TransReg：作为多视图乳腺X线摄影肿块检测自动配准模块的交叉Transformer
TransReg: Cross-transformer as auto-registration module for multi-view mammogram mass detection
AI总结：
受放射科医生双视图阅片流程启发，提出集成交叉Transformer的多视图乳腺肿块检测系统TransReg，在两个公开数据集上取得最优性能，验证了交叉Transformer的自动配准作用。
摘要：
筛查性乳腺X线摄影是早期乳腺癌检测中应用最广泛的方法，可显著降低死亡率。整合多视图乳腺X线摄影的信息能提升放射科医生的诊断信心并降低假阳性率，因为他们可以通过同一乳房的双视图交叉参考病灶的存在与否及位置。受此启发，我们提出了TransReg，这是一款计算机辅助检测（Computer-Aided Detection，CAD）系统，旨在利用头尾位（craniocaudal，CC）与内外斜位（mediolateral oblique，MLO）视图之间的关联。该系统包含交叉Transformer，用于对由孪生Faster RCNN网络提取的感兴趣区域（region of interest，RoIs）之间的关联进行建模，以解决肿块检测问题。我们的工作首次将交叉Transformer集成到目标检测框架中，用于对同侧视图之间的关联进行建模。我们在DDSM和VinDr-Mammo数据集上开展的实验评估表明，配备SwinT作为特征提取器的TransReg取得了当前最优性能。具体而言，在每幅图像假阳性率为0.5的条件下，采用SwinT的TransReg在DDSM数据集上的召回率为83.3%，在VinDr-Mammo数据集上的召回率为79.7%。此外，我们开展了综合分析，证明交叉Transformer可充当自动配准模块，对齐双视图中的肿块并利用该信息辅助最终预测，这是对资深放射科医生诊断工作流的复刻。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
