Title

S4Nd: Single-Shot Single-Scale Lung Nodule Detection

Keywords

Deep learning; Dense CNN; Lung nodule detection; Object detection; Tiny object detection

Abstract

The most recent 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. 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 publicly 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.

Publication Date

1-1-2018

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

11071 LNCS

Number of Pages

794-802

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-030-00934-2_88

Socpus ID

85054092037 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85054092037

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