Class-incremental continual learning is a core step towards developing artificial intelligence systems that can continuously adapt to changes in the environment by learning new concepts without forgetting those previously learned. This is especially needed in the medical domain where continually learning from new incoming data is required to classify an expanded set of diseases. In this work, we focus on how old knowledge can be leveraged to learn new classes without catastrophic forgetting. We propose a framework that comprises of two main components: (1) a dynamic architecture with expanding representations to preserve previously learned features and accommodate new features; and (2) a training procedure alternating between two objectives to balance the learning of new features while maintaining the model's performance on old classes. Experiment results on multiple medical datasets show that our solution is able to achieve superior performance over state-of-the-art baselines in terms of class accuracy and forgetting.

翻译：类增量持续学习是开发人工智能系统的一个核心步骤，该系统能够通过学习新概念而不遗忘先前学到的知识，持续适应环境变化。这在医疗领域尤为必要，因为需要持续从新输入数据中学习以分类更多种类的疾病。本文重点研究如何利用旧知识学习新类别而避免灾难性遗忘。我们提出一个包含两个主要组件的框架：（1）一种具有扩展表示的动态架构，用于保留先前学习的特征并适应新特征；（2）一种交替两个目标的训练过程，以平衡新特征的学习同时保持模型在旧类别上的性能。在多个医学数据集上的实验结果表明，我们的方案在类别准确率和遗忘程度方面均优于现有最先进基准方法。