  Screening mammography is the most widely used method for early breast cancer
detection, significantly reducing mortality rates. The integration of
information from multi-view mammograms enhances radiologists' confidence and
diminishes false-positive rates since they can examine on dual-view of the same
breast to cross-reference the existence and location of the lesion. Inspired by
this, we present TransReg, a Computer-Aided Detection (CAD) system designed to
exploit the relationship between craniocaudal (CC), and mediolateral oblique
(MLO) views. The system includes cross-transformer to model the relationship
between the region of interest (RoIs) extracted by siamese Faster RCNN network
for mass detection problems. Our work is the first time cross-transformer has
been integrated into an object detection framework to model the relation
between ipsilateral views. Our experimental evaluation on DDSM and VinDr-Mammo
datasets shows that our TransReg, equipped with SwinT as a feature extractor
achieves state-of-the-art performance. Specifically, at the false positive rate
per image at 0.5, TransReg using SwinT gets a recall at 83.3% for DDSM dataset
and 79.7% for VinDr-Mammo dataset. Furthermore, we conduct a comprehensive
analysis to demonstrate that cross-transformer can function as an
auto-registration module, aligning the masses in dual-view and utilizing this
information to inform final predictions. It is a replication diagnostic
workflow of expert radiologists
