4 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.

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 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.