Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results.
Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results.
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DOI：
10.1016/j.media.2020.101765
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发表时间：
2020-10
影响因子：
10.9
通讯作者：
Duncan JS
中科院分区：
文献类型：
--
作者：
Li X;Gu Y;Dvornek N;Staib LH;Ventola P;Duncan JS
深度学习模型已经在许多不同的任务中显示出其优势，包括神经图像分析。然而，为了有效地训练高质量的深度学习模型，需要聚合大量的患者信息。例如，在组装大型fMRI数据集时，采集和注释的时间和成本使得难以在单个站点采集大量数据。然而，由于需要保护患者数据的隐私，很难从多个机构组装一个中央数据库。联邦学习允许在不集中实体数据的情况下训练群体级模型，方法是将全局模型传输到本地实体，在本地训练模型，然后对全局模型中的梯度或权重进行平均。然而，一些研究表明，私人信息可以从模型梯度或权重中恢复。在这项工作中，我们解决的问题，多站点功能磁共振成像分类的隐私保护策略。为了解决这个问题，我们提出了一种联邦学习方法，其中实现了分散的迭代优化算法，并通过随机化机制改变共享的局部模型权重。考虑到不同地点的fMRI分布的系统差异，我们进一步提出了两个领域的适应方法，在这个联邦学习公式。我们研究了联邦模型优化的各个实际方面，并将联邦学习与其他训练策略进行了比较。总的来说，我们的研究结果表明，利用多站点数据而不共享数据来提高神经图像分析性能并找到可靠的疾病相关生物标志物是有希望的。我们提出的管道可以推广到其他隐私敏感的医疗数据分析问题。我们的代码可在www.example.com上公开获取。
Deep learning models have shown their advantage in many different tasks, including neuroimage analysis. However, to effectively train a high-quality deep learning model, the aggregation of a significant amount of patient information is required. The time and cost for acquisition and annotation in assembling, for example, large fMRI datasets make it difficult to acquire large numbers at a single site. However, due to the need to protect the privacy of patient data, it is hard to assemble a central database from multiple institutions. Federated learning allows for population-level models to be trained without centralizing entities’ data by transmitting the global model to local entities, training the model locally, and then averaging the gradients or weights in the global model. However, some studies suggest that private information can be recovered from the model gradients or weights. In this work, we address the problem of multi-site fMRI classification with a privacy-preserving strategy. To solve the problem, we propose a federated learning approach, where a decentralized iterative optimization algorithm is implemented and shared local model weights are altered by a randomization mechanism. Considering the systemic differences of fMRI distributions from different sites, we further propose two domain adaptation methods in this federated learning formulation. We investigate various practical aspects of federated model optimization and compare federated learning with alternative training strategies. Overall, our results demonstrate that it is promising to utilize multi-site data without data sharing to boost neuroimage analysis performance and find reliable disease-related biomarkers. Our proposed pipeline can be generalized to other privacy-sensitive medical data analysis problems. Our code is publicly available at: https://github.com/xxlya/Fed_ABIDE/.
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影响因子：
11
作者：
Di Martino, A.;Yan, C-G;Li, Q.;Denio, E.;Castellanos, F. X.;Alaerts, K.;Anderson, J. S.;Assaf, M.;Bookheimer, S. Y.;Dapretto, M.;Deen, B.;Delmonte, S.;Dinstein, I.;Ertl-Wagner, B.;Fair, D. A.;Gallagher, L.;Kennedy, D. P.;Keown, C. L.;Keysers, C.;Lainhart, J. E.;Lord, C.;Luna, B.;Menon, V.;Minshew, N. J.;Monk, C. S.;Mueller, S.;Mueller, R. A.;Nebel, M. B.;Nigg, J. T.;O'Hearn, K.;Pelphrey, K. A.;Peltier, S. J.;Rudie, J. D.;Sunaert, S.;Thioux, M.;Tyszka, J. M.;Uddin, L. Q.;Verhoeven, J. S.;Wenderoth, N.;Wiggins, J. L.;Mostofsky, S. H.;Milham, M. P.
通讯作者：
Milham, M. P.
影响因子：
4.2
作者：
Heinsfeld, Anibal Solon;Franco, Alexandre Rosa;Meneguzzi, Felipe
通讯作者：
Meneguzzi, Felipe
词汇声调感知的功能神经解剖学：激活似然估计荟萃分析
影响因子：
4.3
作者：
Liang B;Du Y
通讯作者：
Du Y
影响因子：
12
作者：
Masoudnia, Saeed;Ebrahimpour, Reza
通讯作者：
Ebrahimpour, Reza
影响因子：
7
作者：
Nishizawa, Toshinori;Morita, Ayako;Kondo, Katsunori
通讯作者：
Kondo, Katsunori