Label-free intraoperative histological imaging

该项目的目的是将机器学习应用于组织学成像，以确定癌变组织和健康组织之间的差异。目前，当外科医生进行手术切除肿瘤组织时，组织样本被采集并由专家在病理学实验室单独检查，结果在两周后返回给外科医生。这可能导致患者需要第二次手术切除任何剩余的癌组织，这是危险和昂贵的。该项目旨在应用机器学习，以便在采集组织学样本时，可以将其放在显微镜下，在初始手术期间通过人工神经网络进行分析，并在患者仍处于全身麻醉状态时提供结果。这项技术随后将应用于中第三系，以提供更大的成像领域和对样品的更深入分析。培训将在斯特拉斯克莱德大学、国家物理实验室和伦敦大学学院进行，以涵盖该项目的硬件和软件方面。

The aim of this project is to apply machine learning to histological imaging to determine the difference between cancerous and healthy tissue. At the moment, when a surgeon performs surgery to remove tumour tissue, histological samples are taken and reviewed separately in a pathology lab by experts with the results returned to the surgeon up to two weeks later. This can result in the patient needing a second surgery to remove any remaining cancerous tissue, something that is dangerous and costly. This project aims to apply machine learning so that when a histological sample is taken, it can be placed under a microscope and analysed by an artificial neural network during the initial surgery and results provided whilst the patient is still under general anaesthesia. This technique will then be applied to the Mesolens to provide a larger field of imaging and greater analysis of the sample. Training will take place at Strathclyde University as well as the National Physical Laboratory and University College London to cover the hardware and software aspects of this project.