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Screening mammography is the most widely used method for early breast cancerdetection, significantly reducing mortality rates. The integration ofinformation from multi-view mammograms enhances radiologists' confidence anddiminishes false-positive rates since they can examine on dual-view of the samebreast to cross-reference the existence and location of the lesion. Inspired bythis, we present TransReg, a Computer-Aided Detection (CAD) system designed toexploit the relationship between craniocaudal (CC), and mediolateral oblique(MLO) views. The system includes cross-transformer to model the relationshipbetween the region of interest (RoIs) extracted by siamese Faster RCNN networkfor mass detection problems. Our work is the first time cross-transformer hasbeen integrated into an object detection framework to model the relationbetween ipsilateral views. Our experimental evaluation on DDSM and VinDr-Mammodatasets shows that our TransReg, equipped with SwinT as a feature extractorachieves state-of-the-art performance. Specifically, at the false positive rateper image at 0.5, TransReg using SwinT gets a recall at 83.3% for DDSM datasetand 79.7% for VinDr-Mammo dataset. Furthermore, we conduct a comprehensiveanalysis to demonstrate that cross-transformer can function as anauto-registration module, aligning the masses in dual-view and utilizing thisinformation to inform final predictions. It is a replication diagnosticworkflow of expert radiologists

TransReg: Cross-transformer as auto-registration module for multi-view mammogram mass detection