域增量白细胞分类中的隐私感知持续学习
Domain-incremental White Blood Cell Classification with Privacy-aware Continual Learning
AI总结：
针对白细胞分类中的域偏移与灾难性遗忘，提出基于生成回放的隐私保护持续学习策略，在多个数据集和骨干模型上验证其有效缓解遗忘并保持性能。
摘要：
白细胞（WBC）分类在血液学中对于诊断各种医学状况起着至关重要的作用。然而，由于样本来源（如血液或骨髓）的差异以及不同医院成像条件的不同所导致的域偏移，它面临着重大挑战。传统深度学习模型在此类动态环境中常常遭受灾难性遗忘，而基础模型虽然通常稳健，但当推理数据分布与训练数据分布不同时，其性能会下降。为应对这些挑战，我们提出了一种基于生成回放的持续学习（CL）策略，旨在防止基础模型在WBC分类中遗忘。我们的方法采用轻量级生成器，通过合成潜在表示模拟过去的数据，以实现隐私保护的回放。为展示有效性，我们进行了大量实验，共使用四个数据集，采用不同的任务顺序，并使用了包括ResNet50、RetCCL、CTransPath和UNI在内的四种骨干模型。实验结果表明，传统的微调方法在先前学习任务上性能下降，并难以应对域偏移。相比之下，我们的持续学习策略有效缓解了灾难性遗忘，保持了模型在不同域间的性能。这项工作为在数据分布频繁变化的真实临床环境中维持可靠的WBC分类提供了一种实用解决方案。
英文摘要：
White blood cell (WBC) classification plays a vital role in hematology for diagnosing various medical conditions. However, it faces significant challenges due to domain shifts caused by variations in sample sources (e.g., blood or bone marrow) and differing imaging conditions across hospitals. Traditional deep learning models often suffer from catastrophic forgetting in such dynamic environments, while foundation models, though generally robust, experience performance degradation when the distribution of inference data differs from that of the training data. To address these challenges, we propose a generative replay-based Continual Learning (CL) strategy designed to prevent forgetting in foundation models for WBC classification. Our method employs lightweight generators to mimic past data with a synthetic latent representation to enable privacy-preserving replay. To showcase the effectiveness, we carry out extensive experiments with a total of four datasets with different task ordering and four backbone models including ResNet50, RetCCL, CTransPath, and UNI. Experimental results demonstrate that conventional fine-tuning methods degrade performance on previously learned tasks and struggle with domain shifts. In contrast, our continual learning strategy effectively mitigates catastrophic forgetting, preserving model performance across varying domains. This work presents a practical solution for maintaining reliable WBC classification in real-world clinical settings, where data distributions frequently evolve.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
