HoechstGAN: virtual lymphocyte staining using generative adversarial networks

Georg Wolflein; In Hwa Um; David J. Harrison; Ognjen Arandjelovic

doi:10.1109/wacv56688.2023.00497

HoechstGAN: virtual lymphocyte staining using generative adversarial networks

Georg Wolflein^*, In Hwa Um, David J. Harrison, Ognjen Arandjelovic

^*Corresponding author for this work

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

4 Citations (Scopus)

Abstract

The presence and density of specific types of immune cells are important to understand a patient’s immune response to cancer. However, immunofluorescence staining required to identify T cell subtypes is expensive, time-consuming, and rarely performed in clinical settings. We present a framework to virtually stain Hoechst images (which are cheap and widespread) with both CD3 and CD8 to identify T cell subtypes in clear cell renal cell carcinoma using generative adversarial networks. Our proposed method jointly learns both staining tasks, incentivising the network to incorporate mutually beneficial information from each task. We devise a novel metric to quantify the virtual staining quality, and use it to evaluate our method.

Original language	English
Title of host publication	2023 IEEE/CVF Winter conference on applications of computer vision (WACV)
Publisher	IEEE
Pages	4986-4996
Number of pages	11
ISBN (Electronic)	9781665493468
ISBN (Print)	9781665493475
DOIs	https://doi.org/10.1109/wacv56688.2023.00497
Publication status	Published - 6 Feb 2023
Event	IEEE/CVF Winter Conference on Applications of Computer Vision - Waikoloa, United States Duration: 3 Jan 2023 → 7 Jan 2023 https://wacv2023.thecvf.com https://doi.org/10.1109/WACV56688.2023

Publication series

Name	2023 IEEE/CVF Winter conference on applications of computer vision (WACV)
Publisher	IEEE
ISSN (Print)	2472-6737
ISSN (Electronic)	2642-9381

Conference

Conference	IEEE/CVF Winter Conference on Applications of Computer Vision
Abbreviated title	WACV 2023
Country/Territory	United States
City	Waikoloa
Period	3/01/23 → 7/01/23
Internet address	https://wacv2023.thecvf.com https://doi.org/10.1109/WACV56688.2023

UN SDGs

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

Access to Document

10.1109/wacv56688.2023.00497

https://openaccess.thecvf.com/content/WACV2023/papers/Wolflein_HoechstGAN_Virtual_Lymphocyte_Staining_Using_Generative_Adversarial_Networks_WACV_2023_paper.pdf

Cite this

Wolflein, G., Um, I. H., Harrison, D. J., & Arandjelovic, O. (2023). HoechstGAN: virtual lymphocyte staining using generative adversarial networks. In 2023 IEEE/CVF Winter conference on applications of computer vision (WACV) (pp. 4986-4996). Article 10030547 (2023 IEEE/CVF Winter conference on applications of computer vision (WACV)). IEEE. https://doi.org/10.1109/wacv56688.2023.00497

@inproceedings{95454d2f584640e4895365973a35b8af,

title = "HoechstGAN: virtual lymphocyte staining using generative adversarial networks",

abstract = "The presence and density of specific types of immune cells are important to understand a patient{\textquoteright}s immune response to cancer. However, immunofluorescence staining required to identify T cell subtypes is expensive, time-consuming, and rarely performed in clinical settings. We present a framework to virtually stain Hoechst images (which are cheap and widespread) with both CD3 and CD8 to identify T cell subtypes in clear cell renal cell carcinoma using generative adversarial networks. Our proposed method jointly learns both staining tasks, incentivising the network to incorporate mutually beneficial information from each task. We devise a novel metric to quantify the virtual staining quality, and use it to evaluate our method.",

author = "Georg Wolflein and Um, {In Hwa} and Harrison, {David J.} and Ognjen Arandjelovic",

note = "Funding: GW is supported by Lothian NHS. This project received funding from the European Union{\textquoteright}s Horizon 2020 research and innovation programme under Grant Agreement No. 101017453 as part of the KATY project. This work is supported in part by the Industrial Centre for AI Research in Digital Diagnostics (iCAIRD) which is funded by Innovate UK on behalf of UK Research and Innovation (UKRI) (project number 104690).; IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2023 ; Conference date: 03-01-2023 Through 07-01-2023",

year = "2023",

month = feb,

day = "6",

doi = "10.1109/wacv56688.2023.00497",

language = "English",

isbn = "9781665493475",

series = "2023 IEEE/CVF Winter conference on applications of computer vision (WACV)",

publisher = "IEEE",

pages = "4986--4996",

booktitle = "2023 IEEE/CVF Winter conference on applications of computer vision (WACV)",

url = "https://wacv2023.thecvf.com, https://doi.org/10.1109/WACV56688.2023",

}

Wolflein, G , Um, IH , Harrison, DJ & Arandjelovic, O 2023, HoechstGAN: virtual lymphocyte staining using generative adversarial networks. in 2023 IEEE/CVF Winter conference on applications of computer vision (WACV)., 10030547, 2023 IEEE/CVF Winter conference on applications of computer vision (WACV), IEEE, pp. 4986-4996, IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, Hawaii, United States, 3/01/23. https://doi.org/10.1109/wacv56688.2023.00497

HoechstGAN: virtual lymphocyte staining using generative adversarial networks. / Wolflein, Georg ; Um, In Hwa ; Harrison, David J. et al.
2023 IEEE/CVF Winter conference on applications of computer vision (WACV). IEEE, 2023. p. 4986-4996 10030547 (2023 IEEE/CVF Winter conference on applications of computer vision (WACV)).

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

TY - GEN

T1 - HoechstGAN

T2 - IEEE/CVF Winter Conference on Applications of Computer Vision

AU - Wolflein, Georg

AU - Um, In Hwa

AU - Harrison, David J.

AU - Arandjelovic, Ognjen

N1 - Funding: GW is supported by Lothian NHS. This project received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 101017453 as part of the KATY project. This work is supported in part by the Industrial Centre for AI Research in Digital Diagnostics (iCAIRD) which is funded by Innovate UK on behalf of UK Research and Innovation (UKRI) (project number 104690).

PY - 2023/2/6

Y1 - 2023/2/6

N2 - The presence and density of specific types of immune cells are important to understand a patient’s immune response to cancer. However, immunofluorescence staining required to identify T cell subtypes is expensive, time-consuming, and rarely performed in clinical settings. We present a framework to virtually stain Hoechst images (which are cheap and widespread) with both CD3 and CD8 to identify T cell subtypes in clear cell renal cell carcinoma using generative adversarial networks. Our proposed method jointly learns both staining tasks, incentivising the network to incorporate mutually beneficial information from each task. We devise a novel metric to quantify the virtual staining quality, and use it to evaluate our method.

AB - The presence and density of specific types of immune cells are important to understand a patient’s immune response to cancer. However, immunofluorescence staining required to identify T cell subtypes is expensive, time-consuming, and rarely performed in clinical settings. We present a framework to virtually stain Hoechst images (which are cheap and widespread) with both CD3 and CD8 to identify T cell subtypes in clear cell renal cell carcinoma using generative adversarial networks. Our proposed method jointly learns both staining tasks, incentivising the network to incorporate mutually beneficial information from each task. We devise a novel metric to quantify the virtual staining quality, and use it to evaluate our method.

UR - https://doi.org/10.1109/WACV56688.2023

U2 - 10.1109/wacv56688.2023.00497

DO - 10.1109/wacv56688.2023.00497

M3 - Conference contribution

SN - 9781665493475

T3 - 2023 IEEE/CVF Winter conference on applications of computer vision (WACV)

SP - 4986

EP - 4996

BT - 2023 IEEE/CVF Winter conference on applications of computer vision (WACV)

PB - IEEE

Y2 - 3 January 2023 through 7 January 2023

ER -

HoechstGAN: virtual lymphocyte staining using generative adversarial networks

Abstract

Publication series

Conference

UN SDGs

Access to Document

Other files and links

Fingerprint

KATY: KATY: Knowledge At the Tip of Your fingers: Clinical Knowledge for Humanity

ICAIRD: I-CAIRD: Industrial Centre for AI Research in Digital Diagnostics

Cite this

HoechstGAN: virtual lymphocyte staining using generative adversarial networks

Abstract

Publication series

Conference

UN SDGs

Access to Document

Other files and links

Fingerprint

Projects

KATY: KATY: Knowledge At the Tip of Your fingers: Clinical Knowledge for Humanity

ICAIRD: I-CAIRD: Industrial Centre for AI Research in Digital Diagnostics

Cite this