Features Fusion for Dual-View Mammography Mass Detection
This work addresses a critical problem in early breast cancer diagnosis for radiologists by improving automatic detection, though it appears incremental as it builds on existing dual-view approaches.
The paper tackles the challenge of fusing information from dual-view mammography for automatic mass detection by proposing MAMM-Net, which processes both views simultaneously with feature-level sharing and achieves superior performance on the DDSM dataset compared to the previous state-of-the-art.
Detection of malignant lesions on mammography images is extremely important for early breast cancer diagnosis. In clinical practice, images are acquired from two different angles, and radiologists can fully utilize information from both views, simultaneously locating the same lesion. However, for automatic detection approaches such information fusion remains a challenge. In this paper, we propose a new model called MAMM-Net, which allows the processing of both mammography views simultaneously by sharing information not only on an object level, as seen in existing works, but also on a feature level. MAMM-Net's key component is the Fusion Layer, based on deformable attention and designed to increase detection precision while keeping high recall. Our experiments show superior performance on the public DDSM dataset compared to the previous state-of-the-art model, while introducing new helpful features such as lesion annotation on pixel-level and classification of lesions malignancy.