Privacy-Aware Federated Learning for Breast Cancer Risk Assessment

摘要： 联邦学习（FL）最近获得了很多关注，因为它可以分析来自众多数据的数据。 协作站点，而不需要共享数据，即，每个合作者的数据始终保留在其 绝佳的价钱FL是有利的，因为它可以：1）克服文化/所有权，隐私和监管问题（因为数据 永远不要离开本地网站），2）提供对受限数据的访问，3）允许收集有意义的数量， 用于分析罕见疾病的数据，以及4）解决健康差距和不公平问题。因此，FL可以被记为 多站点协作的新模式，能够访问大量且重要的多样化数据， 开发可在看不见的数据中推广的强大模型。为此，我们开发了联邦肿瘤 分割（FeTS）平台和开放联合学习（OpenFL）库，作为开源工具， 商业上友好的许可证，促进了a）迄今为止最大的现实世界的联邦，涉及3D大脑 来自6大洲71个地点的肿瘤MRI数据，以及B）FL中的第一个计算挑战，形成 该领域的第一个基准测试环境和数据集。该FeTS-OpenFL基础设施已进一步 用于c）识别组织病理学图像中的肿瘤浸润淋巴细胞和d）在2D中分割致密组织 数字乳腺X线摄影（DM），突出了其在不同成像和疾病类型中的普遍性。基础上 我们成功的FeTS-OpenFL基础设施，我们建议通过新的开发来增强其功能， 隐私感知FL用于分类工作负载，并在乳腺癌的首个此类用例中对其进行评估 (BC)风险评估BC是美国诊断最多的癌症，是癌症死亡的第二大原因 对于40 - 50岁的女性，常规使用2D数字乳腺X射线摄影（DM）进行筛查。 然而，DM产生了很多假阳性和不必要的后续过程。为了缓解这些问题， 3D数字乳腺断层合成摄影（DBT）已经开发出来，并逐渐取代DM。我们集团 开发了新的体积乳腺密度（VBD）的措施，从DBT扫描。以我们团队的集体为基础 在FL和BC风险评估方面的开创性工作，在本提案中，我们专注于开发一个值得信赖的零代码 原则FL框架，用于训练基于AI的分类模型和内置功能，以i）生成逼真的 合成数据，匹配当地人口特征，用于数据扩充和隐私保护，以及ii） 自动确定最佳隐私保护的定量和可解释的设置。我们将使用这个 对用于BC风险评估的训练深度学习模型进行迄今为止最大规模评估的框架 使用DBT VBD指标和其他已确定的风险因素，同时利用多站点、种族多样性数据 接受BC筛查的女性。我们还将通过分发源代码来传播资源， 部署到协作地点，并组织培训活动。我们的首要目标是一个易于使用的 可翻译的值得信赖的FL框架，降低了服务不足人群参与大型 规模FL研究，并为解决健康差距铺平道路，加速发现医疗保健。

ABSTRACT: Federated learning (FL) has gained a lot of attention recently, as it enables analyses of data from numerous collaborating sites without the need to share data, i.e., each collaborator’s data are always retained within their site. FL is advantageous as it can: 1) overcome cultural/ownership, privacy, and regulatory concerns (since data never leave the local site), 2) provide access to restricted data, 3) allow the collection of meaningful amounts of data for analyses of rare diseases, and 4) address health disparities and inequities. Thus, FL can be noted as a novel paradigm for multi-site collaborations, enabling access to ample and importantly diverse data, essential to developing robust models generalizable in unseen data. To this end, we have developed the Federated Tumor Segmentation (FeTS) platform and the Open Federated Learning (OpenFL) library, as open-source tools with a commercially friendly license that have facilitated a) the largest to-date real-world federation, involving 3D brain tumor MRI data from 71 sites across 6 continents, and b) the very first computational challenge in FL, forming the first benchmarking environment and dataset in the field. This FeTS-OpenFL infrastructure has further been used to c) identify tumor-infiltrating lymphocytes in histopathology images and d) segment dense tissue in 2D digital mammography (DM), highlighting its generalizability in different imaging and disease types. Building upon our successful FeTS-OpenFL infrastructure, we propose to enhance its functionality with new developments on privacy-aware FL towards classification workloads and evaluate it on a first-of-its-kind use case on breast cancer (BC) risk assessment. BC is the most diagnosed cancer in the US, the 2nd leading cause of death from cancer in women, and screening is performed routinely with 2D digital mammography (DM) for women in their 40s-50s. However, DM yields a lot of false positives and unnecessary subsequent procedures. To alleviate these issues, 3D Digital Breast Tomosynthesis (DBT) has been developed and increasingly replacing DM. Our group has developed novel volumetric breast density (VBD) measures from DBT scans. Building upon our team’s collective pioneering work in FL and BC risk assessment, in this proposal we focus on developing a trustworthy, zero-code principle FL framework for training AI-based classification models and built-in functionality to i) generate realistic synthetic data, matching local population characteristics, for data augmentation & privacy preservation, and ii) automatically determine quantitative & interpretable settings of optimal privacy preservation. We will use this framework to perform the largest to-date evaluation of training deep-learning models for BC risk assessment using DBT VBD measures and other established risk factors while leveraging multi-site, ethnically diverse data of women undergoing BC screening. We will also disseminate resources via distribution of source code, deployment to collaborating sites, and organization of training activities. Our overarching goal is an easy-to-use translatable trustworthy FL framework, lowering the barrier for under-served populations to participate in large- scale FL studies, and paving the way to address health disparities, towards accelerating discovery healthcare.