Lightweight decision-making decisive feature enhancement network for medical image analysis

IF 6.6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xiangyang Ren , Boyang Jiao , Jianbo Gao , Yazheng Chen , Na Xiao , Ying Bi , Gangqiong Liu
{"title":"Lightweight decision-making decisive feature enhancement network for medical image analysis","authors":"Xiangyang Ren ,&nbsp;Boyang Jiao ,&nbsp;Jianbo Gao ,&nbsp;Yazheng Chen ,&nbsp;Na Xiao ,&nbsp;Ying Bi ,&nbsp;Gangqiong Liu","doi":"10.1016/j.asoc.2025.113518","DOIUrl":null,"url":null,"abstract":"<div><div>Medical image segmentation is crucial in diagnosing and treating various diseases. Most existing medical segmentation methods often overlook the importance of selecting decision features, resulting in the extraction of redundant target features, which often leads to a large number of model parameters and poor deployability. Therefore, to reduce the parameter count of medical image segmentation models and improve their deployability, we propose a two-phase detection network based on enhancing decision-making decisive (DMD) features, termed the Decision-Making Decisive Feature Enhancement Network (DDFE-Net). The core idea of DDFE-net is to reduce the number of parameters required for model fitting and redundant target features by screening and enhancing the features that are important for decision-making. Specifically, in the DDFE-net, we first propose a decision network (DE-net) for initially screening and extracting DMD features through dense multi-level feature fusion and deep supervision. The DMD features of medical targets are effectively extracted through dense multi-level feature extraction and fusion. Subsequently, we introduced a DMD feature enhancement network (DEE-net) into the DDFE network to enhance the feature representation of medical targets. The DEE-net integrates DMD features of different scales and levels in the DE-net by performing secondary encoding and decoding on the extracted DMD features, thereby achieving DMD feature enhancement and further eliminating redundant features, reducing the number of model parameters, and improving the network's feature expression ability. Extensive experimental results on several medical segmentation benchmark datasets, prove that the proposed DDFE-net outperforms other state-of-the-art (SOTA) methods by 8 % in accuracy and achieves a 49 % reduction in model size, greatly improving the deployability of medical image segmentation methods.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"181 ","pages":"Article 113518"},"PeriodicalIF":6.6000,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Soft Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1568494625008294","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}
引用次数: 0

Abstract

Medical image segmentation is crucial in diagnosing and treating various diseases. Most existing medical segmentation methods often overlook the importance of selecting decision features, resulting in the extraction of redundant target features, which often leads to a large number of model parameters and poor deployability. Therefore, to reduce the parameter count of medical image segmentation models and improve their deployability, we propose a two-phase detection network based on enhancing decision-making decisive (DMD) features, termed the Decision-Making Decisive Feature Enhancement Network (DDFE-Net). The core idea of DDFE-net is to reduce the number of parameters required for model fitting and redundant target features by screening and enhancing the features that are important for decision-making. Specifically, in the DDFE-net, we first propose a decision network (DE-net) for initially screening and extracting DMD features through dense multi-level feature fusion and deep supervision. The DMD features of medical targets are effectively extracted through dense multi-level feature extraction and fusion. Subsequently, we introduced a DMD feature enhancement network (DEE-net) into the DDFE network to enhance the feature representation of medical targets. The DEE-net integrates DMD features of different scales and levels in the DE-net by performing secondary encoding and decoding on the extracted DMD features, thereby achieving DMD feature enhancement and further eliminating redundant features, reducing the number of model parameters, and improving the network's feature expression ability. Extensive experimental results on several medical segmentation benchmark datasets, prove that the proposed DDFE-net outperforms other state-of-the-art (SOTA) methods by 8 % in accuracy and achieves a 49 % reduction in model size, greatly improving the deployability of medical image segmentation methods.
用于医学图像分析的轻量级决策决定性特征增强网络
医学图像分割是诊断和治疗各种疾病的关键。现有的大多数医学分割方法往往忽略了决策特征选择的重要性,导致目标特征提取冗余,往往导致模型参数过多,可部署性差。因此,为了减少医学图像分割模型的参数数量,提高模型的可部署性,我们提出了一种基于决策决定性特征增强的两阶段检测网络,称为决策决定性特征增强网络(DDFE-Net)。DDFE-net的核心思想是通过筛选和增强对决策重要的特征,减少模型拟合所需的参数数量和冗余的目标特征。具体而言,在DDFE-net中,我们首先提出了一种决策网络(DE-net),通过密集的多层次特征融合和深度监督,对DMD特征进行初步筛选和提取。通过密集的多层次特征提取与融合,有效提取医学目标的DMD特征。随后,我们在DDFE网络中引入了一个DMD特征增强网络(DEE-net)来增强医学目标的特征表示。DEE-net通过对提取的DMD特征进行二次编码和解码,将不同尺度和层次的DMD特征集成到DE-net中,从而实现DMD特征增强,进一步消除冗余特征,减少模型参数的数量,提高网络的特征表达能力。在多个医学图像分割基准数据集上的大量实验结果证明,所提出的DDFE-net的准确率比其他最先进的SOTA方法提高了8 %,模型尺寸减少了49 %,大大提高了医学图像分割方法的可部署性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Applied Soft Computing
Applied Soft Computing 工程技术-计算机:跨学科应用
CiteScore
15.80
自引率
6.90%
发文量
874
审稿时长
10.9 months
期刊介绍: Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities. Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信