Potentially suspicious breast neoplasms could be masked by high tissue density, thus
increasing the probability of a false‑negative diagnosis. Furthermore, differentiating breast …

Simple Summary The DL model predictions in automated breast density assessment were
independent of the imaging technologies, moderately or substantially agreed with the …

Purpose To develop a deep learning (DL) algorithm to assess mammographic breast
density. Materials and Methods In this retrospective study, a deep convolutional neural …

Breast cancer is the leading cause of death for women in most countries. The high levels of
mortality relate mostly to late diagnosis and to the direct proportionally relationship between …

Purpose Mammographic breast density is an established risk marker for breast cancer and
is visually assessed by radiologists in routine mammogram image reading, using four …

Objective: High breast density is a risk factor for breast cancer. The aim of this study was to
develop a deep convolutional neural network (dCNN) for the automatic classification of …

Breast cancer and mammographic breast density are strongly associated with each other.
As breast density increases, the chance of masking breast cancer also increases which …

Objective We developed deep learning algorithms to automatically assess BI-RADS breast
density. Methods Using a large multi-institution patient cohort of 108,230 digital screening …

Breast density estimation with visual evaluation is still challenging due to low contrast and
significant fluctuations in the mammograms' fatty tissue background. The primary key to …

Background and objective: Breast density (BD) is an independent predictor of breast cancer
risk factor. The automatic classification of BD has yet to resolve. In this paper, we propose an …