An interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification
Section snippets
Introduction and background
Lung cancer is the leading cause of cancer mortality worldwide (Torre, Siegel, & Jemal, 2016). Computed tomography (CT) imaging is increasingly being used to detect and characterize pulmonary nodules with the purpose of diagnosing lung cancer earlier. The National Lung Screening Trial (NLST) (Team et al., 2011) in the United States demonstrated a 20% lung cancer mortality reduction in high-risk subjects who underwent screening using low-dose CT relative to plain chest radiography. Based on the
Lung image database consortium dataset
The Lung Image Database Consortium image collection (LIDC-IDRI) (Armato et al., 2011) is a publicly available dataset, which we used to train and test our proposed methods. LIDC-IDRI contains both screening and diagnostic CT scans collected from 7 academic centers and 8 medical imaging companies. Inclusion criteria for CT scans were: 1) having a collimation and reconstruction interval no greater than 3 mm; and 2) each scan approximately containing no more than 6 lung nodules with the longest
Experimental results
This section first describes how we trained the models. We compare our model to a traditional 3D CNN model and other state-of-the-art methods. We also evaluate the accuracy of semantic feature predictions, providing illustrations of correct and incorrect predictions (Table 4).
Discussion
We present the HSCNN model that incorporates domain knowledge into the model architecture design, predicting semantic nodule characteristics along with the primary task of nodule malignancy diagnosis. Five semantic features were considered: calcification, margin, subtlety, texture, and sphericity. Our results in Section 3.3 suggest that the HSCNN model is capable of providing accurate predictions of semantic descriptors while simultaneously classifying nodule malignancy. The semantic labels are
Conclusion
In this paper, we have developed a novel radiologist-interpretable HSCNN model for predicting lung cancer in CT-detected indeterminate nodules. This model is able to simultaneously predict nodule malignancy while classifying five nodule semantic characteristics, including calcification, margin, subtlety, texture, and sphericity of nodules. These diagnostic semantic features predictions are intermediate outputs associated with the final malignancy prediction and are useful to explain the
Author contributions
All authors contributed to the development of the project. SS developed the methodology, conducted the experiments and wrote the manuscript. SXH contributed to the experiments. AAB and DRA provided valuable advice and domain input. WH provided oversight over the project and contributed to its design. All authors reviewed the manuscript.
Conflict of interest
None declared.
CRediT authorship contribution statement
Shiwen Shen: Conceptualization, Data curation, Investigation, Methodology, Writing - original draft, Writing - review & editing. Simon X Han: Investigation, Methodology, Writing - review & editing. Denise R Aberle: Funding acquisition, Writing - review & editing. Alex A Bui: Methodology, Writing - review & editing. William Hsu: Conceptualization, Methodology, Project administration, Writing - original draft, Writing - review & editing.
Acknowledgements
The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health, and their critical role in the creation of the free publicly available LIDC/IDRI Database used in this study. Research reported in this publication was partly supported by the National Cancer Institute of the National Institutes of Health under award number R01 CA210360, the Center for Domain-Specific Computing (CDSC) funded by the National Science Foundation under grant no. 1436827
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