Contrastive Representation Learning for Cross-Domain Blood Cell Image Classification With Denoising Mechanism

Accurate identification and classification of white blood cells are essential for diagnosing hematological malignancies and analyzing blood disorders. Existing approaches predominantly leverage masked autoencoders (MAEs) to extract intrinsic blood cell features through image reconstruction as a pretext task. However, these methods encounter two critical challenges: (1) their generalization performance deteriorates under domain shifts caused by variations in staining techniques, illumination conditions, and microscope settings, and (2) the learned data distribution often deviates from the true distribution of blood cell features. To overcome these limitations, we propose CD-CBC, a novel framework for cross-domain blood cell image classification that integrates contrastive representation learning with a denoising mechanism. CD-CBC consists of two key components: a LoRA-based segmentation anything model (LoRA-SAM) and a contrastive masked autoencoder (CMAE). LoRA-SAM mitigates shortcut learning in contrastive learning by eliminating background noise and platelet interference, while CMAE captures fine-grained semantic features and models spatial relationships, enhancing cross-domain robustness. Additionally, we introduce a denoising mechanism in the latent space, which guides the model to focus on unmasked patches during reconstruction, allowing it to better capture the true distribution of blood cell features. Extensive experiments on two benchmark blood cell datasets demonstrate that CD-CBC achieves superior cross-domain performance, reaching an average accuracy of 62.47%, which is 3.17% higher than the current state-of-the-art, thereby confirming its strong generalization capability.