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 , Boyang Jiao , Jianbo Gao , Yazheng Chen , Na Xiao , Ying Bi , 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.
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.