Check and Link: Pairwise Lesion Correspondence Guides Mammogram Mass Detection
This work addresses breast cancer diagnosis by enhancing mass detection in mammograms, offering a domain-specific incremental advance over existing methods.
The paper tackles mammogram mass detection by modeling pairwise lesion correspondence explicitly, proposing CL-Net, a transformer-based framework that integrates lesion detection and correspondence learning end-to-end, achieving state-of-the-art performance on public and in-house datasets with significant improvements in low false-positive regimes.
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.