dm) from a first digital representation (BGR, BGR') of a breast, which method comprises: - a step (110) of obtaining a second digital representation (FGT) of a mammary gland; - a step (120) of determining the gland volume (Vg) from the second representation (FGT); - a step (130) of determining the breast volume (Vm) from the first representation (BGR, BGR'); - a step (140) of producing first and second morphological features (Qi1, Qi2) of interest of the mammary gland, the first and second morphological features (Qi1, Qi2) of interest being different from the breast volume (Vm) and mammary gland volume (Vg) only; - a step (150) of producing the breast density (Dm) from the first and second morphological features (Qi1, Qi2).

A computer implemented method of estimating tissue density of a body part of a subject from a medical image of tissue of the body part of the subject includes receiving at least one medical image of tissue of the body part of the subject, the received medical image being from a first imaging modality or a second imaging modality, estimating tissue density of the body part from the at least one medical image using a deep-learning model pretrained using images from the first imaging modality and images from the second imaging modality, and outputting an estimate of tissue density of the body part of the subject based on the tissue density estimated by the deep-learning model.

The application discloses a kind of breast cancer intelligent detection systems based on deep learning, it is related to deep learning and intelligent detection field;Including image acquisition and pretreatment module, multi-modal data set construction module, multi-task classification module, breast cancer intelligent detection module, evaluation and feedback module, user efficient interaction module, multi-task classification module, by integrating attention mechanism and multi-task learning method, system not only improves the accuracy of breast density classification, also provides more abundant prediction information for benign and malignant detection of breast cancer, help to improve the application effect of breast cancer intelligent detection system in clinic.The application greatly improves the performance of breast cancer detection system, especially in the accuracy and efficiency of lesion positioning, feature extraction and classification, greatly improves the practicability of the system in clinical auxiliary diagnosis.

The embodiment of the invention provides a breast density quantification method and an ultrasonic imaging system. The method comprises the following steps: performing ultrasonic scanning on a breast to obtain an ultrasonic image about the breast; determining the volume of the mammary gland and the volume of the breast according to the ultrasonic image; and determining the density of the breast according to the volume of the mammary gland and the volume of the breast. According to the embodiment of the invention, the breast density is quantified by acquiring the three-dimensional volume data of the breast and detecting the glandular milk tissue and the chest wall tissue from the three-dimensional volume data; optionally, the distribution of the breast density can be visually displayed, and more information is provided for clinical application, such as lesion detection navigation and the like.

The invention discloses a dynamic mammary gland density risk assessment method based on AI ultrasound, and belongs to the technical field of mammary gland risk assessment, and the method specifically comprises the steps: obtaining a multi-temporal mammary gland ultrasound image with a physiological cycle timestamp, and building a unified space-time coordinate system through the elastic registration of an anatomical structure reference point; gland morphological features are extracted through a deep convolutional neural network, and a spatial density feature map corresponding to each time node is generated; constructing a mammary gland tissue space-time evolution topological graph by taking the local features as vertexes and taking the deformation field and the feature similarity as edges; fusing space structure and time change information by using a space-time diagram convolutional neural network to obtain a dynamic evolution trajectory vector of hyperplasia and degeneration; and comparing the trajectory vector with a healthy population reference line, calculating a space-time deviation degree to obtain a dynamic risk coefficient, finally constructing a full life cycle prediction model, and outputting a dynamic mammary gland density risk assessment report.

This specification provides a breast imaging method, system, and storage medium. The method includes: controlling a breast scanning device to compress a target breast to obtain a pressure distribution map of the target breast, the pressure distribution map being acquired by a pressure sensor on the compressed target breast; determining lesion identification results and breast density of the target breast based on the pressure distribution map; determining target scanning parameters based on the lesion identification results and breast density; and controlling the breast scanning device to perform a target scan of the target breast based on the target scanning parameters.

The invention relates to a method (100) for determining breast density (Dm) from a first digital representation (BGR, BGR') of a breast, comprising:- a step (110) of obtaining a second digital representation (FGT) of the mammary gland;- a step (120) of determining the gland volume (Vg) from said second representation (FGT);- a step (130) of determining the breast volume (Vm) from said first representation (BGR, BGR');- a step (140) of producing first and second morphological characteristics (Qi1, Qi2) of interest of the mammary gland, the first and second morphological characteristics (Qi1, Qi2) of interest being distinct;- a step (150) of producing the breast density (Dm) from the first and second morphological characteristics (Qi1, Qi2). Figure for abstract: Fig 5