Medical image classification with convolutional neural network

Qing Li; Weidong Cai; Xiaogang Wang; Yun Zhou; David Dagan Feng; Mei Chen

doi:10.1109/ICARCV.2014.7064414

Medical image classification with convolutional neural network

Qing Li, Weidong Cai, Xiaogang Wang, Yun Zhou, David Dagan Feng, Mei Chen

School of Medicine

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

287 Scopus citations

Abstract

Image patch classification is an important task in many different medical imaging applications. In this work, we have designed a customized Convolutional Neural Networks (CNN) with shallow convolution layer to classify lung image patches with interstitial lung disease (ILD). While many feature descriptors have been proposed over the past years, they can be quite complicated and domain-specific. Our customized CNN framework can, on the other hand, automatically and efficiently learn the intrinsic image features from lung image patches that are most suitable for the classification purpose. The same architecture can be generalized to perform other medical image or texture classification tasks.

Original language	English (US)
Title of host publication	2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	844-848
Number of pages	5
ISBN (Print)	9781479951994
DOIs	https://doi.org/10.1109/ICARCV.2014.7064414
State	Published - Mar 19 1997
Event	2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 - Singapore, Singapore Duration: Dec 10 2014 → Dec 12 2014

Other

Other	2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
Country/Territory	Singapore
City	Singapore
Period	12/10/14 → 12/12/14

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Human-Computer Interaction
Artificial Intelligence
Control and Systems Engineering

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10.1109/ICARCV.2014.7064414

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Medical image classification with convolutional neural network. / Li, Qing; Cai, Weidong; Wang, Xiaogang et al.
2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014. Institute of Electrical and Electronics Engineers Inc., 1997. p. 844-848 7064414.

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

Li, Q, Cai, W, Wang, X, Zhou, Y, Feng, DD & Chen, M 1997, Medical image classification with convolutional neural network. in 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014., 7064414, Institute of Electrical and Electronics Engineers Inc., pp. 844-848, 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014, Singapore, Singapore, 12/10/14. https://doi.org/10.1109/ICARCV.2014.7064414

@inproceedings{7e53456681dd4ddcb952dcfc3674b00b,

title = "Medical image classification with convolutional neural network",

abstract = "Image patch classification is an important task in many different medical imaging applications. In this work, we have designed a customized Convolutional Neural Networks (CNN) with shallow convolution layer to classify lung image patches with interstitial lung disease (ILD). While many feature descriptors have been proposed over the past years, they can be quite complicated and domain-specific. Our customized CNN framework can, on the other hand, automatically and efficiently learn the intrinsic image features from lung image patches that are most suitable for the classification purpose. The same architecture can be generalized to perform other medical image or texture classification tasks.",

author = "Qing Li and Weidong Cai and Xiaogang Wang and Yun Zhou and Feng, {David Dagan} and Mei Chen",

year = "1997",

month = mar,

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doi = "10.1109/ICARCV.2014.7064414",

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booktitle = "2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014",

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

note = "2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 ; Conference date: 10-12-2014 Through 12-12-2014",

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AU - Li, Qing

AU - Cai, Weidong

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AU - Zhou, Yun

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AU - Chen, Mei

PY - 1997/3/19

Y1 - 1997/3/19

N2 - Image patch classification is an important task in many different medical imaging applications. In this work, we have designed a customized Convolutional Neural Networks (CNN) with shallow convolution layer to classify lung image patches with interstitial lung disease (ILD). While many feature descriptors have been proposed over the past years, they can be quite complicated and domain-specific. Our customized CNN framework can, on the other hand, automatically and efficiently learn the intrinsic image features from lung image patches that are most suitable for the classification purpose. The same architecture can be generalized to perform other medical image or texture classification tasks.

AB - Image patch classification is an important task in many different medical imaging applications. In this work, we have designed a customized Convolutional Neural Networks (CNN) with shallow convolution layer to classify lung image patches with interstitial lung disease (ILD). While many feature descriptors have been proposed over the past years, they can be quite complicated and domain-specific. Our customized CNN framework can, on the other hand, automatically and efficiently learn the intrinsic image features from lung image patches that are most suitable for the classification purpose. The same architecture can be generalized to perform other medical image or texture classification tasks.

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ER -

Medical image classification with convolutional neural network

Abstract

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