  This paper proposes a new convolutional neural network with multiscale
processing for detecting ground-glass opacity (GGO) nodules in 3D computed
tomography (CT) images, which is referred to as PiaNet for short. PiaNet
consists of a feature-extraction module and a prediction module. The former
module is constructed by introducing pyramid multiscale source connections into
a contracting-expanding structure. The latter module includes a bounding-box
regressor and a classifier that are employed to simultaneously recognize GGO
nodules and estimate bounding boxes at multiple scales. To train the proposed
PiaNet, a two-stage transfer learning strategy is developed. In the first
stage, the feature-extraction module is embedded into a classifier network that
is trained on a large data set of GGO and non-GGO patches, which are generated
by performing data augmentation from a small number of annotated CT scans. In
the second stage, the pretrained feature-extraction module is loaded into
PiaNet, and then PiaNet is fine-tuned using the annotated CT scans. We evaluate
the proposed PiaNet on the LIDC-IDRI data set. The experimental results
demonstrate that our method outperforms state-of-the-art counterparts,
including the Subsolid CAD and Aidence systems and S4ND and GA-SSD methods.
PiaNet achieves a sensitivity of 91.75% with only one false positive per scan
