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.

成像流式细胞仪提供了在短时间内成像数百万个细胞的机会，这是一种变革性的技术，具有诊断疾病的可能性。该项目将使用最新的算法和分析技术来从使用该系统生成的大量临床协作中提取有用的信息。具体而言，该项目建立在深度测定器基础上，这是一种开发的开源工作流程，其目的是将深度学习算法以及单细胞分析以及与GSK和MIT和MIT和哈佛大学研究所合作开发的细胞仪数据分析。 “深度测定2”旨在开发我们当前的软件，用于在2D和3D图像上使用，并使用该技术从患者样品中对组织进行成像。该项目将涉及软件开发以及该深度学习工具在许多令人兴奋的数据集中的应用。