A dual stream network for tumor detection in hyperspectral images

P.J.C. Weijtmans; C. Shan; T. Tan; S.G. Brouwer de Koning; T.J.M. Ruers

doi:10.1109/ISBI.2019.8759566

A dual stream network for tumor detection in hyperspectral images

P.J.C. Weijtmans, C. Shan, T. Tan, S.G. Brouwer de Koning, T.J.M. Ruers

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

13 Citations (Scopus)

Abstract

Hyperspectral imaging has become an emerging imaging modality for medical applications. In this work, we propose to combine both the spectral and structural information in the hyperspectral data cube for tumor detection in tongue tissue. A dual stream network is designed, with a spectral and a structural branch. Hyperspectral data (480 to 920 nm) is collected from 7 patients with tongue squamous cell carcinoma. Histopathological analysis provided ground truth labels. The proposed dual stream model outperforms the pure spectral and structural approaches with areas under the ROC-curve of 0.90, 0.87 and 0.85, respectively.

Original language	English
Title of host publication	ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
Place of Publication	Piscataway
Publisher	IEEE Computer Society
Pages	1256-1259
Number of pages	4
ISBN (Electronic)	9781538636411
DOIs	https://doi.org/10.1109/ISBI.2019.8759566
Publication status	Published - 1 Apr 2019
Event	16th IEEE International Symposium on Biomedical Imaging (ISBI 2019) - Venice, Italy Duration: 8 Apr 2019 → 11 Apr 2019 Conference number: 16

Conference

Conference	16th IEEE International Symposium on Biomedical Imaging (ISBI 2019)
Abbreviated title	ISBI 2019
Country/Territory	Italy
City	Venice
Period	8/04/19 → 11/04/19

Keywords

Hyperspectral imaging
Machine learning
Neural networks
Tongue tumor

Access to Document

10.1109/ISBI.2019.8759566

Cite this

@inproceedings{1033d6ede9514f388a0348f04889397e,

title = "A dual stream network for tumor detection in hyperspectral images",

abstract = "Hyperspectral imaging has become an emerging imaging modality for medical applications. In this work, we propose to combine both the spectral and structural information in the hyperspectral data cube for tumor detection in tongue tissue. A dual stream network is designed, with a spectral and a structural branch. Hyperspectral data (480 to 920 nm) is collected from 7 patients with tongue squamous cell carcinoma. Histopathological analysis provided ground truth labels. The proposed dual stream model outperforms the pure spectral and structural approaches with areas under the ROC-curve of 0.90, 0.87 and 0.85, respectively.",

keywords = "Hyperspectral imaging, Machine learning, Neural networks, Tongue tumor",

author = "P.J.C. Weijtmans and C. Shan and T. Tan and {Brouwer de Koning}, S.G. and T.J.M. Ruers",

year = "2019",

month = apr,

day = "1",

doi = "10.1109/ISBI.2019.8759566",

language = "English",

pages = "1256--1259",

booktitle = "ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging",

publisher = "IEEE Computer Society",

address = "United States",

note = "16th IEEE International Symposium on Biomedical Imaging (ISBI 2019), ISBI 2019 ; Conference date: 08-04-2019 Through 11-04-2019",

}

Weijtmans, PJC, Shan, C, Tan, T, Brouwer de Koning, SG & Ruers, TJM 2019, A dual stream network for tumor detection in hyperspectral images. in ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging., 8759566, IEEE Computer Society, Piscataway, pp. 1256-1259, 16th IEEE International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy, 8/04/19. https://doi.org/10.1109/ISBI.2019.8759566

A dual stream network for tumor detection in hyperspectral images. / Weijtmans, P.J.C.; Shan, C.; Tan, T.; Brouwer de Koning, S.G.; Ruers, T.J.M.

ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging. Piscataway : IEEE Computer Society, 2019. p. 1256-1259 8759566.

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

TY - GEN

T1 - A dual stream network for tumor detection in hyperspectral images

AU - Weijtmans, P.J.C.

AU - Shan, C.

AU - Tan, T.

AU - Brouwer de Koning, S.G.

AU - Ruers, T.J.M.

N1 - Conference code: 16

PY - 2019/4/1

Y1 - 2019/4/1

N2 - Hyperspectral imaging has become an emerging imaging modality for medical applications. In this work, we propose to combine both the spectral and structural information in the hyperspectral data cube for tumor detection in tongue tissue. A dual stream network is designed, with a spectral and a structural branch. Hyperspectral data (480 to 920 nm) is collected from 7 patients with tongue squamous cell carcinoma. Histopathological analysis provided ground truth labels. The proposed dual stream model outperforms the pure spectral and structural approaches with areas under the ROC-curve of 0.90, 0.87 and 0.85, respectively.

AB - Hyperspectral imaging has become an emerging imaging modality for medical applications. In this work, we propose to combine both the spectral and structural information in the hyperspectral data cube for tumor detection in tongue tissue. A dual stream network is designed, with a spectral and a structural branch. Hyperspectral data (480 to 920 nm) is collected from 7 patients with tongue squamous cell carcinoma. Histopathological analysis provided ground truth labels. The proposed dual stream model outperforms the pure spectral and structural approaches with areas under the ROC-curve of 0.90, 0.87 and 0.85, respectively.

KW - Hyperspectral imaging

KW - Machine learning

KW - Neural networks

KW - Tongue tumor

UR - http://www.scopus.com/inward/record.url?scp=85072370566&partnerID=8YFLogxK

U2 - 10.1109/ISBI.2019.8759566

DO - 10.1109/ISBI.2019.8759566

M3 - Conference contribution

AN - SCOPUS:85072370566

SP - 1256

EP - 1259

BT - ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging

PB - IEEE Computer Society

CY - Piscataway

T2 - 16th IEEE International Symposium on Biomedical Imaging (ISBI 2019)

Y2 - 8 April 2019 through 11 April 2019

ER -

A dual stream network for tumor detection in hyperspectral images

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this