TransReg: Cross-transformer as auto-registration module for multi-view
  mammogram mass detection
Hoang C. Nguyen Chi Phan Hieu H. Pham
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