ArticleQuantitative imaging in medicine and surgery2025
Seg-SkiNet: adaptive deformable fusion convolutional network for skin lesion segmentation.
Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- EMCAH-Net: an effective multi-scale context aggregation hybrid network for medical image segmentation.Quantitative imaging in medicine and surgery · 2025Article
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4 authors.
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Abstract
Background: Skin lesion segmentation plays a significant role in skin cancer diagnosis. However, due to the complex shapes, varying sizes, and different color depths, precise segmentation of skin lesions is a challenging task. Therefore, the aim of this study was to design a customized deep learning (DL) model for the precise segmentation of skin lesions, particularly for complex shapes and small target lesions. Methods: In this study, an adaptive deformable fusion convolutional network (Seg-SkiNet) was proposed. Seg-SkiNet integrated dual-channel convolution encoder (Dual-Conv encoder), Multi-Scale-Multi-Receptive Field Extraction and Refinement (Multi Results: We validated the performance of Seg-SkiNet for skin lesion segmentation on three public datasets: International Skin Imaging Collaboration (ISIC)-2016, ISIC-2017, and ISIC-2018. The experimental results demonstrated that Seg-SkiNet achieved a Dice coefficient (DICE) of 93.66%, 89.44% and 92.29%, respectively. Conclusions: The Seg-SkiNet model performed excellently in segmenting complex-shaped lesions and small target lesions.
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