Group-Attention Single-Shot Detector (GA-SSD): Finding Pulmonary Nodules in Large-Scale CT Images

Jiechao Ma, Xiang Li, Hongwei Li, Bjoern H Menze, Sen Liang, Rongguo Zhang, Wei-Shi Zheng

Keywords: Lung Nodule Detection, Single Shot Detector, Attention Network, Group Convolution

TL;DR: Group-Attention Single-Shot Detector for Pulmonary Nodules Detection in Large-Scale CT Images

Abstract: 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). To the best of our knowledge, this dataset is the largest cohort with relatively complete annotations for PNs detection. Extensive experimental results show that the proposed group-attention SSD outperforms the conventional SSD framework as well as the state-of-the-art 3DCNN, especially on some challenging lesion types.

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