A ranking-based lung nodule image classification method using unlabeled image knowledge

Fan Zhang; Yang Song; Weidong Cai; Yun Zhou; Michael Fulham; Stefan Eberl; Shimin Shan; Dagan Feng

A ranking-based lung nodule image classification method using unlabeled image knowledge

Fan Zhang, Yang Song, Weidong Cai, Yun Zhou, Michael Fulham, Stefan Eberl, Shimin Shan, Dagan Feng

School of Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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)
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
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

Cite this

A ranking-based lung nodule image classification method using unlabeled image knowledge. / Zhang, Fan; Song, Yang; Cai, Weidong; Zhou, Yun; Fulham, Michael; Eberl, Stefan; Shan, Shimin; Feng, Dagan.

2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1356-1359 6868129.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhang, F, Song, Y, Cai, W, Zhou, Y, Fulham, M, Eberl, S, Shan, S & Feng, D 2014, A ranking-based lung nodule image classification method using unlabeled image knowledge. in 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014., 6868129, Institute of Electrical and Electronics Engineers Inc., pp. 1356-1359, 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, Beijing, China, 4/29/14.

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title = "A ranking-based lung nodule image classification method using unlabeled image knowledge",

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.",

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AU - Eberl, Stefan

AU - Shan, Shimin

AU - Feng, Dagan

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N2 - 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.

AB - 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.

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KW - Classification

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KW - Ranking score

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A ranking-based lung nodule image classification method using unlabeled image knowledge

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Keywords

ASJC Scopus subject areas

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