DualResolution Residual Architecture with Artifact Suppression for Melanocytic Lesion Segmentation

TL;DR

This paper introduces a dual-resolution residual architecture with artifact suppression and boundary-aware features for precise melanocytic lesion segmentation in dermoscopic images, improving accuracy over traditional methods.

Contribution

A novel dual-resolution network with boundary-aware residuals and artifact suppression tailored for melanoma segmentation, addressing artifacts and small datasets effectively.

Findings

Significantly improved boundary precision and segmentation metrics.

Outperforms traditional encoder-decoder baselines on public benchmarks.

Effective artifact suppression enhances robustness in clinical images.

Abstract

Lesion segmentation, in contrast to natural scene segmentation, requires handling subtle variations in texture and color, frequent imaging artifacts (such as hairs, rulers, and bubbles), and a critical need for precise boundary localization to aid in accurate diagnosis. The accurate delineation of melanocytic tumors in dermoscopic images is a crucial component of automated skin cancer screening systems and clinical decision support. In this paper, we present a novel dual-resolution architecture inspired by ResNet, specifically tailored for the segmentation of melanocytic tumors. Our approach incorporates a high-resolution stream that preserves fine boundary details, alongside a complementary pooled stream that captures multi-scale contextual information for robust lesion recognition. These two streams are closely integrated through boundary-aware residual connections, which inject edge information into deep feature maps, and a channel attention mechanism that adapts the model's sensitivity to color and texture variations in dermoscopic images. To tackle common imaging artifacts and the challenges posed by small clinical datasets, we introduce a lightweight artifact suppression block and a multi-task training strategy. This strategy combines the Dice-Tversky loss with an explicit boundary loss and a contrastive regularizer to enhance feature stability. This unified design enables the model to generate pixel-accurate segmentation masks without the need for extensive post-processing or complex pre-training. Extensive evaluation on public dermoscopic benchmarks reveals that our method significantly enhances boundary precision and clinically relevant segmentation metrics, outperforming traditional encoder-decoder baselines. This makes our approach a valuable component for building automated melanoma assessment systems.