A fast automatic juxta-pleural lung nodule detection framework using convolutional neural networks and vote algorithm

Jiaxing Tan; Yumei Huo; Zhengrong Liang; Lihong Li

doi:10.1007/978-3-030-00500-9_10

A fast automatic juxta-pleural lung nodule detection framework using convolutional neural networks and vote algorithm

Jiaxing Tan
, Yumei Huo
, Zhengrong Liang
, Lihong Li

Radiology

City University of New York

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

1 Scopus citations

Abstract

Lung Nodule Detection from CT scans is a crucial task for the early detection of lung cancer with high difficulty performing an automatic detection. In this paper, we propose a fast automatic voting based framework using Convolutional Neural Network to detect juxta-pleural nodules, which are pulmonary (lung) nodules attached to the chest wall and hard to detect even by human experts. The detection result for each region in the CT scan is voted by the detection results of the extracted candidates from the region, which we formulate as a generative model. We perform two sets of experiments: one is to validate our framework, and the other is to compare different convolution neural network settings under our framework. The result shows our framework is competent to detect juxta-pleural lung nodules especially when only a weak classifier trained on noisy data is available. Meanwhile, we overcome the problem of determining the proper input size for nodules with high variance in diameters.

Original language	English
Title of host publication	Patch-Based Techniques in Medical Imaging - 4th International Workshop, Patch-MI 2018, Held in Conjunction with MICCAI 2018, Proceedings
Editors	Brent C. Munsell, Guorong Wu, Pierrick Coupé, Gerard Sanroma, Yiqiang Zhan, Wenjia Bai
Publisher	Springer Verlag
Pages	85-92
Number of pages	8
ISBN (Print)	9783030004996
DOIs	https://doi.org/10.1007/978-3-030-00500-9_10
State	Published - 2018
Event	4th International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2018 Held in Conjunction with 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 - Granada, Spain Duration: Sep 20 2018 → Sep 20 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11075 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2018 Held in Conjunction with 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018
Country/Territory	Spain
City	Granada
Period	09/20/18 → 09/20/18

Keywords

Deep learning
Juxta-pleural nodule detection
Lung cancer
Weakly labeled

Access to Document

10.1007/978-3-030-00500-9_10

Cite this

Tan, J., Huo, Y., Liang, Z., & Li, L. (2018). A fast automatic juxta-pleural lung nodule detection framework using convolutional neural networks and vote algorithm. In B. C. Munsell, G. Wu, P. Coupé, G. Sanroma, Y. Zhan, & W. Bai (Eds.), Patch-Based Techniques in Medical Imaging - 4th International Workshop, Patch-MI 2018, Held in Conjunction with MICCAI 2018, Proceedings (pp. 85-92). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11075 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-00500-9_10

Tan, Jiaxing ; Huo, Yumei ; Liang, Zhengrong et al. / A fast automatic juxta-pleural lung nodule detection framework using convolutional neural networks and vote algorithm. Patch-Based Techniques in Medical Imaging - 4th International Workshop, Patch-MI 2018, Held in Conjunction with MICCAI 2018, Proceedings. editor / Brent C. Munsell ; Guorong Wu ; Pierrick Coupé ; Gerard Sanroma ; Yiqiang Zhan ; Wenjia Bai. Springer Verlag, 2018. pp. 85-92 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{207c1a922aa147aeabb942bd55948883,

title = "A fast automatic juxta-pleural lung nodule detection framework using convolutional neural networks and vote algorithm",

abstract = "Lung Nodule Detection from CT scans is a crucial task for the early detection of lung cancer with high difficulty performing an automatic detection. In this paper, we propose a fast automatic voting based framework using Convolutional Neural Network to detect juxta-pleural nodules, which are pulmonary (lung) nodules attached to the chest wall and hard to detect even by human experts. The detection result for each region in the CT scan is voted by the detection results of the extracted candidates from the region, which we formulate as a generative model. We perform two sets of experiments: one is to validate our framework, and the other is to compare different convolution neural network settings under our framework. The result shows our framework is competent to detect juxta-pleural lung nodules especially when only a weak classifier trained on noisy data is available. Meanwhile, we overcome the problem of determining the proper input size for nodules with high variance in diameters.",

keywords = "Deep learning, Juxta-pleural nodule detection, Lung cancer, Weakly labeled",

author = "Jiaxing Tan and Yumei Huo and Zhengrong Liang and Lihong Li",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 4th International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2018 Held in Conjunction with 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 ; Conference date: 20-09-2018 Through 20-09-2018",

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doi = "10.1007/978-3-030-00500-9\_10",

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booktitle = "Patch-Based Techniques in Medical Imaging - 4th International Workshop, Patch-MI 2018, Held in Conjunction with MICCAI 2018, Proceedings",

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Tan, J, Huo, Y, Liang, Z & Li, L 2018, A fast automatic juxta-pleural lung nodule detection framework using convolutional neural networks and vote algorithm. in BC Munsell, G Wu, P Coupé, G Sanroma, Y Zhan & W Bai (eds), Patch-Based Techniques in Medical Imaging - 4th International Workshop, Patch-MI 2018, Held in Conjunction with MICCAI 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11075 LNCS, Springer Verlag, pp. 85-92, 4th International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2018 Held in Conjunction with 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, Granada, Spain, 09/20/18. https://doi.org/10.1007/978-3-030-00500-9_10

A fast automatic juxta-pleural lung nodule detection framework using convolutional neural networks and vote algorithm. / Tan, Jiaxing; Huo, Yumei; Liang, Zhengrong et al.
Patch-Based Techniques in Medical Imaging - 4th International Workshop, Patch-MI 2018, Held in Conjunction with MICCAI 2018, Proceedings. ed. / Brent C. Munsell; Guorong Wu; Pierrick Coupé; Gerard Sanroma; Yiqiang Zhan; Wenjia Bai. Springer Verlag, 2018. p. 85-92 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11075 LNCS).

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

TY - GEN

T1 - A fast automatic juxta-pleural lung nodule detection framework using convolutional neural networks and vote algorithm

AU - Tan, Jiaxing

AU - Huo, Yumei

AU - Liang, Zhengrong

AU - Li, Lihong

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2018.

PY - 2018

Y1 - 2018

N2 - Lung Nodule Detection from CT scans is a crucial task for the early detection of lung cancer with high difficulty performing an automatic detection. In this paper, we propose a fast automatic voting based framework using Convolutional Neural Network to detect juxta-pleural nodules, which are pulmonary (lung) nodules attached to the chest wall and hard to detect even by human experts. The detection result for each region in the CT scan is voted by the detection results of the extracted candidates from the region, which we formulate as a generative model. We perform two sets of experiments: one is to validate our framework, and the other is to compare different convolution neural network settings under our framework. The result shows our framework is competent to detect juxta-pleural lung nodules especially when only a weak classifier trained on noisy data is available. Meanwhile, we overcome the problem of determining the proper input size for nodules with high variance in diameters.

AB - Lung Nodule Detection from CT scans is a crucial task for the early detection of lung cancer with high difficulty performing an automatic detection. In this paper, we propose a fast automatic voting based framework using Convolutional Neural Network to detect juxta-pleural nodules, which are pulmonary (lung) nodules attached to the chest wall and hard to detect even by human experts. The detection result for each region in the CT scan is voted by the detection results of the extracted candidates from the region, which we formulate as a generative model. We perform two sets of experiments: one is to validate our framework, and the other is to compare different convolution neural network settings under our framework. The result shows our framework is competent to detect juxta-pleural lung nodules especially when only a weak classifier trained on noisy data is available. Meanwhile, we overcome the problem of determining the proper input size for nodules with high variance in diameters.

KW - Deep learning

KW - Juxta-pleural nodule detection

KW - Lung cancer

KW - Weakly labeled

UR - https://www.scopus.com/pages/publications/85054344589

U2 - 10.1007/978-3-030-00500-9_10

DO - 10.1007/978-3-030-00500-9_10

M3 - Conference contribution

AN - SCOPUS:85054344589

SN - 9783030004996

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 85

EP - 92

BT - Patch-Based Techniques in Medical Imaging - 4th International Workshop, Patch-MI 2018, Held in Conjunction with MICCAI 2018, Proceedings

A2 - Munsell, Brent C.

A2 - Wu, Guorong

A2 - Coupé, Pierrick

A2 - Sanroma, Gerard

A2 - Zhan, Yiqiang

A2 - Bai, Wenjia

PB - Springer Verlag

T2 - 4th International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2018 Held in Conjunction with 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018

Y2 - 20 September 2018 through 20 September 2018

ER -

Tan J, Huo Y, Liang Z, Li L. A fast automatic juxta-pleural lung nodule detection framework using convolutional neural networks and vote algorithm. In Munsell BC, Wu G, Coupé P, Sanroma G, Zhan Y, Bai W, editors, Patch-Based Techniques in Medical Imaging - 4th International Workshop, Patch-MI 2018, Held in Conjunction with MICCAI 2018, Proceedings. Springer Verlag. 2018. p. 85-92. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-00500-9_10

A fast automatic juxta-pleural lung nodule detection framework using convolutional neural networks and vote algorithm

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Keywords

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