Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:2427-2432. doi: 10.1109/EMBC46164.2021.9630382.
Data-driven deep learning has been considered a promising method for building powerful models for medical data, which often requires a large amount of diverse data to be sufficiently effective. However, the expensive cost of collecting and the privacy constraints lead to the fact that existing medical datasets are small-scale and distributed. Federated learning via model distillation is a data-private collaborative learning where the model can leverage all available data without direct sharing. The data knowledge is shared by distillation through the multi-site average prediction scores on the public dataset. However, the average consensus is suboptimal to individual client due to data domain shift in MRI data caused by acquisition protocols, recruitment criteria, etc. In this work, we propose a federated conditional mutual learning (FedCM) to improve the performance by considering the clients' local performance and the similarity between clients. This work is the first federated learning on multi-dataset Alzheimer's disease classification by 3DCNN using T1w MRI. Our method achieves the best recognition rates comparing with FedMD and other frameworks. Further visualization and relevance ranking on the region of interests (ROI) in human brains implies that the left hemisphere may have greater relevance than the right hemisphere does. Several potential regions are listed for future investigation.
数据驱动的深度学习被认为是构建医学数据强大模型的一种很有前途的方法，而这通常需要大量不同的数据才能充分发挥其效果。然而，由于收集成本高昂以及隐私限制，现有的医学数据集规模较小且分散。通过模型蒸馏的联邦学习是一种数据隐私协作学习，模型可以在不直接共享数据的情况下利用所有可用的数据。通过在公共数据集上对多站点的平均预测分数进行蒸馏，可以共享数据知识。然而，由于 MRI 数据中采集协议、招募标准等导致的数据域偏移，平均共识对个体客户端来说并不是最优的。在这项工作中，我们提出了联邦条件互学习（FedCM），通过考虑客户端的本地性能和客户端之间的相似性来提高性能。这是首次使用 3DCNN 通过 T1w MRI 对多数据集阿尔茨海默病分类进行联邦学习。与 FedMD 和其他框架相比，我们的方法实现了最佳的识别率。对人脑感兴趣区域（ROI）的进一步可视化和相关性排序表明，左半球的相关性可能大于右半球。列出了几个潜在的区域，供未来研究。
