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

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

10 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
出版商	Institute of Electrical and Electronics Engineers Inc.
页	2287-2290
页数	4
ISBN（电子版）	9781457702204
DOI	https://doi.org/10.1109/EMBC.2016.7591186
出版状态	已出版 - 13 10月 2016
已对外发布	是
活动	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, 美国期限: 16 8月 2016 → 20 8月 2016

出版系列

姓名	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
卷	2016-October
ISSN（印刷版）	1557-170X

会议

会议	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
国家/地区	美国
市	Orlando
时期	16/08/16 → 20/08/16

访问文件

10.1109/EMBC.2016.7591186

其它文件与链接

链接到 Scopus 的出版物

引用此

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

Chen, Jie ; Qi, Xianbiao ; Tervonen, Osmo 等. / 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. 页码 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.",

pages = "2287--2290",

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

address = "United States",

}

Chen, J, Qi, X, Tervonen, O, Silven, O, Zhao, G & Pietikainen, M 2016, Thorax disease diagnosis using deep convolutional neural network. 在 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, 卷 2016-October, Institute of Electrical and Electronics Engineers Inc., 页码 2287-2290, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016, Orlando, 美国, 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 等.
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 页码 2287-2290 7591186 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; 卷 2016-October).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Thorax disease diagnosis using deep convolutional neural network

AU - Chen, Jie

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.

UR - https://www.scopus.com/pages/publications/85009084541

U2 - 10.1109/EMBC.2016.7591186

DO - 10.1109/EMBC.2016.7591186

M3 - Conference contribution

C2 - 28268784

AN - SCOPUS:85009084541

T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

SP - 2287

EP - 2290

BT - 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016

Y2 - 16 August 2016 through 20 August 2016

ER -

Chen J, Qi X, Tervonen O, Silven O, Zhao G, Pietikainen M. Thorax disease diagnosis using deep convolutional neural network. 在 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Institute of Electrical and Electronics Engineers Inc. 2016. 页码 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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