SegMan-based dual-prior network with boundary-augmented hybrid attention for robust skin lesion segmentation
Jiayue Wang, Tianlu Zhang, Ping Li
TL;DR
This paper introduces a new deep learning network for skin lesion segmentation that improves accuracy and efficiency by combining boundary and shape priors.
Contribution
The novel dual-prior network integrates boundary and shape priors with a hybrid attention mechanism for robust skin lesion segmentation.
Findings
The proposed method achieves state-of-the-art IoU/DSC scores on ISIC2018, HAM10000, and PH2 datasets.
It outperforms existing methods by 1.4 percentage points in IoU while reducing HD95 and ASD metrics.
The model is computationally efficient, requiring only 3.79G FLOPs.
Abstract
Skin lesion segmentation is a crucial component of dermoscopic computer-aided diagnosis, yet challenges such as boundary ambiguity, morphological diversity, and noise interference under complex imaging conditions still limit the accuracy and robustness of existing methods. To address these issues, we propose a dual-prior hybrid segmentation network that integrates both boundary priors and shape priors. In the encoder, a gradient-driven Boundary-augmented Hybrid Attention module is constructed to jointly capture long-range contextual information through explicit boundary enhancement, self-attention, and state space–inspired modeling. In the decoder, a Multi-scale Lesion Shape Prior module is designed to impose global structural constraints on the segmentation mask via multi-scale shape priors and a unified loss formulation, thereby balancing fine-grained contour precision with overall…
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Taxonomy
TopicsCutaneous Melanoma Detection and Management · Advanced Neural Network Applications · Face recognition and analysis