  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 studied medical imaging applications of artificial
intelligence, the development and evaluation of the algorithms are hindered due
to the lack of well-annotated large-scale publicly available datasets. This is
particularly an issue for digital breast tomosynthesis (DBT) which is a
relatively new breast cancer screening modality. We have curated and made
publicly available a large-scale dataset of digital breast tomosynthesis
images. It contains 22,032 reconstructed DBT volumes belonging to 5,610 studies
from 5,060 patients. This included four groups: (1) 5,129 normal studies, (2)
280 studies where additional imaging was needed but no biopsy was performed,
(3) 112 benign biopsied studies, and (4) 89 studies with cancer. Our dataset
included masses and architectural distortions which were annotated by two
experienced radiologists. Additionally, we developed a single-phase deep
learning detection model and tested it using our dataset to serve as a baseline
for future research. Our model reached a sensitivity of 65% at 2 false
positives per breast. Our large, diverse, and highly-curated dataset will
facilitate development and evaluation of AI algorithms for breast cancer
screening through providing data for training as well as common set of cases
for model validation. The performance of the model developed in our study shows
that the task remains challenging and will serve as a baseline for future model
development.
