… ways in which deep learning can be best integrated into breast cancer screening workflows
… standard in breast screening. We then survey the foundations of deep learning methods in …

Purpose To develop a computerized detection system for the automatic classification of the
presence/absence of mass lesions in digital breast tomosynthesis (DBT) annotated exams, …

… in applying deep learning to … deep learning approach that (1) achieves state-of-the-art
performance in mammogram classification, (2) successfully extends to digital breast tomosynthesis …

… Our deep learning model reached breast-based sensitivity of 65% (39 of 60; 95% CI, 56%-74%)
at 2 false positives per DBT volume on a test set of 460 examinations from 418 patients. …

… A deep learning convolution neural network (DLCNN) was designed to differentiate …
image generated from the digital breast tomosynthesis volume reconstructed by a multiscale …

… breast parenchyma or by the summation of normal breast parenchyma [6]. The digital breast
tomosynthesis … The DBT generates quasi-3D tomosynthesis images via reconstruction of low…

… to deep learning we observed high utility of the latter in the analysis of digital breast tomosynthesis
… Deep feature learning is a new technology enabling rapid development of computer-…

… true breast density or the patient-specific dose. This study proposes a reconstruction
algorithm for DBT based on deep learning … This extends previous work on a deep learning-based …

… a deep learning–based AI system to single-view DBT image reading may allow for an
improvement in the reading time and in the performance of radiologists for breast cancer detection. …

… Recently, such deep learning algorithms have been applied to mammography and digital
breast tomosynthesis (DBT). In this review, the authors explain how deep learning works in the …