  The ability to incrementally learn new classes from limited samples is
crucial to the development of artificial intelligence systems for real clinical
application. Although existing incremental learning techniques have attempted
to address this issue, they still struggle with only few labeled data,
particularly when the samples are from varied domains. In this paper, we
explore the cross-domain few-shot incremental learning (CDFSCIL) problem.
CDFSCIL requires models to learn new classes from very few labeled samples
incrementally, and the new classes may be vastly different from the target
space. To counteract this difficulty, we propose a cross-domain enhancement
constraint and cross-domain data augmentation method. Experiments on MedMNIST
show that the classification performance of this method is better than other
similar incremental learning methods.
