  Pulmonary cancer is one of the most commonly diagnosed and fatal cancers and
is often diagnosed by incidental findings on computed tomography. Automated
pulmonary nodule detection is an essential part of computer-aided diagnosis,
which is still facing great challenges and difficulties to quickly and
accurately locate the exact nodules' positions. This paper proposes a dual skip
connection upsampling strategy based on Dual Path network in a U-Net structure
generating multiscale feature maps, which aims to minimize the ratio of false
positives and maximize the sensitivity for lesion detection of nodules. The
results show that our new upsampling strategy improves the performance by
having 85.3% sensitivity at 4 FROC per image compared to 84.2% for the regular
upsampling strategy or 81.2% for VGG16-based Faster-R-CNN.
