Abstract
In this paper, we propose a novel semi-supervised classification method for four types of lung nodules, i.e., well-circumscribed, vascularized, juxta-pleural and pleural-tail, in low dose computed tomography (LDCT) scans. The proposed method focuses on classifier design by incorporating the knowledge extracted from both training and testing datasets, and contains two stages: (1) bipartite graph construction, which presents the direct similar relationship between labeled and unlabeled images, (2) ranking score calculation, which computes the possibility of unlabeled images for each of the given four types. Our proposed method is evaluated on a publicly available dataset and clearly demonstrates its promising classification performance.
Original language | English (US) |
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Title of host publication | 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1356-1359 |
Number of pages | 4 |
ISBN (Print) | 9781467319591 |
State | Published - Jul 29 2014 |
Event | 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 - Beijing, China Duration: Apr 29 2014 → May 2 2014 |
Other
Other | 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 |
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Country/Territory | China |
City | Beijing |
Period | 4/29/14 → 5/2/14 |
Keywords
- Bipartite graph
- Classification
- Lung nodule
- Ranking score
ASJC Scopus subject areas
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging