Accurate classification of white blood cells in peripheral blood is essential for diagnosing hematological diseases. Due to constantly evolving clinical settings, data sources, and disease classifications, it is necessary to update machine learning classification models regularly for practical real-world use. Such models significantly benefit from sequentially learning from incoming data streams without forgetting previously acquired knowledge. However, models can suffer from catastrophic forgetting, causing a drop in performance on previous tasks when fine-tuned on new data. Here, we propose a rehearsal-based continual learning approach for class incremental and domain incremental scenarios in white blood cell classification. To choose representative samples from previous tasks, we employ exemplar set selection based on the model's predictions. This involves selecting the most confident samples and the most challenging samples identified through uncertainty estimation of the model. We thoroughly evaluated our proposed approach on three white blood cell classification datasets that differ in color, resolution, and class composition, including scenarios where new domains or new classes are introduced to the model with every task. We also test a long class incremental experiment with both new domains and new classes. Our results demonstrate that our approach outperforms established baselines in continual learning, including existing iCaRL and EWC methods for classifying white blood cells in cross-domain environments.

翻译：外周血白细胞的准确分类对于血液疾病的诊断至关重要。由于临床环境、数据来源和疾病分类的不断演变，为满足实际应用需求，需要定期更新机器学习分类模型。这类模型若能顺序学习流入的数据流而不遗忘先前获取的知识，将显著受益。然而，当模型基于新数据进行微调时，可能遭遇灾难性遗忘，导致先前任务的性能下降。本文针对白细胞分类中的类别增量与领域增量场景，提出一种基于重演的持续学习方法。为从先前任务中选择代表性样本，我们采用基于模型预测的样例集选择策略，通过模型不确定性估计筛选出最置信样本与最具挑战性样本。我们在三个颜色、分辨率和类别构成各异的白细胞分类数据集上全面评估了所提方法，涵盖每个任务引入新领域或新类别的场景，并测试了同时包含新领域与新类别的长序列类别增量实验。结果表明，在跨域环境中，我们的方法在白细胞分类任务上优于现有iCaRL、EWC等持续学习基线方法。