Detecting mass in mammogram is significant due to the high occurrence and mortality of breast cancer. In mammogram mass detection, modeling pairwise lesion correspondence explicitly is particularly important. However, most of the existing methods build relatively coarse correspondence and have not utilized correspondence supervision. In this paper, we propose a new transformer-based framework CL-Net to learn lesion detection and pairwise correspondence in an end-to-end manner. In CL-Net, View-Interactive Lesion Detector is proposed to achieve dynamic interaction across candidates of cross views, while Lesion Linker employs the correspondence supervision to guide the interaction process more accurately. The combination of these two designs accomplishes precise understanding of pairwise lesion correspondence for mammograms. Experiments show that CL-Net yields state-of-the-art performance on the public DDSM dataset and our in-house dataset. Moreover, it outperforms previous methods by a large margin in low FPI regime.

翻译：乳房X光照片中的检测质量由于乳腺癌发病率和死亡率高而具有显著意义。在乳房X光照片大规模检测中,成型的对称损害通信明显特别重要。然而,大多数现有方法都造就相对粗糙的通信,没有利用通信监督。在本文中,我们提议建立一个新的基于变压器的框架CL-Net,以学习对立检测和端对端通信。在CL-Net中,建议查看-互动悬浮探测器实现跨视图候选人之间的动态互动,而Lesion Linker则使用通信监督,以更准确地指导互动进程。这两种方法的结合能够准确理解乳房X光照片对应的对立对等通信。实验表明,CL-Net在公共DDSM数据集和我们的内部数据集中产生最先进的性能。此外,在低FPI系统中,它比以往的方法大幅度要强。