Journal
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT II
Volume 11071, Issue -, Pages 759-767Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-030-00934-2_84
Keywords
-
Categories
Funding
- National Natural Science Foundation of China [61525206, 61572472, U1703261, 61571424]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available