# LTPNet: Lesion-Aware Triple-Path Feature Fusion Network for Skin Lesion Segmentation

## TL;DR

This paper introduces LTPNet, a new deep learning framework for accurately segmenting skin lesions in images.

## Contribution

The novel LTPNet framework uses a triple-path feature fusion approach with lesion-aware attention modules for improved segmentation.

## Key findings

- LTPNet achieves superior segmentation accuracy on skin lesion datasets.
- The framework demonstrates reasonable inference efficiency and model complexity.
- Experiments show effectiveness in both in-domain and cross-domain settings.

## Abstract

Skin lesion segmentation has achieved notable progress in recent years; however, accurate delineation remains challenging due to complex backgrounds, ambiguous boundaries, and low lesion-to-skin contrast. To address these issues, we propose the lesion-aware triple-path feature fusion network (LTPNet), an end-to-end framework that progressively processes features through extraction, refinement, and aggregation stages. In the extraction stage, we incorporate a general foreground–background attention to suppress background interference and accelerate model convergence. In the refinement stage, we introduce an attentive spatial modulator (ASM) to jointly exploit local structural cues and global semantic context for precise spatial modulation. We further develop a lesion-aware lite-gate attention (LALGA) module that performs local spatial feature modulation and global channel recalibration tailored to lesion characteristics. In the aggregation stage, we propose a triple-path feature fusion (TPFF) module that explicitly models feature relationships across scales via three complementary pathways: a common path (CP) for semantic consistency, a saliency path (SP) for highlighting co-activated regions, and a difference path (DP) for accentuating structural discrepancies. Extensive experiments on in-domain and cross-domain datasets show that LTPNet achieves superior segmentation accuracy with reasonable inference efficiency and model complexity, demonstrating its potential for efficient and reliable clinical decision support.