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
N1 - Publisher Copyright:
© 2023 IEEE.
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
UR - http://www.scopus.com/inward/record.url?scp=85172073167&partnerID=8YFLogxK
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 -