KEYWORDS: Digital breast tomosynthesis, Data modeling, Mammography, Breast, Computer aided diagnosis and therapy, Detection and tracking algorithms, Radiology, Image processing, Data conversion, Machine learning
The paper presents a framework for the detection of mass-like lesions in 3D digital breast tomosynthesis. It consists of several steps, including pre and post-processing, and a main detection block based on a Faster RCNN deep learning network. In addition to the framework, the paper describes different training steps to achieve better performance, including transfer learning using both mammographic and DBT data. The presented approach obtained third place in the recent DBT Lesion detection Challenge, DBTex, being the top approach without using an ensemble based method.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.