Abstract
As the core step of lung nodule analysis, lung nodule diagnosis comprises two important tasks: False Positive Reduction (FPR) and Malignancy Suspiciousness Estimation (MSE). Many studies tackle these two tasks separately. However, these two tasks share a lot of similarities and have connections with each other, since MSE is the successive step of FPR, and both tasks can be deemed as the lung nodule labeling problems. In this paper, we split the label ‘real nodule’ defined in FPR into two new finer grain labels, namely ‘low risk’ and ‘high risk’, which are defined in MSE. In such way, we merge these two separated issues into a unified fine grain lung nodule classification problem. Finally, a novel Attribute Sensitive Multi-Branch 3D CNN (ASMB3DCNN) is proposed for performing the fine grain lung nodule classification. We evaluate our model on LIDC-IDRI and LUNA2016 datasets. Experiments demonstrate that ASMB3DCNN can efficiently address the two tasks above in a joint way and achieve the promising performances in comparison with the state-of-the-arts.
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In clinical, nodules larger than 30 mm in diameter are called lung masses and will not be discussed here
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Acknowledgment
This work was partially supported by the Chongqing Major Thematic Projects (Grant no. cstc2018jszx-cyztzxX0017).
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Wang, Q., Zhang, J., Huang, S., Liu, C., Zhang, X., Yang, D. (2019). Fine Grain Lung Nodule Diagnosis Based on CT Using 3D Convolutional Neural Network. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11858. Springer, Cham. https://doi.org/10.1007/978-3-030-31723-2_12
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DOI: https://doi.org/10.1007/978-3-030-31723-2_12
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