这篇文章介绍了一种通过多重免疫荧光（mIF）与苏木精和伊红（H&E）图像协同注册来自动化组织病理学细胞注释和分类的新颖方法。它详细阐述了如何利用深度学习模型，结合自监督学习和领域适应技术，对H&E图像中的四种主要细胞类型进行分类，并取得了86%至89%的整体准确率。该研究强调了这种自动化方法在识别空间生物标志物方面的潜力，这些生物标志物与癌症患者的生存率和对免疫检查点抑制剂的反应相关。通过消除人工注释的低效和误差，此技术有望推动精准肿瘤学的发展，提供一种可扩展的、用于常规组织病理学单细胞分析的强大工具。References: Li Z, Mirjahanmardi S H, Sali R, et al. Automated cell annotation and classification on histopathology for spatial biomarker discovery[J]. Nature Communications, 2025, 16(1): 6240.
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