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, RogerRJ
• 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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=753097
• 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尖端进步，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)无标记虚拟组织成像和(2)基于设想的虚拟图像的省时的癌组织术中诊断系统。它包括艾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Zemp小组开创的新型成像技术，在不进行切割、固定或染色程序的情况下形成组织的虚拟组织图像。LI小组将开发基于深度学习的染色转移模型来呈现这种虚拟组织图像，使它们几乎无法与真正的金标准病理图像区分开来，并研究包含可解释人工智能(XAI)的术中深度诊断模型，以确定切除组织边缘是否存在肿瘤组织及其位置。从长远来看，我们的目标是为外科医生提供一种工具，利用这种新的成像技术和深度学习工具集来评估是否有需要切除的残留肿瘤组织。在这一过程中，第二个目标是开发深度学习方法，能够提供自动化的组织学决策并生成热图，以引导病理学家的注意力，以便快速审查和加速工作流程。