Distributed Learning of Deep Learning Models for Cancer Research

项目摘要 深度学习方法在推进癌症研究方面显示出巨大的前景，并有可能 改善癌症的临床决策，如原发性脑胶质瘤，深度学习模型在这些肿瘤中具有 最近在预测异柠檬酸脱氢酶(IDH)突变和存活方面取得了令人振奋的结果 病人。然而，阻碍这项研究的一个主要挑战是对大量标记的 图像数据用于训练深度学习模型。创建大型公共集中图像数据集合的努力 受到数据共享障碍、图像识别成本、患者隐私问题和控制的阻碍 数据是如何使用的。当前正在使用一个或几个数据构建的深度学习模型 机构受到潜在的过度适应和较差的通用性的限制。而不是集中或共享患者 图像，我们的目标是通过计算在不同机构之间分发深度学习模型的培训 在他们的本地图像数据上执行。虽然我们的初步结果证明了这一点的可行性 将这些方法转化为研究实践有三个关键挑战：(1)数据是 机构之间在数据的数量和质量上存在差异，这可能会损害分布式 计算，(2)存在数据安全和隐私问题，以及(3)没有软件包 利用医学图像实现分布式深度学习。我们通过(1)优化和 扩展我们当前的分布式深度学习方法，以应对数据可变性和数据的挑战 隐私/安全，(2)创建免费可用的软件系统，用于在多个 使用分布式计算的机构数据，以及(3)评估我们的系统以解决深度学习问题 以应用案例为例，对原发性脑癌进行分类和临床预测。我们的方法是 在开发分布式深度学习方法方面具有创新性，该方法将解决不同数据之间的差异 机构，在分布式计算期间保护患者隐私，并使站点能够发现 相关数据集，并参与创建深度学习模型。我们的工作将是重要的和有影响的 通过克服研究人员在利用多机构患者数据创建 基于比数据更能代表疾病的大量图像数据的深度学习模型 从单一机构获取，同时避免机构间共享患者数据的障碍。 最终，我们的方法将使研究人员能够协作开发更具普遍性的深度学习 通过开放访问和利用大量的多机构应用来推进癌症护理 图像数据。虽然我们开发这项技术的临床用例是原发性脑癌，但我们的方法 将推广到所有癌症以及除用于创建深度学习的图像之外的其他类型的数据 模型，并最终将导致强大的深度学习应用程序，预计将改善临床护理 以及多种癌症的转归。

Project Summary Deep learning methods are showing great promise for advancing cancer research and could potentially improve clinical decision making in cancers such as primary brain glioma, where deep learning models have recently shown promising results in predicting isocitrate dehydrogenase (IDH) mutation and survival in these patients. A major challenge thwarting this research, however, is the requirement for large quantities of labeled image data to train deep learning models. Efforts to create large public centralized collections of image data are hindered by barriers to data sharing, costs of image de-identification, patient privacy concerns, and control over how data are used. Current deep learning models that are being built using data from one or a few institutions are limited by potential overfitting and poor generalizability. Instead of centralizing or sharing patient images, we aim to distribute the training of deep learning models across institutions with computations performed on their local image data. Although our preliminary results demonstrate the feasibility of this approach, there are three key challenges to translating these methods into research practice: (1) data is heterogeneous among institutions in the amount and quality of data that could impair the distributed computations, (2) there are data security and privacy concerns, and (3) there are no software packages that implement distributed deep learning with medical images. We tackle these challenges by (1) optimizing and expanding our current methods of distributed deep learning to tackle challenges of data variability and data privacy/security, (2) creating a freely available software system for building deep learning models on multi- institutional data using distributed computation, and (3) evaluating our system to tackle deep learning problems in example use cases of classification and clinical prediction in primary brain cancer. Our approach is innovative in developing distributed deep learning methods that will address variations in data among different institutions, that protect patient privacy during distributed computations, and that enable sites to discover pertinent datasets and participate in creating deep learning models. Our work will be significant and impactful by overcoming critical hurdles that researchers face in tapping into multi-institutional patient data to create deep learning models on large collections of image data that are more representative of disease than data acquired from a single institution, while avoiding the hurdles to inter-institutional sharing of patient data. Ultimately, our methods will enable researchers to collaboratively develop more generalizable deep learning applications to advance cancer care by unlocking access to and leveraging huge amounts of multi-institutional image data. Although our clinical use case in developing this technology is primary brain cancer, our methods will generalize to all cancers, as well as to other types of data besides images for use in creating deep learning models, and will ultimately lead to robust deep learning applications that are expected to improve clinical care and outcomes in many types of cancer.