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.

翻译：生物标记物确定了病人对治疗的反应。由于最近在基于变异器网络的人工智能方面的进步,在测量挑战性病理图象的性能方面只进行了有限的研究。在本文中,我们调查了免疫检查点生物标记的无数最先进的变异器网络的功效,即从免疫生物学化学幻灯片中导出结肠癌的Tcell COStimulator蛋白细胞分解。广泛而全面的实验结果证实,MiSSFormer取得了比其他经评估的变异器和高效的U-Net方法最高达74.85%的Dice分数。