In vivo classification of inflammation in blood vessels with convolutional neural networks

Stuart McIlroy; Yoshimasa Kubo; Thomas Trappenberg; James Toguri; Christian Lehmann

doi:10.1109/IJCNN.2017.7966231

In vivo classification of inflammation in blood vessels with convolutional neural networks

Stuart McIlroy, Yoshimasa Kubo, Thomas Trappenberg, James Toguri, Christian Lehmann

Medicine

Medicine

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

4 Citations (Scopus)

Abstract

An emerging field in medical diagnostics is the study of micro-circulations in blood vessels. Several characteristics of the micro-circulations in blood vessels have been shown to predict inflammation in a patient's tissue. The characteristics are video recorded via a camera inserted into the subject. At present, the analysis of the videos are done manually by visual inspection to determine inflammation. In our paper, we propose a technique to automatically classify the videos as containing inflammation or not. Our technique uses a convolutional neural network which classifies many different segments of images from a video and averages the predictions. Our network achieves an accuracy of 83%. We further divide inflammation into extreme and moderate inflammation and our network achieves an accuracy of 80%. This is the first step in developing methods that can perform a better quantitative analysis of inflammation to speed up medical diagnosis.

Original language	English
Title of host publication	2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3022-3027
Number of pages	6
ISBN (Electronic)	9781509061815
DOIs	https://doi.org/10.1109/IJCNN.2017.7966231
Publication status	Published - Jun 30 2017
Event	2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States Duration: May 14 2017 → May 19 2017

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2017-May

Conference

Conference	2017 International Joint Conference on Neural Networks, IJCNN 2017
Country/Territory	United States
City	Anchorage
Period	5/14/17 → 5/19/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

ASJC Scopus Subject Areas

Software
Artificial Intelligence

Access to Document

10.1109/IJCNN.2017.7966231

Cite this

McIlroy, S., Kubo, Y., Trappenberg, T., Toguri, J., & Lehmann, C. (2017). In vivo classification of inflammation in blood vessels with convolutional neural networks. In 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings (pp. 3022-3027). Article 7966231 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2017-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2017.7966231

In vivo classification of inflammation in blood vessels with convolutional neural networks. / McIlroy, Stuart; Kubo, Yoshimasa; Trappenberg, Thomas et al.
2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 3022-3027 7966231 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2017-May).

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

McIlroy, S, Kubo, Y, Trappenberg, T, Toguri, J & Lehmann, C 2017, In vivo classification of inflammation in blood vessels with convolutional neural networks. in 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings., 7966231, Proceedings of the International Joint Conference on Neural Networks, vol. 2017-May, Institute of Electrical and Electronics Engineers Inc., pp. 3022-3027, 2017 International Joint Conference on Neural Networks, IJCNN 2017, Anchorage, United States, 5/14/17. https://doi.org/10.1109/IJCNN.2017.7966231

McIlroy S, Kubo Y, Trappenberg T, Toguri J , Lehmann C. In vivo classification of inflammation in blood vessels with convolutional neural networks. In 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 3022-3027. 7966231. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN.2017.7966231

McIlroy, Stuart ; Kubo, Yoshimasa ; Trappenberg, Thomas et al. / In vivo classification of inflammation in blood vessels with convolutional neural networks. 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 3022-3027 (Proceedings of the International Joint Conference on Neural Networks).

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abstract = "An emerging field in medical diagnostics is the study of micro-circulations in blood vessels. Several characteristics of the micro-circulations in blood vessels have been shown to predict inflammation in a patient's tissue. The characteristics are video recorded via a camera inserted into the subject. At present, the analysis of the videos are done manually by visual inspection to determine inflammation. In our paper, we propose a technique to automatically classify the videos as containing inflammation or not. Our technique uses a convolutional neural network which classifies many different segments of images from a video and averages the predictions. Our network achieves an accuracy of 83%. We further divide inflammation into extreme and moderate inflammation and our network achieves an accuracy of 80%. This is the first step in developing methods that can perform a better quantitative analysis of inflammation to speed up medical diagnosis.",

author = "Stuart McIlroy and Yoshimasa Kubo and Thomas Trappenberg and James Toguri and Christian Lehmann",

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In vivo classification of inflammation in blood vessels with convolutional neural networks

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Access to Document

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