Domain-incremental white blood cell classification with privacy-aware continual learning.

ABSTRACT: 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

SUBMITTER: Kumari P 

PROVIDER: S-EPMC12264020 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature