Due to data privacy constraints, data sharing among multiple clinical centers
is restricted, which impedes the development of high performance deep learning
models from multicenter collaboration. Naive weight transfer methods share
intermediate model weights without raw data and hence can bypass data privacy
restrictions. However, performance drops are typically observed when the model
is transferred from one center to the next because of the forgetting problem.
Incremental transfer learning, which combines peer-to-peer federated learning
and domain incremental learning, can overcome the data privacy issue and
meanwhile preserve model performance by using continual learning techniques. In
this work, a conventional domain/task incremental learning framework is adapted
for incremental transfer learning. A comprehensive survey on the efficacy of
different regularization-based continual learning methods for multicenter
collaboration is performed. The influences of data heterogeneity, classifier
head setting, network optimizer, model initialization, center order, and weight
transfer type have been investigated thoroughly. Our framework is publicly
accessible to the research community for further development.

由于数据隐私限制，多个临床中心之间的数据共享受限制，这阻碍了通过多中心合作开发高性能深度学习模型的进展。本文通过使用持续学习技术，将点对点联邦学习和领域增量学习相结合，进行增量迁移学习，以克服数据隐私问题并同时保持模型性能。进行了一项针对多中心合作的不同基于正则化的持续学习方法效果的全面调查，对数据异质性、分类器设置、网络优化器、模型初始化、中心顺序和权重传递类型的影响进行了彻底研究。我们的框架对研究社区是公开可访问的，以进行进一步的开发。

增量迁移学习综述：将点对点联邦学习与领域增量学习结合以实现多中心协作

A Survey of Incremental Transfer Learning: Combining Peer-to-Peer  Federated Learning and Domain Incremental Learning for Multicenter  Collaboration

Due to data privacy constraints, data sharing among multiple centers is
restricted. Continual learning, as one approach to peer-to-peer federated
learning, can promote multicenter collaboration on deep learning algorithm
development by sharing intermediate models instead of training data. This work
aims to investigate the feasibility of continual learning for multicenter
collaboration on an exemplary application of brain metastasis identification
using DeepMedic. 920 T1 MRI contrast enhanced volumes are split to simulate
multicenter collaboration scenarios. A continual learning algorithm, synaptic
intelligence (SI), is applied to preserve important model weights for training
one center after another. In a bilateral collaboration scenario, continual
learning with SI achieves a sensitivity of 0.917, and naive continual learning
without SI achieves a sensitivity of 0.906, while two models trained on
internal data solely without continual learning achieve sensitivity of 0.853
and 0.831 only. In a seven-center multilateral collaboration scenario, the
models trained on internal datasets (100 volumes each center) without continual
learning obtain a mean sensitivity value of 0.699. With single-visit continual
learning (i.e., the shared model visits each center only once during training),
the sensitivity is improved to 0.788 and 0.849 without SI and with SI,
respectively. With iterative continual learning (i.e., the shared model
revisits each center multiple times during training), the sensitivity is
further improved to 0.914, which is identical to the sensitivity using mixed
data for training. Our experiments demonstrate that continual learning can
improve brain metastasis identification performance for centers with limited
data. This study demonstrates the feasibility of applying continual learning
for peer-to-peer federated learning in multicenter collaboration.

本研究证明了持续学习可以提高数据有限的中心的脑转移识别性能，可以实现多中心协作的点对点联邦学习的可行性，共享中间模型而不是训练数据可以促进深度学习算法的多中心协作。