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.

本文提出了一个全新的基于 Transformer 的 CL-Net 框架，可以在端到端的方式下，学习病变检测和配对位置，并设计了一个可交互的检测器和病变关联器来确定正确的匹配位置，从而在乳腺 X 线摄影图像的准确病变检测方面取得了最优异的表现。