  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.
