Breast Cancer (BC) is the most commonly diagnosed cancer and second leading cause of
mortality among women. About 1 in 8 US women (about 13%) will develop invasive BC …

This review gives an overview of the current state of deep learning research in breast cancer
imaging. Breast imaging plays a major role in detecting breast cancer at an earlier stage, as …

Deep learning (DL) is one of the most powerful data-driven machine-learning techniques in
artificial intelligence (AI). It can automatically learn from raw data without manual feature …

Background Background parenchymal enhancement (BPE) at breast MRI has been
associated with increased breast cancer risk in several independent studies. However …

Accurate and automatic segmentation of fibroglandular tissue in breast MRI screening is
essential for the quantification of breast density and background parenchymal …

Background Background parenchymal enhancement (BPE) is assessed on breast MRI
reports as mandated by the Breast Imaging Reporting and Data System (BI‐RADS) but is …

Background Deep learning models require large‐scale training to perform confidently, but
obtaining annotated datasets in medical imaging is challenging. Weak annotation has …

Within the past decade, background parenchymal enhancement (BPE) and background
parenchymal uptake (BPU) have emerged as novel imaging-derived biomarkers in the …

Objectives Development of automated segmentation models enabling standardized
volumetric quantification of fibroglandular tissue (FGT) from native volumes and background …

Background Parenchymal Enhancement (BPE) quantification in Dynamic Contrast-
Enhanced Magnetic Resonance Imaging (DCE-MRI) plays a pivotal role in clinical breast …