In real-world clinical settings, data distributions evolve over time, with a continuous influx of new, limited disease cases. Therefore, class incremental learning is of great significance, i.e., deep learning models are required to learn new class knowledge while maintaining accurate recognition of previous diseases. However, traditional deep neural networks often suffer from severe forgetting of prior knowledge when adapting to new data unless trained from scratch, which undesirably costs much time and computational burden. Additionally, the sample sizes for different diseases can be highly imbalanced, with newly emerging diseases typically having much fewer instances, consequently causing the classification bias. To tackle these challenges, we are the first to propose a class-incremental learning method under limited samples in the biomedical field. First, we propose a novel cumulative entropy prediction module to measure the uncertainty of the samples, of which the most uncertain samples are stored in a memory bank as exemplars for the model's later review. Furthermore, we theoretically demonstrate its effectiveness in measuring uncertainty. Second, we developed a fine-grained semantic expansion module through various augmentations, leading to more compact distributions within the feature space and creating sufficient room for generalization to new classes. Besides, a cosine classifier is utilized to mitigate classification bias caused by imbalanced datasets. Across four imbalanced data distributions over two datasets, our method achieves optimal performance, surpassing state-of-the-art methods by as much as 53.54% in accuracy.

本研究针对临床环境中数据分布不断演变及有限样本问题，提出了一种创新的增量学习方法，旨在在新的疾病类别到来时保持对旧疾病的准确识别。其核心亮点是提出了累积熵预测模块和细粒度语义扩展模块，从而有效测量不确定性并优化特征空间分布，使模型在处理不同类别时具备更强的泛化能力。实验结果表明，该方法在四个不平衡数据分布中的准确性比现有技术提高了高达53.54%。

从不确定性到明确性：基于不确定性的有限生物医学样本增量学习