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

doi:10.1109/ISBI53787.2023.10230631

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 proceeding › Conference contribution › peer-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 language	English
Title of host publication	2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023
Publisher	IEEE Computer Society
ISBN (Electronic)	9781665473583
DOIs	https://doi.org/10.1109/ISBI53787.2023.10230631
Publication status	Published - 2023
Externally published	Yes
Event	20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 - Cartagena, Colombia Duration: 18 Apr 2023 → 21 Apr 2023

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2023-April
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	20th IEEE International Symposium on Biomedical Imaging, ISBI 2023
Country/Territory	Colombia
City	Cartagena
Period	18/04/23 → 21/04/23

Keywords

Pulmonary nodule detection
feature pyramid network
filter network

ASJC Scopus subject areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ISBI53787.2023.10230631

Cite this

Lu, X., Zeng, N., Wang, X., Huang, J., Hu, Y., Fang, J., & Liu, J. (2023). FFNET: An End-To-End Framework Based on Feature Pyramid Network and Filter Network for Pulmonary Nodule Detection. In 2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2023-April). IEEE Computer Society. https://doi.org/10.1109/ISBI53787.2023.10230631

@inproceedings{bc1d3158eb2a4b24abb6e727401344dd,

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

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.",

keywords = "Pulmonary nodule detection, feature pyramid network, filter network",

author = "Xiaoxi Lu and Na Zeng and Xingyue Wang and Jingqi Huang and Yan Hu and Jiansheng Fang and Jiang Liu",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 ; Conference date: 18-04-2023 Through 21-04-2023",

year = "2023",

doi = "10.1109/ISBI53787.2023.10230631",

language = "English",

series = "Proceedings - International Symposium on Biomedical Imaging",

publisher = "IEEE Computer Society",

booktitle = "2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023",

address = "United States",

}

Lu, X, Zeng, N, Wang, X, Huang, J, Hu, Y, Fang, J & Liu, J 2023, FFNET: An End-To-End Framework Based on Feature Pyramid Network and Filter Network for Pulmonary Nodule Detection. in 2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023. Proceedings - International Symposium on Biomedical Imaging, vol. 2023-April, IEEE Computer Society, 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023, Cartagena, Colombia, 18/04/23. https://doi.org/10.1109/ISBI53787.2023.10230631

FFNET: An End-To-End Framework Based on Feature Pyramid Network and Filter Network for Pulmonary Nodule Detection. / Lu, Xiaoxi; Zeng, Na; Wang, Xingyue et al.
2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023. IEEE Computer Society, 2023. (Proceedings - International Symposium on Biomedical Imaging; Vol. 2023-April).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - FFNET

T2 - 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023

AU - Lu, Xiaoxi

AU - Zeng, Na

AU - Wang, Xingyue

AU - Huang, Jingqi

AU - Hu, Yan

AU - Fang, Jiansheng

AU - Liu, Jiang

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Pulmonary nodule detection

KW - feature pyramid network

KW - filter network

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U2 - 10.1109/ISBI53787.2023.10230631

DO - 10.1109/ISBI53787.2023.10230631

M3 - Conference contribution

AN - SCOPUS:85172073167

T3 - Proceedings - International Symposium on Biomedical Imaging

BT - 2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023

PB - IEEE Computer Society

Y2 - 18 April 2023 through 21 April 2023

ER -

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

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

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