  Computer-aided detection systems based on deep learning have shown good
performance in breast cancer detection. However, high-density breasts show
poorer detection performance since dense tissues can mask or even simulate
masses. Therefore, the sensitivity of mammography for breast cancer detection
can be reduced by more than 20% in dense breasts. Additionally, extremely dense
cases reported an increased risk of cancer compared to low-density breasts.
This study aims to improve the mass detection performance in highdensity
breasts using synthetic high-density full-field digital mammograms (FFDM) as
data augmentation during breast mass detection model training. To this end, a
total of five cycle-consistent GAN (CycleGAN) models using three FFDM datasets
were trained for low-to-high-density image translation in highresolution
mammograms. The training images were split by breast density BIRADS categories,
being BI-RADS A almost entirely fatty and BI-RADS D extremely dense breasts.
Our results showed that the proposed data augmentation technique improved the
sensitivity and precision of mass detection in models trained with small
datasets and improved the domain generalization of the models trained with
large databases. In addition, the clinical realism of the synthetic images was
evaluated in a reader study involving two expert radiologists and one surgical
oncologist.
