arXiv 2410.024432024-10-03cs.CVcs.AIcs.LG

Clinnova 联邦学习概念验证：跨境合作的关键经验

Clinnova Federated Learning Proof of Concept: Key Takeaways from a Cross-border Collaboration

IHU Strasbourg（斯特拉斯堡大学医院研究所）；University of Strasbourg（斯特拉斯堡大学）；University Hospital Basel（巴塞尔大学医院）；Medical Center – University of Freiburg（弗莱堡大学医学中心）；Faculty of Medicine, University of Freiburg（弗莱堡大学医学院）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University（海德堡大学）；Bosch Digital Innovation Hub I Koordinierungsstelle Telemedizin Baden-Württemberg (BDIH I KTBW)（博世数字创新中心 I 巴登-符腾堡远程医疗协调办公室）；Bosch Health Campus (BHC)（博世健康园区）

Julia Alekseenko, Bram Stieltjes, Michael Bach, Melanie Boerries, Oliver Opitz, Alexandros Karargyris, Nicolas Padoy

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AI总结：

Clinnova 计划通过法德瑞卢跨境联邦学习概念验证，在 MRI 图像多发性硬化症分割上验证了数据联邦化与 AI 协作的可行性，并总结了技术、后勤与伦理方面的关键经验。

摘要：

Clinnova 是一项涉及法国、德国、瑞士和卢森堡的合作计划，致力于通过数据联邦化、标准化和互操作性释放精准医疗的潜力。这项欧洲大区计划旨在利用人工智能（AI）和数据科学创建可互操作的欧洲标准，以提升医疗保健效果和效率。关键组成部分包括多学科研究中心、联邦生物样本库策略、数字健康创新平台以及联邦 AI 策略。该计划针对炎症性肠病、风湿性疾病和多发性硬化症（MS），强调数据质量，以开发用于个性化治疗和转化研究的 AI 算法。\nIHU Strasbourg（微创外科研究所）在该计划中牵头开发联邦学习（FL）概念验证（POC），该验证将作为推进医疗保健领域 AI 的基础。其核心目标 Clinnova-MS 旨在通过使用 FL 开发更准确的模型来改善 MS 患者护理，这些模型能够检测疾病进展、指导干预措施，并在多个站点验证数字生物标志物。本技术报告介绍了 Clinnova 框架内首个关于 MRI 图像 MS 分割的跨境联邦 POC 的见解和关键经验。虽然我们的工作标志着通过跨境合作推进 MS 分割的重要里程碑，但它也强调了解决技术、后勤和伦理方面问题的重要性，以充分发挥 FL 在医疗保健环境中的潜力。

英文摘要：

Clinnova, a collaborative initiative involving France, Germany, Switzerland, and Luxembourg, is dedicated to unlocking the power of precision medicine through data federation, standardization, and interoperability. This European Greater Region initiative seeks to create an interoperable European standard using artificial intelligence (AI) and data science to enhance healthcare outcomes and efficiency. Key components include multidisciplinary research centers, a federated biobanking strategy, a digital health innovation platform, and a federated AI strategy. It targets inflammatory bowel disease, rheumatoid diseases, and multiple sclerosis (MS), emphasizing data quality to develop AI algorithms for personalized treatment and translational research. The IHU Strasbourg (Institute of Minimal-invasive Surgery) has the lead in this initiative to develop the federated learning (FL) proof of concept (POC) that will serve as a foundation for advancing AI in healthcare. At its core, Clinnova-MS aims to enhance MS patient care by using FL to develop more accurate models that detect disease progression, guide interventions, and validate digital biomarkers across multiple sites. This technical report presents insights and key takeaways from the first cross-border federated POC on MS segmentation of MRI images within the Clinnova framework. While our work marks a significant milestone in advancing MS segmentation through cross-border collaboration, it also underscores the importance of addressing technical, logistical, and ethical considerations to realize the full potential of FL in healthcare settings.

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