Development of deepometry for the supervised and weakly supervised learning of imaging data

用于成像数据监督和弱监督学习的深度测量的发展

• 批准号: 2748735
• 金额: --
• 依托单位: Swansea University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2748735
• 关键词: Development; deepometry; supervised; weakly; learning

Imaging flow cytometry offers the opportunity to image millions of cells in a short period of time, it is a transformative technology with the potential to diagnose a host of diseases. This project will use the latest algorithms and analytic techniques to extract useful information from large datasets generated using this system for a host of clinical collaborations. Specifically, the project builds on, Deepometry, an open-source workflow developed with the objective of applying deep learning algorithms along with single cell analytics to the analysis of cytometry data which was developed in collaboration with GSK and the Broad Institute of MIT and Harvard. 'Deepometry 2' aims to develop our current software for use on both 2D and 3D images and to use the technology to image tissue from patient samples. This project will involve software development and the application of this deep learning tool to a number of exciting datasets.

成像流式细胞术提供了在短时间内对数百万个细胞进行成像的机会，它是一种具有诊断多种疾病潜力的变革性技术。该项目将使用最新的算法和分析技术，从使用该系统生成的大型数据集中提取有用的信息，用于许多临床合作。具体来说，该项目建立在Deepometry的基础上，Deepometry是一个开源工作流程，其目标是将深度学习算法沿着单细胞分析应用于细胞计数数据的分析，该分析是与GSK和麻省理工学院和哈佛的布罗德研究所合作开发的。“深度测量2”旨在开发我们目前的软件，用于2D和3D图像，并使用该技术对患者样本的组织进行成像。该项目将涉及软件开发以及将这种深度学习工具应用于许多令人兴奋的数据集。