Deep learning for digital and virtual histology

数字和虚拟组织学的深度学习

• 批准号: 567581-2021
• 负责人: Zemp, Roger
• 金额: $ 6.56万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=722620
• 关键词: Deep; learning; digital; virtual; histology

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.

拟议的项目将利用组织成像技术和机器学习的前沿进展，开发下一代虚拟和数字病理学工具集，包括基于人工智能的算法：（a）无标签虚拟组织学成像和（2）在设想的虚拟图像上的时间效率高的癌组织术中诊断系统。它包括阿尔伯塔大学研究人员Roger Zemp和Xingyu Li与机器学习公司AltaML之间的合作。特别是，它将利用Zemp集团开创的新型成像技术，在不进行切割、固定或染色的情况下形成组织的虚拟组织学图像。Li团队将开发基于深度学习的染色转移模型，以渲染此类虚拟组织学图像，使其与真正的黄金标准病理学图像几乎无法区分，并研究深度术中诊断模型，该模型包含可解释的人工智能（XAI），以确定切除组织边缘肿瘤组织的存在和位置。从长远来看，我们的目标是为外科医生提供一种工具，使用这种新型成像技术沿着深度学习工具集，评估是否有需要切除的残留肿瘤组织。沿着这段旅程，第二个目标是开发深度学习方法，可以提供自动组织学决策并生成热图，以引导病理学家的注意力，从而进行快速审查和加速工作流程。