ICOS Protein Expression Segmentation: Can Transformer Networks Give Better Results?

TL;DR

This paper evaluates the performance of various Transformer networks for ICOS protein cell segmentation in colon cancer histopathology images, demonstrating that MiSSFormer outperforms other models with a Dice score of 74.85%.

Contribution

It introduces a comprehensive comparison of Transformer-based models for biomarker segmentation in challenging histopathology images, highlighting MiSSFormer's superior performance.

Findings

MiSSFormer achieved the highest Dice score of 74.85%.

Transformer networks can effectively segment ICOS protein in colon cancer images.

The study provides a benchmark for future Transformer-based histopathology segmentation methods.

Abstract

Biomarkers identify a patients response to treatment. With the recent advances in artificial intelligence based on the Transformer networks, there is only limited research has been done to measure the performance on challenging histopathology images. In this paper, we investigate the efficacy of the numerous state-of-the-art Transformer networks for immune-checkpoint biomarker, Inducible Tcell COStimulator (ICOS) protein cell segmentation in colon cancer from immunohistochemistry (IHC) slides. Extensive and comprehensive experimental results confirm that MiSSFormer achieved the highest Dice score of 74.85% than the rest evaluated Transformer and Efficient U-Net methods.