Automated Pulmonary Nodule Detection: High Sensitivity With Few Candidates

Abstract

Automated pulmonary nodule detection plays an important role in lung cancer diagnosis. In this paper, we propose a pulmonary detection framework that can achieve high sensitivity with few candidates. First, the Feature Pyramid Network (FPN), which leverages multi-level features, is applied to detect nodule candidates that cover almost all true positives. Then redundant candidates are removed by a simple but effective Conditional 3-Dimensional Non-Maximum Suppression (Conditional 3D-NMS). Moreover, a novel Attention 3D CNN (Attention 3D-CNN) which efficiently utilizes contextual information is proposed to further remove the overwhelming majority of false positives. The proposed method yields a sensitivity of 95.8% at 2 false positives per scan on the LUng Nodule Analysis 2016 (LUNA16) dataset, which is competitive compared to the current published state-of-the-art methods.

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

759-767

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-030-00934-2_84

Socpus ID

85054059936 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85054059936

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