Deep learning based gastric cancer identification

Yuexiang Li; Xuechen Li; Xinpeng Xie; Linlin Shen

doi:10.1109/ISBI.2018.8363550

Deep learning based gastric cancer identification

Yuexiang Li, Xuechen Li, Xinpeng Xie, Linlin Shen

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

51 Citations (Scopus)

Abstract

Gastric cancer is one of the most common cancers, which causes the second largest deaths worldwide. Manual pathological inspection of gastric slice is time-consuming and usually suffers from inter-observer variations. In this paper, we proposed a deep learning based framework, namely GastricNet, for automatic gastric cancer identification. The proposed network adopts different architectures for shallow and deep layers for better feature extraction. We evaluate the proposed framework on publicly available BOT gastric slice dataset. The experimental results show that our deep learning framework performs better than state-of-the-art networks like DenseNet, ResNet, and achieved an accuracy of 100% for slice-based classification.

Original language	English
Title of host publication	2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
Publisher	IEEE Computer Society
Pages	182-185
Number of pages	4
ISBN (Electronic)	9781538636367
DOIs	https://doi.org/10.1109/ISBI.2018.8363550
Publication status	Published - 23 May 2018
Externally published	Yes
Event	15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States Duration: 4 Apr 2018 → 7 Apr 2018

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2018-April
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Country/Territory	United States
City	Washington
Period	4/04/18 → 7/04/18

Keywords

Classification
Deep learning network
Gastric cancer

ASJC Scopus subject areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ISBI.2018.8363550

Cite this

@inproceedings{2bf6f204c78f4b20bc6e9201c8349ff1,

title = "Deep learning based gastric cancer identification",

abstract = "Gastric cancer is one of the most common cancers, which causes the second largest deaths worldwide. Manual pathological inspection of gastric slice is time-consuming and usually suffers from inter-observer variations. In this paper, we proposed a deep learning based framework, namely GastricNet, for automatic gastric cancer identification. The proposed network adopts different architectures for shallow and deep layers for better feature extraction. We evaluate the proposed framework on publicly available BOT gastric slice dataset. The experimental results show that our deep learning framework performs better than state-of-the-art networks like DenseNet, ResNet, and achieved an accuracy of 100% for slice-based classification.",

keywords = "Classification, Deep learning network, Gastric cancer",

author = "Yuexiang Li and Xuechen Li and Xinpeng Xie and Linlin Shen",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 ; Conference date: 04-04-2018 Through 07-04-2018",

year = "2018",

month = may,

day = "23",

doi = "10.1109/ISBI.2018.8363550",

language = "English",

series = "Proceedings - International Symposium on Biomedical Imaging",

publisher = "IEEE Computer Society",

pages = "182--185",

booktitle = "2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018",

address = "United States",

}

Li, Y, Li, X, Xie, X & Shen, L 2018, Deep learning based gastric cancer identification. in 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. Proceedings - International Symposium on Biomedical Imaging, vol. 2018-April, IEEE Computer Society, pp. 182-185, 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018, Washington, United States, 4/04/18. https://doi.org/10.1109/ISBI.2018.8363550

Deep learning based gastric cancer identification. / Li, Yuexiang; Li, Xuechen; Xie, Xinpeng et al.
2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. IEEE Computer Society, 2018. p. 182-185 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2018-April).

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

TY - GEN

T1 - Deep learning based gastric cancer identification

AU - Li, Yuexiang

AU - Li, Xuechen

AU - Xie, Xinpeng

AU - Shen, Linlin

PY - 2018/5/23

Y1 - 2018/5/23

N2 - Gastric cancer is one of the most common cancers, which causes the second largest deaths worldwide. Manual pathological inspection of gastric slice is time-consuming and usually suffers from inter-observer variations. In this paper, we proposed a deep learning based framework, namely GastricNet, for automatic gastric cancer identification. The proposed network adopts different architectures for shallow and deep layers for better feature extraction. We evaluate the proposed framework on publicly available BOT gastric slice dataset. The experimental results show that our deep learning framework performs better than state-of-the-art networks like DenseNet, ResNet, and achieved an accuracy of 100% for slice-based classification.

AB - Gastric cancer is one of the most common cancers, which causes the second largest deaths worldwide. Manual pathological inspection of gastric slice is time-consuming and usually suffers from inter-observer variations. In this paper, we proposed a deep learning based framework, namely GastricNet, for automatic gastric cancer identification. The proposed network adopts different architectures for shallow and deep layers for better feature extraction. We evaluate the proposed framework on publicly available BOT gastric slice dataset. The experimental results show that our deep learning framework performs better than state-of-the-art networks like DenseNet, ResNet, and achieved an accuracy of 100% for slice-based classification.

KW - Classification

KW - Deep learning network

KW - Gastric cancer

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U2 - 10.1109/ISBI.2018.8363550

DO - 10.1109/ISBI.2018.8363550

M3 - Conference contribution

AN - SCOPUS:85048131929

T3 - Proceedings - International Symposium on Biomedical Imaging

SP - 182

EP - 185

BT - 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018

PB - IEEE Computer Society

T2 - 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018

Y2 - 4 April 2018 through 7 April 2018

ER -

Deep learning based gastric cancer identification

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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