Yuxuan Luo , Yongquan Xue , Yifei Teng , Liejun Wang, Panpan Zheng
{"title":"MLK-Net: Leveraging multi-scale and large kernel convolutions for robust skin lesion segmentation","authors":"Yuxuan Luo , Yongquan Xue , Yifei Teng , Liejun Wang, Panpan Zheng","doi":"10.1016/j.eswa.2025.127135","DOIUrl":null,"url":null,"abstract":"<div><div>Accurate medical image segmentation, especially for skin lesions, is challenging due to fuzzy boundaries, diverse shapes, and varying lesion sizes. Most existing SSM-based skin lesion segmentation methods use pure SSM or simply combine CNN with SSM as the network backbone, but often fail to fully consider background information and multi-scale features. To address these issues, we propose a novel network that integrates large convolution kernels for rich background feature extraction and combines multi-head mixed convolutions with small and large kernels to capture multi-scale features. This design improves the segmentation of complex structures and diverse lesion scales. Experiments on three benchmark skin lesion segmentation datasets demonstrate that our method consistently outperforms state-of-the-art approaches across multiple evaluation metrics, showcasing its robustness and effectiveness in tackling critical segmentation challenges. For reproduction, the implementation codes can be checked out at <span><span>https://github.com/yuxl2023/MLK-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"276 ","pages":"Article 127135"},"PeriodicalIF":7.5000,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert Systems with Applications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0957417425007572","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
MLK-Net: Leveraging multi-scale and large kernel convolutions for robust skin lesion segmentation
Accurate medical image segmentation, especially for skin lesions, is challenging due to fuzzy boundaries, diverse shapes, and varying lesion sizes. Most existing SSM-based skin lesion segmentation methods use pure SSM or simply combine CNN with SSM as the network backbone, but often fail to fully consider background information and multi-scale features. To address these issues, we propose a novel network that integrates large convolution kernels for rich background feature extraction and combines multi-head mixed convolutions with small and large kernels to capture multi-scale features. This design improves the segmentation of complex structures and diverse lesion scales. Experiments on three benchmark skin lesion segmentation datasets demonstrate that our method consistently outperforms state-of-the-art approaches across multiple evaluation metrics, showcasing its robustness and effectiveness in tackling critical segmentation challenges. For reproduction, the implementation codes can be checked out at https://github.com/yuxl2023/MLK-Net.
期刊介绍:
Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.