  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.
