Multi-Head Feature Pyramid Networks for Breast Mass Detection
This work addresses a specific bottleneck in breast cancer diagnosis by enhancing detection accuracy for masses in medical imaging, though it appears incremental as it builds on existing feature pyramid networks.
The paper tackled the problem of unequal focus on mass boxes in breast mass detection from X-ray images, which limits accuracy by prioritizing larger masses over smaller ones, and proposed a multi-head feature pyramid module (MHFPN) and network (MBMDnet) that improved AP@50 by 6.58% and TPR@50 by 5.4% on the INbreast dataset, with 6-8% gains on other datasets.
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.