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Multi−Head Feature Pyramid Networks for Breast Mass Detection

Hexiang Zhang‚ Zhenghua Xu‚ Dan Yao‚ Shuo Zhang‚ Junyang Chen and Thomas Lukasiewicz


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.

Book Title
Proceedings of the IEEE International Conference on Acoustics‚ Speech and Signal Processing‚ ICASSP 2023‚ Rhodes Island‚ Greece‚ 4−10 June 2023