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.

翻译：X射线图像分析是诊断乳腺癌的主要工具之一。从海量图像数据中快速准确地检测出肿块位置，是降低乳腺癌发病率和死亡率的关键。目前，限制乳腺肿块检测精度的主要因素是对肿块框的关注不均衡，导致网络过度关注较大肿块而忽视较小肿块。本文提出多头特征金字塔模块（MHFPN）来解决特征图融合过程中目标框关注不均衡的问题，并设计了多头乳腺肿块检测网络（MBMDnet）。实验研究表明，与当前最优检测基线相比，本方法在常用INbreast数据集上的AP@50和TPR@50分别提升了6.58%和5.4%，同时在公开的MIAS和BCS-DBT数据集上，AP@20也获得了约6-8%的提升。