  The mortality of lung cancer has ranked high among cancers for many years.
Early detection of lung cancer is critical for disease prevention, cure, and
mortality rate reduction. However, existing detection methods on pulmonary
nodules introduce an excessive number of false positive proposals in order to
achieve high sensitivity, which is not practical in clinical situations. In
this paper, we propose the multi-head detection and spatial
squeeze-and-attention network, MHSnet, to detect pulmonary nodules, in order to
aid doctors in the early diagnosis of lung cancers. Specifically, we first
introduce multi-head detectors and skip connections to customize for the
variety of nodules in sizes, shapes and types and capture multi-scale features.
Then, we implement a spatial attention module to enable the network to focus on
different regions differently inspired by how experienced clinicians screen CT
images, which results in fewer false positive proposals. Lastly, we present a
lightweight but effective false positive reduction module with the Linear
Regression model to cut down the number of false positive proposals, without
any constraints on the front network. Extensive experimental results compared
with the state-of-the-art models have shown the superiority of the MHSnet in
terms of the average FROC, sensitivity and especially false discovery rate
(2.98% and 2.18% improvement in terms of average FROC and sensitivity, 5.62%
and 28.33% decrease in terms of false discovery rate and average candidates per
scan). The false positive reduction module significantly decreases the average
number of candidates generated per scan by 68.11% and the false discovery rate
by 13.48%, which is promising to reduce distracted proposals for the downstream
tasks based on the detection results.
