Skip to main content

Research Repository

Advanced Search

ICOSeg: real-time ICOS protein expression segmentation from immunohistochemistry slides using a lightweight conv-transformer network.

Singh, Vivek Kumar; Sarker, Md. Mostafa Kamal; Makhlouf, Yasmine; Craig, Stephanie G.; Humphries, Matthew P.; Loughrey, Maurice B.; James, Jacqueline A.; Salto-Tellez, Manuel; O�Reilly, Paul; Maxwell, Perry

Authors

Vivek Kumar Singh

Md. Mostafa Kamal Sarker

Yasmine Makhlouf

Stephanie G. Craig

Matthew P. Humphries

Maurice B. Loughrey

Jacqueline A. James

Manuel Salto-Tellez

Paul O�Reilly

Perry Maxwell



Abstract

In this article, we propose ICOSeg, a lightweight deep learning model that accurately segments the immune-checkpoint biomarker, Inducible T-cell COStimulator (ICOS) protein in colon cancer from immunohistochemistry (IHC) slide patches. The proposed model relies on the MobileViT network that includes two main components: convolutional neural network (CNN) layers for extracting spatial features; and a transformer block for capturing a global feature representation from IHC patch images. The ICOSeg uses an encoder and decoder sub-network. The encoder extracts the positive cell's salient features (i.e., shape, texture, intensity, and margin), and the decoder reconstructs important features into segmentation maps. To improve the model generalization capabilities, we adopted a channel attention mechanism that added to the bottleneck of the encoder layer. This approach highlighted the most relevant cell structures by discriminating between the targeted cell and background tissues. We performed extensive experiments on our in-house dataset. The experimental results confirm that the proposed model achieves more significant results against state-of-the-art methods, together with an 8× reduction in parameters.

Citation

SINGH, V.K., SARKER, M.M.K., MAKHLOUF, Y., CRAIG, S.G., HUMPHRIES, M.P., LOUGHREY, M.B., JAMES, J.A., SALTO-TELLEZ, M., O'REILLY, P. and MAXWELL, P. 2022. ICOSeg: real-time ICOS protein expression segmentation from immunohistochemistry slides using a lightweight conv-transformer network. Cancers [online], 14(16), article 3910. Available from: https://doi.org/10.3390/cancers14163910

Journal Article Type Article
Acceptance Date Aug 11, 2022
Online Publication Date Aug 13, 2022
Publication Date Aug 31, 2022
Deposit Date Sep 8, 2022
Publicly Available Date Mar 29, 2024
Journal Cancers
Electronic ISSN 2072-6694
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 14
Issue 16
Article Number 3910
DOI https://doi.org/10.3390/cancers14163910
Keywords Colon cancer; Immunohistochemistry; ICOS; Deep learning; Channel attention
Public URL https://rgu-repository.worktribe.com/output/1745128

Files





You might also like



Downloadable Citations