In pathology and biomedical research, histology is the cornerstone method for tissue analysis. Currently, the histological workflow consumes plenty of chemicals, water, and time for staining procedures. Deep learning is now enabling digital replacement of parts of the histological staining procedure. In virtual staining, histological stains are created by training neural networks to produce stained images from an unstained tissue image, or through transferring information from one stain to another. These technical innovations provide more sustainable, rapid, and cost-effective alternatives to traditional histological pipelines, but their development is in an early phase and requires rigorous validation. In this review we cover the basic concepts of virtual staining for histology and provide future insights into the utilization of artificial intelligence (AI)-enabled virtual histology.

中文翻译：

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通过深度学习进行组织学虚拟染色

在病理学和生物医学研究中，组织学是组织分析的基石方法。目前，组织学工作流程消耗大量化学品、水和染色程序的时间。深度学习现在可以数字化替换部分组织学染色程序。在虚拟染色中，组织学染色是通过训练神经网络从未染色的组织图像产生染色图像，或通过将信息从一种染色转移到另一种染色来创建的。这些技术创新为传统组织学管道提供了更可持续、更快速且更具成本效益的替代方案，但其开发仍处于早期阶段，需要严格的验证。在这篇综述中，我们介绍了组织学虚拟染色的基本概念，并提供了对人工智能 (AI) 虚拟组织学应用的未来见解。