  Pulmonary nodule detection plays an important role in lung cancer screening
with low-dose computed tomography (CT) scans. It remains challenging to build
nodule detection deep learning models with good generalization performance due
to unbalanced positive and negative samples. In order to overcome this problem
and further improve state-of-the-art nodule detection methods, we develop a
novel deep 3D convolutional neural network with an Encoder-Decoder structure in
conjunction with a region proposal network. Particularly, we utilize a
dynamically scaled cross entropy loss to reduce the false positive rate and
combat the sample imbalance problem associated with nodule detection. We adopt
the squeeze-and-excitation structure to learn effective image features and
utilize inter-dependency information of different feature maps. We have
validated our method based on publicly available CT scans with manually
labelled ground-truth obtained from LIDC/IDRI dataset and its subset LUNA16
with thinner slices. Ablation studies and experimental results have
demonstrated that our method could outperform state-of-the-art nodule detection
methods by a large margin.
