Structure-preserving lightweight skin lesion segmentation with dual-resolution attention and boundary-aware fusion
Year: 2026
Type: article
Abstract: Objective Dermoscopic skin lesion segmentation suffers from low contrast, artifacts, and blurred boundaries in lightweight models due to lossy downsampling and rigid tokenization. We propose Structure-Preserving Representation Learning (SP-RL), a lightweight framework designed to preserve geometric topology and continuity for stronger representation learning. Methods SP-RL integrates four components: 1) Structure-Preserving Sampling Strategy (SPSS) using a bijective pixel-folding mechanism to redistribute spatial details into channel dimensions; 2) Lite Multi-Scale Aggregation Block (LMA-Block) to expand receptive fields at high resolutions; 3) Dual-Resolution Attention (DR-Atn) maintaining high-resolution queries while compressing keys/values via hierarchical pooling; and 4) Dual-Gated Multi-Scale Fusion (DG-MSF) to suppress low-level artifacts and inject noise-resilient Sobel gradient priors into progressive deconvolution paths. Results Evaluations across 23 models show SP-RL achieves a 0.8976 Dice score and the lowest HD95 (15.1829) on ISIC2018 among lightweight alternatives. It exhibits superior robustness across four public datasets (ISIC 2016/2017/2018, PH 2 ) and unseen domains like HAM10000 under severe noise and focus blur. Conclusion SP-RL offers an optimal trade-off between boundary accuracy, reliability, and efficiency. By retaining high-frequency energy and domain-invariant morphology, it provides a dependable solution for resource-constrained diagnosis.
Source: Digital Health
Authors: Xu Yang, Duwei Dai, Caixia Dong, Liang Wang, Pengyu Ren
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,000
OpenAlex ID: W7224116282