Advancing Federated Learning of Neural Networks for Medical Imaging

推进医学成像神经网络的联合学习

• 批准号: 2594573
• 金额: --
• 依托单位: University of Oxford
• 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-2594573
• 关键词: Advancing; Federated; Learning; Neural; Networks

Machine Learning (ML) algorithms, computational methods that learn to detect patterns in data, promise to improve diagnosis and treatment of disease by enabling fast and accurate medical image analysis. State of the art ML methods, Deep Neural Networks (DNNs), are commonly trained to identify patterns using manually labelled data, such as pairs of medical scans and corresponding manally-generated labels that describe what pathology the scans show. Such labelled medical data are limited because annotation by clinicians is expensive. Moreover, aggregating data from clinical centres across the world in one central database is often infeasible due to privacy concerns. As a result, training databases are small and do not capture the real heterogeneity in clinical practice (rare pathologies, different scanners, etc). Consequently, DNNs trained on such limited data do not generalize well, which hinders their adoption in healthcare. This project will develop methods that enable multiple institutions to collaborate and train a single DNN on their data, without the need to centrally aggregate them in one computational node. This framework is known as Federated Learning (FL) of DNNs between multiple computational nodes (institutions). Models trained with FL could potentially generalize better by learning from diverse databases collected across the world. This can lead to powerful and reliable ML tools for improved disease diagnosis and treatment.FL has the potential to become the standard paradigm for large-scale, international studies on ML for healthcare. There are multiple technical challenges, however, hindering its effective use. This project tackles the following:a) Data acquired at different clinical centres have heterogeneous characteristics, such as due to varying patient demographics or acquisition scanners. When FL is performed between databases with such systematic differences, model optimization is suboptimal. This is because common optimization methods assume the data are identically and independently distributed (iid), which is not true in an FL setting. This project will develop optimization algorithms for FL with non-iid data to improve its effectiveness.b) Performance of existing DNNs is unreliable when applied on data that present different characteristics from those used for training. We will investigate how to identify and model factors of variation between databases used for FL (e.g. from different institutions), enabling inference about expected variability after deployment, to improve model generalization.c) Labels are often limited in healthcare, whereas unlabelled data are abundant. FL methods have been primarily designed for learning using labels. This project will develop FL using unlabelled data, enabling any institution to provide their unlabelled data in a collaborative consortium, to allow models capture better the true data heterogeneity across the world.This research is timely and will advance medical image analysis and the field of ML. Value of FL in healthcare has been demonstrated previously, generating great interest, but technical challenges limit its use. Learning from non-iid and unlabelled data are long standing challenges in ML yet to be solved. Hence results by this project are valuable for medical image analysis but also of interest to other domains. This project falls within the EPSRC Medical Imaging research area and the Healthcare Technologies theme. Its ultimate goal is to create effective FL tools to enable the medical imaging community perform collaborative studies and improve disease diagnosis and treatment.This research is conducted at the University of Oxford within the Institute of Biomedical Engineering, in collaboration with the Big-Data Institute. It is facilitated by existing collaborations with Imperial College London and University of Cambridge, and will seek to establish new ones within UK and internationally.

机器学习（ML）算法，学会检测数据模式的计算方法，有望通过快速准确的医学图像分析来改善疾病的诊断和治疗。最先进的ML方法，深神经网络（DNN），通常接受使用手动标记的数据（例如医学扫描对以及相应的人为生成的标签）来识别模式，以描述扫描显示的病理。这种标记的医疗数据是有限的，因为临床医生的注释很昂贵。此外，由于隐私问题，全球临床中心的汇总数据通常是不可行的。结果，培训数据库很小，并且不会捕获临床实践中的实际异质性（罕见的病理，不同的扫描仪等）。因此，接受过这种有限数据培训的DNN不能很好地概括，这阻碍了他们在医疗保健中的采用。该项目将开发方法，使多个机构能够在其数据上协作和培训单个DNN，而无需将它们集中在一个计算节点中。该框架称为多个计算节点（机构）之间DNN的联合学习（FL）。通过FL培训的模型可以通过从全球收集的各种数据库中学习来更好地概括。这可能会导致强大而可靠的ML工具，以改善疾病诊断和治疗。FL有可能成为大型，国际医疗保健国际研究的标准范式。但是，存在多种技术挑战，阻碍了其有效使用。该项目解决了以下内容：a）在不同临床中心获取的数据具有异质特征，例如由于患者人口统计或摄取扫描仪的不同。当在具有这种系统差异的数据库之间执行FL时，模型优化是次优的。这是因为通用优化方法假定数据是相同和独立分布的（IID），这在FL设置中是不正确的。该项目将通过非IID数据为FL开发优化算法以提高其有效性。B）现有DNN的性能是不可靠的。我们将调查如何识别用于FL的数据库之间的变化因素（例如来自不同机构的），实现对部署后预期可变性的推断，以改善模型概括。 FL方法主要是为使用标签学习而设计的。该项目将使用未标记的数据开发FL，使任何机构能够在协作财团中提供其未标记的数据，以允许模型更好地捕获全球的真实数据异质性。这项研究是及时的，并且将推进医疗图像分析和ML领域。以前已经证明了FL在医疗保健中的价值，引起了极大的兴趣，但是技术挑战限制了其使用。从非IID和未标记的数据中学习是ML尚未解决的长期挑战。因此，该项目的结果对于医学图像分析很有价值，也对其他领域感兴趣。该项目属于EPSRC医学成像研究领域和医疗技术主题。它的最终目标是创建有效的FL工具，以使医学成像社区进行协作研究并改善疾病的诊断和治疗。这项研究是在生物医学工程研究所的牛津大学与大数据研究所合作进行的。现有的与伦敦帝国学院和剑桥大学的现有合作促进了它，并将寻求在英国和国际上建立新的合作。