  Early diagnosis of pulmonary nodules (PNs) can improve the survival rate of
patients and yet is a challenging task for radiologists due to the image noise
and artifacts in computed tomography (CT) images. In this paper, we propose a
novel and effective abnormality detector implementing the attention mechanism
and group convolution on 3D single-shot detector (SSD) called group-attention
SSD (GA-SSD). We find that group convolution is effective in extracting rich
context information between continuous slices, and attention network can learn
the target features automatically. We collected a large-scale dataset that
contained 4146 CT scans with annotations of varying types and sizes of PNs
(even PNs smaller than 3mm were annotated). To the best of our knowledge, this
dataset is the largest cohort with relatively complete annotations for PNs
detection. Our experimental results show that the proposed group-attention SSD
outperforms the classic SSD framework as well as the state-of-the-art 3DCNN,
especially on some challenging lesion types.
