The state of the art lung nodule detection studies rely on computationally
expensive multi-stage frameworks to detect nodules from CT scans. To address
this computational challenge and provide better performance, in this paper we
propose S4ND, a new deep learning based method for lung nodule detection. Our
approach uses a single feed forward pass of a single network for detection and
provides better performance when compared to the current literature. The whole
detection pipeline is designed as a single $3D$ Convolutional Neural Network
(CNN) with dense connections, trained in an end-to-end manner. S4ND does not
require any further post-processing or user guidance to refine detection
results. Experimentally, we compared our network with the current
state-of-the-art object detection network (SSD) in computer vision as well as
the state-of-the-art published method for lung nodule detection (3D DCNN). We
used publically available $888$ CT scans from LUNA challenge dataset and showed
that the proposed method outperforms the current literature both in terms of
efficiency and accuracy by achieving an average FROC-score of $0.897$. We also
provide an in-depth analysis of our proposed network to shed light on the
unclear paradigms of tiny object detection.

本文提出了一种基于深度学习和单个三维卷积神经网络的肺结节检测方法 S4ND，相较于当前文献在效率和准确性上都有明显提升，平均 FROC 得分为 0.897。