  Segmentation of Multiple Sclerosis (MS) lesions is a challenging problem.
Several deep-learning-based methods have been proposed in recent years.
However, most methods tend to be static, that is, a single model trained on a
large, specialized dataset, which does not generalize well. Instead, the model
should learn across datasets arriving sequentially from different hospitals by
building upon the characteristics of lesions in a continual manner. In this
regard, we explore experience replay, a well-known continual learning method,
in the context of MS lesion segmentation across multi-contrast data from 8
different hospitals. Our experiments show that replay is able to achieve
positive backward transfer and reduce catastrophic forgetting compared to
sequential fine-tuning. Furthermore, replay outperforms the multi-domain
training, thereby emerging as a promising solution for the segmentation of MS
lesions. The code is available at this link:
https://github.com/naga-karthik/continual-learning-ms
