  In this paper, we propose a novel framework with 3D convolutional networks
(ConvNets) for automated detection of pulmonary nodules from low-dose CT scans,
which is a challenging yet crucial task for lung cancer early diagnosis and
treatment. Different from previous standard ConvNets, we try to tackle the
severe hard/easy sample imbalance problem in medical datasets and explore the
benefits of localized annotations to regularize the learning, and hence boost
the performance of ConvNets to achieve more accurate detections. Our proposed
framework consists of two stages: 1) candidate screening, and 2) false positive
reduction. In the first stage, we establish a 3D fully convolutional network,
effectively trained with an online sample filtering scheme, to sensitively and
rapidly screen the nodule candidates. In the second stage, we design a
hybrid-loss residual network which harnesses the location and size information
as important cues to guide the nodule recognition procedure. Experimental
results on the public large-scale LUNA16 dataset demonstrate superior
performance of our proposed method compared with state-of-the-art approaches
for the pulmonary nodule detection task.
