Objectives Digital breast tomosynthesis (DBT) can detect more cancers than the current
standard breast screening method, digital mammography (DM); however, it can substantially …

The relatively recent reintroduction of deep learning has been a revolutionary force in the
interpretation of diagnostic imaging studies. However, the technology used to acquire those …

Purpose To evaluate the use of artificial intelligence (AI) to shorten digital breast
tomosynthesis (DBT) reading time while maintaining or improving accuracy. Materials and …

Background There is considerable interest in the potential use of artificial intelligence (AI)
systems in mammographic screening. However, it is essential to critically evaluate the …

Background Use of artificial intelligence (AI) as a stand-alone reader for digital
mammography (DM) or digital breast tomosynthesis (DBT) breast screening could ease …

Screening for breast cancer with mammography has been introduced in various countries
over the last 30 years, initially using analog screen-film-based systems and, over the last 20 …

Background The workflow of breast cancer screening programs could be improved given the
high workload and the high number of false-positive and false-negative assessments …

Breast cancer screening is one of the most common radiological tasks with over 39 million
exams performed each year. While breast cancer screening has been one of the most …

Digital Breast Tomosynthesis (DBT) provides three-dimensional images of the breast that
enable radiologists to discern whether densities are due to overlapping structures or lesions …

Objective To evaluate the impact of utilizing digital breast tomosynthesis (DBT) or/and full-
field digital mammography (FFDM), and different transfer learning strategies on deep …