BACKGROUND: Breast magnetic resonance imaging (MRI) protocols often include T2-weighted fat-saturated (T2w-FS) sequences, which support tissue characterization but significantly increase scan time. This study aims to evaluate whether a 2D-U-Net neura...
Enduring studies in the field of early breast cancer screening are investigating the use of multispectral transmission imaging. The frame accumulation system handles multispectral transmission images with deprived grayscale and unsatisfactory resolut...
Breast cancer is the most prevalent cancer and the second cause of cancer related death among women in the United States. Accurate and early detection of breast cancer can reduce the number of mortalities. Recent works explore deep learning technique...
Background Limited data are available regarding the accuracy of artificial intelligence (AI) algorithms trained on bilateral mammograms for second breast cancer surveillance in patients with a personal history of breast cancer treated with unilateral...
To investigate the potential of employing artificial intelligence (AI) -driven breast ultrasound analysis models for the classification of glandular tissue components (GTC) in dense breast tissue. A total of 1,848 healthy women with mammograms classi...
This study investigated a series of deep learning (DL) models for the objective assessment of four categories of mammographic breast density (e.g., fatty, scattered, heterogeneously dense, and extremely dense). A retrospective analysis was conducted ...
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
40184647
PURPOSE: Breast cancer remains a significant cause of mortality among women globally, highlighting the critical need for accurate diagnosis. Although Convolutional Neural Networks (CNNs) have shown effectiveness in segmenting breast ultrasound images...
Purpose To build a deep learning framework using contrast-enhanced MRI for lesion segmentation and automatic molecular subtype classification in breast cancer. Materials and Methods This retrospective multicenter study included patients with biopsy-p...
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
40138245
Ultrasound computed tomography (USCT) is a promising technique for breast cancer detection because of its quantitative imaging capability of the speed of sound (SOS) of soft tissues and the fact that malignant breast lesions often have a higher SOS c...
BACKGROUND: This study aimed to develop a BI-RADS network (DL-UM) via integrating ultrasound (US) and mammography (MG) images and explore its performance in improving breast lesion diagnosis and management when collaborating with radiologists, partic...