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.

中文翻译：

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检查和链接：成对病变对应指南乳房 X 线照片质量检测

由于乳腺癌的高发病率和死亡率，在乳房 X 线照片中检测质量具有重要意义。在乳房 X 线照片质量检测中，明确地建模成对病变对应关系尤为重要。然而，现有的大多数方法都建立了相对粗略的对应关系，并且没有利用对应监督。在本文中，我们提出了一种新的基于转换器的框架 CL-Net，以端到端的方式学习病变检测和成对对应。在 CL-Net 中，View-Interactive Lesion Detector 被提出来实现跨视图候选者的动态交互，而 Lesion Linker 采用对应监督来更准确地指导交互过程。这两种设计的结合实现了对乳房 X 线照片成对病变对应的精确理解。实验表明，CL-Net 在公共 DDSM 数据集和我们的内部数据集上产生了最先进的性能。此外，它在低 FPI 制度下大大优于以前的方法。