MorphoFormer : Dual‐Branch Dilated Transformer With Pathological Prior Fusion for Cervical Cell Morphology Analysis

ABSTRACT Cervical cancer is one of the most common malignant tumors among women worldwide, and accurate early diagnosis is critical for improving patient survival rates. Traditional cytological screening methods rely on manual microscopic examination, which suffers from low efficiency and high subjectivity. In recent years, deep learning has facilitated the automation of cervical cell image analysis, yet challenges such as insufficient modeling of pathological features and high computational cost remain. To address these issues, this study proposes a novel dual‐branch multi‐scale model, MorphoFormer. The model employs a multi‐scale dilated Transformer (DilateFormer) as its backbone and innovatively incorporates specialized modules for each branch: A Local Context Aggregation (LCA) module in the local branch and a Global Focus Attention (GFA) module in the global branch. These modules respectively enhance the representation of local details and global semantics, and their features are fused to enable collaborative multi‐scale information modeling. Experimental results on the publicly available SIPaKMeD dataset demonstrate that MorphoFormer achieves classification accuracies of 99.58%, 98.51%, and 98.14% for binary, three‐class, and five‐class tasks, respectively. Further validation on the Blood Cell Count and Detection (BCCD) dataset indicates strong cross‐task robustness. Moreover, MorphoFormer requires only 8.22 GFLOPs for inference, highlighting its practical potential by achieving high performance with low computational overhead. Related codes: https://github.com/sijhb/MorphoFormer .