Deep learning for digital and virtual histology

拟议的项目将利用组织成像技术和机器学习的前沿进展，利用基于人工智能的算法开发下一代虚拟和数字病理工具集：(a)无标签虚拟组织学成像和(2)基于预期虚拟图像的高效癌组织术中诊断系统。它包括阿尔伯塔大学研究人员罗杰·泽普和李星宇与机器学习公司AltaML的合作伙伴关系。特别是，它将利用由Zemp团队首创的新型成像技术，无需切割、固定或染色程序即可形成组织的虚拟组织学图像。Li小组将开发基于深度学习的染色转移模型，以呈现这种虚拟组织学图像，使它们与真正的金标准病理图像几乎无法区分，并研究包含可解释人工智能（XAI）的深度术中诊断模型，以确定切除组织边缘肿瘤组织的存在和位置。从长远来看，目标是为外科医生提供一种工具来评估是否存在需要切除的残留肿瘤组织，使用这种新颖的成像技术和深度学习工具集。在此过程中，第二个目标是开发深度学习方法，该方法可以提供自动化的组织学决策并生成热图，以指导病理学家的注意力，以便快速审查和加速工作流程。

The proposed project will leverage the cutting-edge advances in tissue imaging techniques and machine learning to develop next generation virtual and digital pathology toolsets with artificial intelligence-based algorithms: (a) label-free virtual histological imaging and (2) time-efficient cancerous tissue intraoperative diagnosis system on the envisioned virtual images. It includes a partnership between University of Alberta researchers Roger Zemp and Xingyu Li and a Machine Learning company, AltaML. Particularly, it will leverage novel imaging technology pioneered by the Zemp group to form virtual histological images of tissues without cutting, fixing, or staining procedures. The Li group will develop deep-learning based stain-transfer models to render such virtual histological images so that they are virtually indistinguishable from true gold-standard pathology images and investigate deep intraoperative diagnostic models that incorporate explainable artificial intelligence (XAI) to determine the presence and locations of tumor tissues in margins of resected tissues. In the long-term, the goal is to provide a surgeon with a tool to assess whether there may be residual tumor tissues which need removal, using this novel imaging technology along with deep learning toolsets. Along this journey, a secondary goal is to develop deep learning methods that can provide automated histological decision making and generate heatmaps to direct a pathologist's attention for rapid review and accelerated workflow.