版权说明 操作指南
首页 > 成果 > 详情

Rethinking Feature Guidance for Medical Image Segmentation

认领
导出
Link by DOI
反馈
分享
QQ微信 微博
成果类型:
期刊论文
作者:
Wang, Wei;He, Jixing;Wang, Xin
通讯作者:
Wang, X
作者机构:
[He, Jixing; Wang, Wei; Wang, Xin] Changsha Univ Sci & Technol, Sch Comp & Commun Engn, Changsha 410114, Peoples R China.
通讯机构:
[Wang, X ] C
Changsha Univ Sci & Technol, Sch Comp & Commun Engn, Changsha 410114, Peoples R China.
语种:
英文
关键词:
Feature extraction;Image segmentation;Lesions;Medical diagnostic imaging;Convolution;Logic gates;Transformers;Data mining;COVID-19;Accuracy;Feature guidance;feature interactions;medical image segmentation
期刊:
IEEE Signal Processing Letters
ISSN:
1070-9908
年:
2025
卷:
32
页码:
641-645
基金类别:
National Science Innovation Special Zone Project (Grant Number: 2019XXX00701) National Key Basic Research Program Project (Grant Number: 624XXXX0206) Key Research and Development Projects of Hunan Province (Grant Number: 2020SK2134) 10.13039/501100004735-Natural Science Foundation of Hunan Province (Grant Number: 2019JJ80105 and 2022JJ30625)
机构署名:
本校为第一且通讯机构
院系归属:
计算机与通信工程学院
摘要:
Despite the evident advantages of variants of UNet in medical image segmentation, these methods still exhibit limitations in the extraction of foreground, background, and boundary features. Based on feature guidance, we propose a new network (FG-UNet). Specifically, adjacent high-level and low-level features are used to gradually guide the network to perceive lesion features. To accommodate lesion features of different scales, the multi-order gated aggregation (MGA) block is designed based on multi-order feature interactions. Furthermore, a novel feature-guided context-aware (FGCA) block is de...

反馈

验证码:
看不清楚,换一个
确定
取消

成果认领

标题:
用户 作者 通讯作者
请选择
请选择
确定
取消

提示

该栏目需要登录且有访问权限才可以访问

如果您有访问权限,请直接 登录访问

如果您没有访问权限,请联系管理员申请开通

管理员联系邮箱:yun@hnwdkj.com