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.

翻译：乳腺X线图像中恶性病变检测对早期乳腺癌诊断至关重要。临床实践中，图像从两个不同角度获取，放射科医生可充分利用双视角信息并同步定位同一病灶。然而，对自动检测方法而言，此类信息融合仍具挑战性。本文提出名为MAMM-Net的新模型，该模型不仅能在现有研究中常见的对象层面共享信息，还能在特征层面同时处理两个视角的乳腺X线图像。MAMM-Net的核心组件是基于可变形注意力机制的融合层，旨在提升检测精度的同时保持高召回率。实验表明，与先前最优模型相比，本方法在公开DDSM数据集上表现更优，并引入病灶像素级标注及病变恶性程度分类等实用新功能。