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。