Deep learning enables virtual histological staining of label-free tissue sections using auto-fluorescence

Yair Rivenson; Hongda Wang; Kevin De Haan; Zhensong Wei; Aydogan Ozcan

doi:10.1364/CLEO_AT.2019.ATu4K.6

Deep learning enables virtual histological staining of label-free tissue sections using auto-fluorescence

Yair Rivenson
, Hongda Wang
, Kevin De Haan
, Zhensong Wei
, Aydogan Ozcan

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

Abstract

We report a data-driven method for label-free virtual histological staining of tissue sections using deep learning. This framework is successfully demonstrated by inferring multiple types of stains on different tissue types using auto-fluorescence signal.

Original language	English
Title of host publication	CLEO
Subtitle of host publication	Applications and Technology, CLEO_AT 2019
Publisher	Optica Publishing Group (formerly OSA)
ISBN (Print)	9781943580576
DOIs	https://doi.org/10.1364/CLEO_AT.2019.ATu4K.6
State	Published - 1 Jan 2019
Externally published	Yes
Event	CLEO: Applications and Technology, CLEO_AT 2019 - San Jose, United States Duration: 5 May 2019 → 10 May 2019

Publication series

Name	Optics InfoBase Conference Papers
Volume	Part F127-CLEO_AT 2019
ISSN (Electronic)	2162-2701

Conference

Conference	CLEO: Applications and Technology, CLEO_AT 2019
Country/Territory	United States
City	San Jose
Period	5/05/19 → 10/05/19

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Mechanics of Materials

Access to Document

10.1364/CLEO_AT.2019.ATu4K.6

Cite this

Rivenson, Y., Wang, H., De Haan, K., Wei, Z., & Ozcan, A. (2019). Deep learning enables virtual histological staining of label-free tissue sections using auto-fluorescence. In CLEO: Applications and Technology, CLEO_AT 2019 (Optics InfoBase Conference Papers; Vol. Part F127-CLEO_AT 2019). Optica Publishing Group (formerly OSA). https://doi.org/10.1364/CLEO_AT.2019.ATu4K.6

@inproceedings{91f6f460ed11439489134f113b950d74,

title = "Deep learning enables virtual histological staining of label-free tissue sections using auto-fluorescence",

abstract = "We report a data-driven method for label-free virtual histological staining of tissue sections using deep learning. This framework is successfully demonstrated by inferring multiple types of stains on different tissue types using auto-fluorescence signal.",

author = "Yair Rivenson and Hongda Wang and \{De Haan\}, Kevin and Zhensong Wei and Aydogan Ozcan",

note = "Publisher Copyright: {\textcopyright} 2019 The Author(s); CLEO: Applications and Technology, CLEO\_AT 2019 ; Conference date: 05-05-2019 Through 10-05-2019",

year = "2019",

month = jan,

day = "1",

doi = "10.1364/CLEO\_AT.2019.ATu4K.6",

language = "English",

isbn = "9781943580576",

series = "Optics InfoBase Conference Papers",

publisher = "Optica Publishing Group (formerly OSA)",

booktitle = "CLEO",

}

Rivenson, Y, Wang, H, De Haan, K, Wei, Z & Ozcan, A 2019, Deep learning enables virtual histological staining of label-free tissue sections using auto-fluorescence. in CLEO: Applications and Technology, CLEO_AT 2019. Optics InfoBase Conference Papers, vol. Part F127-CLEO_AT 2019, Optica Publishing Group (formerly OSA), CLEO: Applications and Technology, CLEO_AT 2019, San Jose, United States, 5/05/19. https://doi.org/10.1364/CLEO_AT.2019.ATu4K.6

Deep learning enables virtual histological staining of label-free tissue sections using auto-fluorescence. / Rivenson, Yair; Wang, Hongda; De Haan, Kevin et al.
CLEO: Applications and Technology, CLEO_AT 2019. Optica Publishing Group (formerly OSA), 2019. (Optics InfoBase Conference Papers; Vol. Part F127-CLEO_AT 2019).

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

TY - GEN

T1 - Deep learning enables virtual histological staining of label-free tissue sections using auto-fluorescence

AU - Rivenson, Yair

AU - Wang, Hongda

AU - De Haan, Kevin

AU - Wei, Zhensong

AU - Ozcan, Aydogan

PY - 2019/1/1

Y1 - 2019/1/1

N2 - We report a data-driven method for label-free virtual histological staining of tissue sections using deep learning. This framework is successfully demonstrated by inferring multiple types of stains on different tissue types using auto-fluorescence signal.

AB - We report a data-driven method for label-free virtual histological staining of tissue sections using deep learning. This framework is successfully demonstrated by inferring multiple types of stains on different tissue types using auto-fluorescence signal.

UR - https://www.scopus.com/pages/publications/85068115101

U2 - 10.1364/CLEO_AT.2019.ATu4K.6

DO - 10.1364/CLEO_AT.2019.ATu4K.6

M3 - Conference contribution

AN - SCOPUS:85068115101

SN - 9781943580576

T3 - Optics InfoBase Conference Papers

BT - CLEO

PB - Optica Publishing Group (formerly OSA)

T2 - CLEO: Applications and Technology, CLEO_AT 2019

Y2 - 5 May 2019 through 10 May 2019

ER -

Deep learning enables virtual histological staining of label-free tissue sections using auto-fluorescence

Abstract

Publication series

Conference

ASJC Scopus subject areas

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