Detecting spongiosis in stained histopathological specimen using multispectral imaging and machine learning

Sanush Khyle Abeysekera; Melanie Po-Leen Ooi; Ye Chow Kuang; Chee Pin Tan; Sharifah Syed Hassan

doi:10.1109/SAS.2014.6798945

Detecting spongiosis in stained histopathological specimen using multispectral imaging and machine learning

Sanush Khyle Abeysekera, Melanie Po-Leen Ooi, Ye Chow Kuang, Chee Pin Tan, Sharifah Syed Hassan

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

Original language	English
Title of host publication	2014 IEEE Sensors Applications Symposium (SAS 2014) Proceedings
Editors	Salvatore Baglio, Deniz Gurkan
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	195 - 200
Number of pages	6
ISBN (Print)	9781479921805
DOIs	https://doi.org/10.1109/SAS.2014.6798945
Publication status	Published - 2014
Event	IEEE Sensors Applications Symposium (SAS) 2014 - Queenstown, New Zealand Duration: 18 Feb 2014 → 20 Feb 2014

Conference

Conference	IEEE Sensors Applications Symposium (SAS) 2014
Abbreviated title	SAS 2014
Country/Territory	New Zealand
City	Queenstown
Period	18/02/14 → 20/02/14

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10.1109/SAS.2014.6798945

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Abeysekera, S. K., Ooi, M. P-L., Kuang, Y. C., Tan, C. P., & Hassan, S. S. (2014). Detecting spongiosis in stained histopathological specimen using multispectral imaging and machine learning. In S. Baglio, & D. Gurkan (Eds.), 2014 IEEE Sensors Applications Symposium (SAS 2014) Proceedings (pp. 195 - 200). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/SAS.2014.6798945

Abeysekera, Sanush Khyle ; Ooi, Melanie Po-Leen ; Kuang, Ye Chow ; Tan, Chee Pin ; Hassan, Sharifah Syed. / Detecting spongiosis in stained histopathological specimen using multispectral imaging and machine learning. 2014 IEEE Sensors Applications Symposium (SAS 2014) Proceedings. editor / Salvatore Baglio ; Deniz Gurkan. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2014. pp. 195 - 200

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doi = "10.1109/SAS.2014.6798945",

language = "English",

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booktitle = "2014 IEEE Sensors Applications Symposium (SAS 2014) Proceedings",

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Abeysekera, SK, Ooi, MP-L, Kuang, YC, Tan, CP & Hassan, SS 2014, Detecting spongiosis in stained histopathological specimen using multispectral imaging and machine learning. in S Baglio & D Gurkan (eds), 2014 IEEE Sensors Applications Symposium (SAS 2014) Proceedings. IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 195 - 200, IEEE Sensors Applications Symposium (SAS) 2014, Queenstown, New Zealand, 18/02/14. https://doi.org/10.1109/SAS.2014.6798945

Detecting spongiosis in stained histopathological specimen using multispectral imaging and machine learning. / Abeysekera, Sanush Khyle; Ooi, Melanie Po-Leen; Kuang, Ye Chow; Tan, Chee Pin ; Hassan, Sharifah Syed.

2014 IEEE Sensors Applications Symposium (SAS 2014) Proceedings. ed. / Salvatore Baglio; Deniz Gurkan. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2014. p. 195 - 200.

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

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T1 - Detecting spongiosis in stained histopathological specimen using multispectral imaging and machine learning

AU - Abeysekera, Sanush Khyle

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AU - Kuang, Ye Chow

AU - Tan, Chee Pin

AU - Hassan, Sharifah Syed

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DO - 10.1109/SAS.2014.6798945

M3 - Conference Paper

SN - 9781479921805

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EP - 200

BT - 2014 IEEE Sensors Applications Symposium (SAS 2014) Proceedings

A2 - Baglio, Salvatore

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PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

T2 - IEEE Sensors Applications Symposium (SAS) 2014

Y2 - 18 February 2014 through 20 February 2014

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Abeysekera SK, Ooi MP-L, Kuang YC, Tan CP , Hassan SS. Detecting spongiosis in stained histopathological specimen using multispectral imaging and machine learning. In Baglio S, Gurkan D, editors, 2014 IEEE Sensors Applications Symposium (SAS 2014) Proceedings. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2014. p. 195 - 200 https://doi.org/10.1109/SAS.2014.6798945

Detecting spongiosis in stained histopathological specimen using multispectral imaging and machine learning

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