Neural network-based multiplexed and micro-structured virtual staining of unlabeled tissue

Yijie Zhang; Kevin de Haan; Jingxi Li; Yair Rivenson; Aydogan Ozcan

Neural network-based multiplexed and micro-structured virtual staining of unlabeled tissue

Yijie Zhang, Kevin de Haan, Jingxi Li, Yair Rivenson, Aydogan Ozcan

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

Abstract

We present a method to generate multiple virtual stains on an image of label-free tissue using a single deep neural network, which is fed with the autofluorescence images of the unlabeled tissue alongside a user-defined digital-staining matrix. Users can indicate which stain to apply on each pixel by editing the digital-staining matrix and blend multiple virtual stains, creating entirely new stain combinations.

Original language	English
Title of host publication	CLEO
Subtitle of host publication	Applications and Technology, A and T 2022
Publisher	Optica Publishing Group (formerly OSA)
ISBN (Electronic)	9781957171050
State	Published - 1 Jan 2022
Externally published	Yes
Event	CLEO: Applications and Technology, A and T 2022 - San Jose, United States Duration: 15 May 2022 → 20 May 2022

Publication series

Name	Optics InfoBase Conference Papers
ISSN (Electronic)	2162-2701

Conference

Conference	CLEO: Applications and Technology, A and T 2022
Country/Territory	United States
City	San Jose
Period	15/05/22 → 20/05/22

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Mechanics of Materials

Cite this

@inproceedings{af2412eb405947abaa846aad4aef428b,

title = "Neural network-based multiplexed and micro-structured virtual staining of unlabeled tissue",

abstract = "We present a method to generate multiple virtual stains on an image of label-free tissue using a single deep neural network, which is fed with the autofluorescence images of the unlabeled tissue alongside a user-defined digital-staining matrix. Users can indicate which stain to apply on each pixel by editing the digital-staining matrix and blend multiple virtual stains, creating entirely new stain combinations.",

author = "Yijie Zhang and \{de Haan\}, Kevin and Jingxi Li and Yair Rivenson and Aydogan Ozcan",

note = "Publisher Copyright: {\textcopyright} Optica Publishing Group 2022, {\textcopyright} 2022 The Author(s); CLEO: Applications and Technology, A and T 2022 ; Conference date: 15-05-2022 Through 20-05-2022",

year = "2022",

month = jan,

day = "1",

language = "English",

series = "Optics InfoBase Conference Papers",

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

booktitle = "CLEO",

}

Zhang, Y, de Haan, K, Li, J, Rivenson, Y & Ozcan, A 2022, Neural network-based multiplexed and micro-structured virtual staining of unlabeled tissue. in CLEO: Applications and Technology, A and T 2022., ATh2I.2, Optics InfoBase Conference Papers, Optica Publishing Group (formerly OSA), CLEO: Applications and Technology, A and T 2022, San Jose, United States, 15/05/22.

Neural network-based multiplexed and micro-structured virtual staining of unlabeled tissue. / Zhang, Yijie; de Haan, Kevin; Li, Jingxi et al.
CLEO: Applications and Technology, A and T 2022. Optica Publishing Group (formerly OSA), 2022. ATh2I.2 (Optics InfoBase Conference Papers).

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

TY - GEN

T1 - Neural network-based multiplexed and micro-structured virtual staining of unlabeled tissue

AU - Zhang, Yijie

AU - de Haan, Kevin

AU - Li, Jingxi

AU - Rivenson, Yair

AU - Ozcan, Aydogan

PY - 2022/1/1

Y1 - 2022/1/1

N2 - We present a method to generate multiple virtual stains on an image of label-free tissue using a single deep neural network, which is fed with the autofluorescence images of the unlabeled tissue alongside a user-defined digital-staining matrix. Users can indicate which stain to apply on each pixel by editing the digital-staining matrix and blend multiple virtual stains, creating entirely new stain combinations.

AB - We present a method to generate multiple virtual stains on an image of label-free tissue using a single deep neural network, which is fed with the autofluorescence images of the unlabeled tissue alongside a user-defined digital-staining matrix. Users can indicate which stain to apply on each pixel by editing the digital-staining matrix and blend multiple virtual stains, creating entirely new stain combinations.

UR - https://www.scopus.com/pages/publications/85136830789

M3 - Conference contribution

AN - SCOPUS:85136830789

T3 - Optics InfoBase Conference Papers

BT - CLEO

PB - Optica Publishing Group (formerly OSA)

T2 - CLEO: Applications and Technology, A and T 2022

Y2 - 15 May 2022 through 20 May 2022

ER -

Neural network-based multiplexed and micro-structured virtual staining of unlabeled tissue

Abstract

Publication series

Conference

ASJC Scopus subject areas

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Cite this