论文标题
ICOS蛋白表达分割：变压器网络可以给出更好的结果吗？
ICOS Protein Expression Segmentation: Can Transformer Networks Give Better Results?
论文作者
论文摘要
生物标志物确定患者对治疗的反应。随着基于变压器网络的人工智能的最新进展，仅进行了有限的研究来衡量具有挑战性的组织病理学图像的性能。在本文中，我们研究了众多最先进的变压器网络对免疫 - 可诱导型TCELL costimulator（ICOS）蛋白细胞分割在免疫组织化学（IHC）幻灯片中结肠癌中的疗效。广泛而全面的实验结果证实，与其余评估的变压器和有效的U-NET方法相比，Missformer的骰子得分最高74.85％。
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.
