  Deep convolutional neural networks have made significant breakthroughs in
medical image classification, under the assumption that training samples from
all classes are simultaneously available. However, in real-world medical
scenarios, there's a common need to continuously learn about new diseases,
leading to the emerging field of class incremental learning (CIL) in the
medical domain. Typically, CIL suffers from catastrophic forgetting when
trained on new classes. This phenomenon is mainly caused by the imbalance
between old and new classes, and it becomes even more challenging with
imbalanced medical datasets. In this work, we introduce two simple yet
effective plug-in methods to mitigate the adverse effects of the imbalance.
First, we propose a CIL-balanced classification loss to mitigate the classifier
bias toward majority classes via logit adjustment. Second, we propose a
distribution margin loss that not only alleviates the inter-class overlap in
embedding space but also enforces the intra-class compactness. We evaluate the
effectiveness of our method with extensive experiments on three benchmark
datasets (CCH5000, HAM10000, and EyePACS). The results demonstrate that our
approach outperforms state-of-the-art methods.
