Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9952311 | Computer Methods and Programs in Biomedicine | 2018 | 14 Pages |
Abstract
Conclusion: We presented a novel false positive reduction framework, the ensemble of single-view 2D CNNs with fully automatic non-nodule categorization, for pulmonary nodule detection. Unlike previous 3D CNN-based frameworks, we utilized 2D CNNs using 2D single views to improve computational efficiency. Also, our training scheme using categorized non-nodules, extends the learning capability of representative features of different non-nodules. Our framework achieved state-of-the-art performance with low computational complexity.
Related Topics
Physical Sciences and Engineering
Computer Science
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Authors
Hyunjun Eun, Daeyeong Kim, Chanho Jung, Changick Kim,