Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network.

ABSTRACT: Several computer aided diagnosis (CAD) systems have been developed for mammography. They are widely used in certain countries such as the U.S. where mammography studies are conducted more frequently; however, they are not yet globally employed for clinical use due to their inconsistent performance, which can be attributed to their reliance on hand-crafted features. It is difficult to use hand-crafted features for mammogram images that vary due to factors such as the breast density of patients and differences in imaging devices. To address these problems, several studies have leveraged a deep convolutional neural network that does not require hand-crafted features. Among the recent object detectors, RetinaNet is particularly promising as it is a simpler one-stage object detector that is fas

SUBMITTER: Jung H 

PROVIDER: S-EPMC6143189 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature