Thorax disease diagnosis using deep convolutional neural network

Jie Chen; Xianbiao Qi; Osmo Tervonen; Olli Silven; Guoying Zhao; Matti Pietikainen

doi:10.1109/EMBC.2016.7591186

Thorax disease diagnosis using deep convolutional neural network

Jie Chen, Xianbiao Qi, Osmo Tervonen, Olli Silven, Guoying Zhao, Matti Pietikainen

University of Oulu

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

10 Citations (Scopus)

Abstract

Computer aided diagnosis (CAD) is an important issue, which can significantly improve the efficiency of doctors. In this paper, we propose a deep convolutional neural network (CNN) based method for thorax disease diagnosis. We firstly align the images by matching the interest points between the images, and then enlarge the dataset by using Gaussian scale space theory. After that we use the enlarged dataset to train a deep CNN model and apply the obtained model for the diagnosis of new test data. Our experimental results show our method achieves very promising results.

Original language	English
Title of host publication	2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2287-2290
Number of pages	4
ISBN (Electronic)	9781457702204
DOIs	https://doi.org/10.1109/EMBC.2016.7591186
Publication status	Published - 13 Oct 2016
Externally published	Yes
Event	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States Duration: 16 Aug 2016 → 20 Aug 2016

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume	2016-October
ISSN (Print)	1557-170X

Conference

Conference	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Country/Territory	United States
City	Orlando
Period	16/08/16 → 20/08/16

Access to Document

10.1109/EMBC.2016.7591186

Cite this

Chen, J., Qi, X., Tervonen, O., Silven, O., Zhao, G., & Pietikainen, M. (2016). Thorax disease diagnosis using deep convolutional neural network. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 (pp. 2287-2290). Article 7591186 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2016-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2016.7591186

Chen, Jie ; Qi, Xianbiao ; Tervonen, Osmo et al. / Thorax disease diagnosis using deep convolutional neural network. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 2287-2290 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

@inproceedings{b3437163745f47f0a126fa04e1711f80,

title = "Thorax disease diagnosis using deep convolutional neural network",

abstract = "Computer aided diagnosis (CAD) is an important issue, which can significantly improve the efficiency of doctors. In this paper, we propose a deep convolutional neural network (CNN) based method for thorax disease diagnosis. We firstly align the images by matching the interest points between the images, and then enlarge the dataset by using Gaussian scale space theory. After that we use the enlarged dataset to train a deep CNN model and apply the obtained model for the diagnosis of new test data. Our experimental results show our method achieves very promising results.",

author = "Jie Chen and Xianbiao Qi and Osmo Tervonen and Olli Silven and Guoying Zhao and Matti Pietikainen",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 ; Conference date: 16-08-2016 Through 20-08-2016",

year = "2016",

month = oct,

day = "13",

doi = "10.1109/EMBC.2016.7591186",

language = "English",

series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016",

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}

Chen, J, Qi, X, Tervonen, O, Silven, O, Zhao, G & Pietikainen, M 2016, Thorax disease diagnosis using deep convolutional neural network. in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016., 7591186, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2016-October, Institute of Electrical and Electronics Engineers Inc., pp. 2287-2290, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016, Orlando, United States, 16/08/16. https://doi.org/10.1109/EMBC.2016.7591186

Thorax disease diagnosis using deep convolutional neural network. / Chen, Jie; Qi, Xianbiao; Tervonen, Osmo et al.
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 2287-2290 7591186 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2016-October).

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

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AU - Qi, Xianbiao

AU - Tervonen, Osmo

AU - Silven, Olli

AU - Zhao, Guoying

AU - Pietikainen, Matti

PY - 2016/10/13

Y1 - 2016/10/13

N2 - Computer aided diagnosis (CAD) is an important issue, which can significantly improve the efficiency of doctors. In this paper, we propose a deep convolutional neural network (CNN) based method for thorax disease diagnosis. We firstly align the images by matching the interest points between the images, and then enlarge the dataset by using Gaussian scale space theory. After that we use the enlarged dataset to train a deep CNN model and apply the obtained model for the diagnosis of new test data. Our experimental results show our method achieves very promising results.

AB - Computer aided diagnosis (CAD) is an important issue, which can significantly improve the efficiency of doctors. In this paper, we propose a deep convolutional neural network (CNN) based method for thorax disease diagnosis. We firstly align the images by matching the interest points between the images, and then enlarge the dataset by using Gaussian scale space theory. After that we use the enlarged dataset to train a deep CNN model and apply the obtained model for the diagnosis of new test data. Our experimental results show our method achieves very promising results.

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Chen J, Qi X, Tervonen O, Silven O, Zhao G, Pietikainen M. Thorax disease diagnosis using deep convolutional neural network. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 2287-2290. 7591186. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2016.7591186

Thorax disease diagnosis using deep convolutional neural network

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