Abstract
Accurate detection of pulmonary nodules with high sensitivity and specificity is essential for automatic lung cancer diagnosis from CT scans. Although many deep learning-based algorithms make great progress for improving the accuracy of nodule detection, the high false positive rate is still a challenging problem which limited the automatic diagnosis in routine clinical practice. In this paper, we propose a novel pulmonary nodule detection framework based on a 3D Feature Pyramid Network (3DFPN) to improve the sensitivity of nodule detection by employing multi-scale features to increase the resolution of nodules, as well as a parallel top-down path to transit the high-level semantic features to complement low-level general features. Furthermore, a High Sensitivity and Specificity (HS\(^2\)) network is introduced to eliminate the falsely detected nodule candidates by tracking the appearance changes in continuous CT slices of each nodule candidate. The proposed framework is evaluated on the public Lung Nodule Analysis (LUNA16) challenge dataset. Our method is able to accurately detect lung nodules at high sensitivity and specificity and achieves \(90.4\%\) sensitivity with 1/8 false positive per scan which outperforms the state-of-the-art results \(15.6\%\).
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Acknowledgements
This material is based upon work supported by the National Science Foundation under award number IIS-1400802 and Memorial Sloan Kettering Cancer Center Support Grant/Core Grant P30 CA008748. Oguz Akin, MD serves as a scientific advisor for Ezra AI, Inc., which is unrelated to the research being reported.
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Liu, J., Cao, L., Akin, O., Tian, Y. (2019). 3DFPN-HS\(^2\): 3D Feature Pyramid Network Based High Sensitivity and Specificity Pulmonary Nodule Detection. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11769. Springer, Cham. https://doi.org/10.1007/978-3-030-32226-7_57
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