FFNET: An End-To-End Framework Based on Feature Pyramid Network and Filter Network for Pulmonary Nodule Detection

Xiaoxi Lu, Na Zeng, Xingyue Wang, Jingqi Huang, Yan Hu, Jiansheng Fang, Jiang Liu

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Accurate nodule detection with high sensitivity is essential for early lung cancer diagnosis. Focusing on small nodule detection, we propose an end-to-end framework, which includes a backbone, a candidate detection network, and a filter network. The backbone learns multi-layer features so that the region proposal network with feature pyramid structure detects nodules of various sizes, especially small ones. Moreover, the filter net is designed to further classify the proposals with low confidence, which utilizes the decoupled feature maps to make the features of nodules more discriminative. We validate our framework on the LUNA16 dataset. The results show that our framework detects more small nodules, and achieves a comparable performance with other CAD systems.

Original languageEnglish
Title of host publication2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023
PublisherIEEE Computer Society
ISBN (Electronic)9781665473583
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 - Cartagena, Colombia
Duration: 18 Apr 202321 Apr 2023

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2023-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference20th IEEE International Symposium on Biomedical Imaging, ISBI 2023
Country/TerritoryColombia
CityCartagena
Period18/04/2321/04/23

Keywords

  • Pulmonary nodule detection
  • feature pyramid network
  • filter network

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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