郑州大学学报（理学版）2026，Vol.58Issue(1)：65-71,7.DOI:10.13705/j.issn.1671-6841.2024091
基于局部上下文引导特征深度融合的轻量级医学图像分割方法
Lightweight Medical Segmentation Network Based on Local Context Guided Feature Deep Fusion
摘要
Abstract
For most of the existing deep learning-based medical image segmentation,a large amount of training data were used to improve the detection network to achieve excellent detection performance.These methods needed a large number of the models and parameter,resulting in poor real-time detection performance.To address this,a local context guided feature deep fusion lightweight medical segmentation network(LCGML-net)was proposed.The main idea of LCGML-net was to reduce the number of parame-ters required for model fitting by accurate feature selection and feature fusion,thus achieving smaller model while maintaining detection accuracy.LCGML-net enriched the feature representation accurate pre-cision segmentation of the target by extracting dense multi-level and multi-scale local context features of the target in the feature extraction stage and the feature mapping stage,respectively.Extensive experi-ments were conducted on multiple medical segmentation benchmark datasets,including STARE,CHASEDB1,and KITS19.The results demonstrated that compared to other advanced methods,the pro-posed LCGML-net exhibited the best detection performance with the smallest model parameters.关键词
医学图像分割/神经网络/局部上下文特征/特征深度融合Key words
medical image segmentation/neural network/local context feature/deep fusion分类
信息技术与安全科学引用本文复制引用
任向阳,赵梦媛,胡微,刘刚琼,毕莹..基于局部上下文引导特征深度融合的轻量级医学图像分割方法[J].郑州大学学报（理学版）,2026,58(1):65-71,7.基金项目
国家自然科学基金项目(62206251 （）
62476253) （）
河南省青年人才托举工程项目(2024HYTP038) （2024HYTP038）
河南省医学科技攻关计划联合共建项目(LHGJ20220431) （LHGJ20220431）
河南省中青年卫生健康科技创新杰出青年人才培养项目(YXKC2021041) （YXKC2021041）
河南省自然科学基金项目(242300421401) （242300421401）
河南省科技研发联合基金(产业类)重大项目(245101610001) （产业类）
