  Analysis of X-ray images is one of the main tools to diagnose breast cancer.
The ability to quickly and accurately detect the location of masses from the
huge amount of image data is the key to reducing the morbidity and mortality of
breast cancer. Currently, the main factor limiting the accuracy of breast mass
detection is the unequal focus on the mass boxes, leading the network to focus
too much on larger masses at the expense of smaller ones. In the paper, we
propose the multi-head feature pyramid module (MHFPN) to solve the problem of
unbalanced focus of target boxes during feature map fusion and design a
multi-head breast mass detection network (MBMDnet). Experimental studies show
that, comparing to the SOTA detection baselines, our method improves by 6.58%
(in AP@50) and 5.4% (in TPR@50) on the commonly used INbreast dataset, while
about 6-8% improvements (in AP@20) are also observed on the public MIAS and
BCS-DBT datasets.
