论文标题
整个医院多发性硬化病变的细分：不断学习还是从头开始训练？
Segmentation of Multiple Sclerosis Lesions across Hospitals: Learn Continually or Train from Scratch?
论文作者
论文摘要
多发性硬化症（MS）病变的分割是一个具有挑战性的问题。近年来已经提出了几种基于深度学习的方法。但是，大多数方法往往是静态的，也就是说，在大型专门数据集中训练的单个模型，该模型不能很好地概括。取而代之的是，该模型应以连续的方式基于病变的特征来依次从不同医院学习。在这方面，我们探索了经验重播，这是一种众所周知的持续学习方法，在MS病变分割的背景下，来自8家不同医院的多对比数据。我们的实验表明，与顺序微调相比，重播能够实现正向向后转移并减少灾难性遗忘。此外，重播优于多域训练，从而成为MS病变分割的有前途的解决方案。该代码可在此链接上找到：https：//github.com/naga-karthik/continual-learning-ms
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
