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) 算法是一种学习检测数据模式的计算方法，有望通过实现快速、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，即深度神经网络 (DNN)，通常经过训练以使用手动标记的数据来识别模式，例如成对的医学扫描和相应的手动生成的标签，用于描述扫描显示的病理情况。这种标记的医疗数据是有限的，因为临床医生的注释成本很高。此外，由于隐私问题，将世界各地临床中心的数据汇总到一个中央数据库通常是不可行的。因此，训练数据库很小，无法捕捉临床实践中真正的异质性（罕见的病理、不同的扫描仪等）。因此，在如此有限的数据上训练的 DNN 不能很好地泛化，这阻碍了它们在医疗保健领域的采用。该项目将开发一种方法，使多个机构能够协作并根据其数据训练单个 DNN，而无需将它们集中聚合在一个计算节点中。该框架称为多个计算节点（机构）之间的 DNN 联邦学习 (FL)。通过 FL 训练的模型可以通过从世界各地收集的不同数据库中学习来更好地概括。这可以带来强大而可靠的 ML 工具，以改善疾病诊断和治疗。FL 有潜力成为医疗保健 ML 的大规模国际研究的标准范例。然而，存在多种技术挑战阻碍其有效使用。该项目解决以下问题：a) 在不同临床中心采集的数据具有异质特征，例如由于患者人口统计数据或采集扫描仪的不同。当在具有如此系统差异的数据库之间执行 FL 时，模型优化不是最优的。这是因为常见的优化方法假设数据是相同且独立分布的 (iid)，但在 FL 设置中并非如此。该项目将开发具有非独立同分布数据的 FL 优化算法，以提高其有效性。b) 当应用于呈现与训练所用特征不同的数据时，现有 DNN 的性能不可靠。我们将研究如何识别用于 FL 的数据库（例如来自不同机构）之间的变化因素并对其进行建模，从而能够推断部署后的预期变化，以提高模型的泛化性。c) 医疗保健领域的标签通常有限，而未标记的数据却很丰富。 FL 方法主要是为使用标签进行学习而设计的。该项目将使用未标记数据开发 FL，使任何机构都能够在协作联盟中提供其未标记数据，从而使模型能够更好地捕获世界各地的真实数据异质性。这项研究是及时的，将推动医学图像分析和 ML 领域的发展。 FL 在医疗保健中的价值之前已得到证明，引起了人们的极大兴趣，但技术挑战限制了其使用。从非独立同分布和未标记数据中学习是机器学习领域长期存在的挑战，尚未解决。因此，该项目的结果对于医学图像分析很有价值，但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保健技术主题。其最终目标是创建有效的 FL 工具，使医学成像界能够开展协作研究并改善疾病诊断和治疗。这项研究是在牛津大学生物医学工程研究所与大数据研究所合作进行的。它得到了与伦敦帝国理工学院和剑桥大学现有合作的促进，并将寻求在英国和国际上建立新的合作。