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

翻译：筛查性乳腺X光摄影是早期乳腺癌检测最广泛应用的方法，可显著降低死亡率。多视图乳腺X光影像的信息整合能提高放射科医生的诊断信心并降低假阳性率，因为医生可通过同一乳房的双视角图像交叉验证病变的存在与位置。受此启发，我们提出TransReg——一种计算机辅助检测（CAD）系统，旨在利用头尾位（CC）和内外斜位（MLO）视图之间的关系。该系统包含交叉变换器（cross-transformer），用于建模由孪生Faster RCNN网络提取的感兴趣区域（RoI）在肿块检测任务中的关系。本研究首次将交叉变换器集成到目标检测框架中，以建模同侧视图间的关联。在DDSM和VinDr-Mammo数据集上的实验评估表明，采用SwinT作为特征提取器的TransReg达到了最先进性能。具体而言，在每张图像假阳性率为0.5时，使用SwinT的TransReg在DDSM数据集上召回率达83.3%，在VinDr-Mammo数据集上达79.7%。此外，我们通过全面分析证明交叉变换器可作为自动配准模块，对齐双视角中的肿块并利用此信息指导最终预测。该流程复现了资深放射科专家的诊断工作模式。