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 in the medical domain. Typically%2C CIL suffers from catastrophic forgetting when trained on new classes. This phenomenon is mainly caused by the imbalance between old and new classes%2C and it becomes even more challenging with imbalanced medical datasets. In this work%2C we introduce two simple yet effective plug-in methods to mitigate the adverse effects of the imbalance. First%2C we propose a CIL-balanced classification loss to mitigate the classifier bias toward majority classes via logit adjustment. Second%2C 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%2C HAM10000%2C and EyePACS). The results demonstrate that our approach outperforms state-of-the-art methods.?quality=80&w=800)
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