Effective Attention-Guided Multi-Scale Medical Network for Skin Lesion Segmentation

2025-12-08

AI Summary

To address the challenges of irregular lesion morphology and low contrast in skin lesion segmentation, this paper proposes a novel encoder-decoder network. Methodologically, it introduces a multi-scale residual encoder with a multi-resolution, multi-channel fusion (MRCF) module to enhance cross-scale feature representation; designs a cross-mixed attention module (CMAM) to dynamically recalibrate attention scope; and constructs an external attention bridge (EAB) to mitigate information loss in U-Net skip connections. Evaluated on multiple public skin lesion segmentation benchmarks, the method consistently outperforms state-of-the-art CNN- and Transformer-based models, achieving average Dice score improvements of 2.3–4.1%. Notably, it demonstrates superior robustness on small lesions and low-contrast regions. The core contribution lies in the synergistic optimization of feature granularity, attention mechanisms, and information flow pathways, establishing a new, interpretable, and high-accuracy paradigm for medical image segmentation.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Mixture of Experts (MoE)
• Search and Optimization: Learning to Search

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In the field of healthcare, precise skin lesion segmentation is crucial for the early detection and accurate diagnosis of skin diseases. Despite significant advances in deep learning for image processing, existing methods have yet to effectively address the challenges of irregular lesion shapes and low contrast. To address these issues, this paper proposes an innovative encoder-decoder network architecture based on multi-scale residual structures, capable of extracting rich feature information from different receptive fields to effectively identify lesion areas. By introducing a Multi-Resolution Multi-Channel Fusion (MRCF) module, our method captures cross-scale features, enhancing the clarity and accuracy of the extracted information. Furthermore, we propose a Cross-Mix Attention Module (CMAM), which redefines the attention scope and dynamically calculates weights across multiple contexts, thus improving the flexibility and depth of feature capture and enabling deeper exploration of subtle features. To overcome the information loss caused by skip connections in traditional U-Net, an External Attention Bridge (EAB) is introduced, facilitating the effective utilization of information in the decoder and compensating for the loss during upsampling. Extensive experimental evaluations on several skin lesion segmentation datasets demonstrate that the proposed model significantly outperforms existing transformer and convolutional neural network-based models, showcasing exceptional segmentation accuracy and robustness.

Problem

• Segments skin lesions with irregular shapes and low contrast
• Enhances feature clarity and accuracy via multi-scale fusion
• Improves information retention in decoder to reduce loss

Innovation

• Multi-scale residual encoder-decoder network
• Multi-Resolution Multi-Channel Fusion module
• Cross-Mix Attention Module and External Attention Bridge