DCDSN: dual-color domain siamese network for multi-classification of pathological artifacts

Pathological images are prone to artifacts during scanning and preparation, which can compromise diagnostic accuracy. Therefore, robust artifact detection is essential for improving image quality and ensuring reliable pathological assessments. However, existing methods often struggle with the wide variability of artifact types, leading to high computational cost and poor feature representation. Furthermore, most methods rely on a single-color domain, resulting in weak color perception in Hematoxylin-Eosin (H&E) stained images and reduced ability to distinguish artifacts from normal tissue. To address these issues, we propose a Dual-Color-Domain Siamese Network (DCDSN) that leverages RGB and HSV color domains. By minimizing representation discrepancies between two color domains via Siamese network similarity learning, our method achieves better feature alignment and enhances pathological feature representation. Additionally, to improve efficiency, we integrate a lightweight MobileViT-XS backbone with transfer learning, significantly reducing computational cost. And we introduce a Dynamic Snake Convolution-based feature mapper to enhance the network's sensitivity to subtle artifact features and reduce misclassification between artifacts and diseased tissues. Experiment results show that DCDSN achieves a 90.97% accuracy, outperforming the AR-Classifier baseline. Notably, it reduces parameter counts and computation by 74.39% and 96.55% respectively, demonstrating strong performance with lower resource consumption in artifact detection tasks.