Machine Learning Techniques for Evaluating Disease and Drugs Effectiveness in Fibre-Bundle Endomicroscopy Systems

用于评估纤维束内镜系统中疾病和药物有效性的机器学习技术

• 批准号: 2589298
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2589298
• 关键词: Machine; Learning; Techniques; Evaluating; Disease

In partnership with GlaxoSmithKline (GSK) and the National Physical Laboratory (NPL), the project will build and utilise strong signal and image processing or machine learning skills to develop algorithms to improve the quality of data received from a Fluorescent Lifetime Imaging (FLIM) system called Kronoscan. There is an emphasis on developing robust algorithms that can be translated in clinical applications.

该项目将与葛兰素史克（GSK）和国家物理实验室（NPL）合作，建立并利用强大的信号和图像处理或机器学习技能来开发算法，以提高从称为Kronoscan的荧光寿命成像（FLIM）系统接收的数据质量。重点是开发可在临床应用中转化的稳健算法。