SegFormer with Hausdorff distance-based spatial bias and conceptual split gate for automated segmentation of cervical...

Automated segmentation of acetowhite lesions in Visual Inspection with Acetic Acid (VIA) cervicography is critical for colposcopy-guided biopsy targeting in low-resource cervical cancer screening programs. Standard transformer architectures compute attention solely from feature content, neglecting spatial proximity between tokens, while unified decoder designs conflate semantic and boundary channels-leaving boundary accuracy at the squamocolumnar junction unresolved. We propose SegFormer-HF-CSG, a parameter-efficient framework that addresses these limitations through two targeted modifications to the SegFormer backbone (as Illustrated in Figure 1):•Hausdorff Distance-based Spatial Bias (HF): injects pairwise spatial proximity information into the self-attention mechanism across all encoder stages, improving geometric coherence of acetowhite boundary predictions without adding trainable parameters.•Conceptual Split Gate (CSG): decouples the decoder into independent semantic and boundary pathways with adaptive soft gating, adding only 59,712 parameters (+2.3%).Evaluated on 545 VIA cervicography images, SegFormer-HF-CSG achieves DSC of 96.91%, HD95 of 3.46 mm, and Normalized Surface Distance of 98.82%, outperforming eight reference architectures spanning convolutional, hybrid, and transformer families (p < 0.01, Bonferroni-corrected) at a total of 2.62 M parameters-the smallest footprint in the evaluated set.