Few-shot Class-incremental Learning for Cross-domain Disease Classification
This addresses the challenge of learning new disease classes from limited and varied data in clinical AI systems, but it is incremental as it builds on existing techniques.
The paper tackles the problem of cross-domain few-shot incremental learning for disease classification, proposing a cross-domain enhancement constraint and data augmentation method that outperforms existing incremental learning methods on MedMNIST.
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.