Scale Feature-Aware Generative Adversarial Network Improve MRI Device Data Imbalance for Healthy Consumption

IF 4.3 2区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Yawu Zhao;Shudong Wang;Sibo Qiao;Yulin Zhang;Jiehuan Wang;Wenjing Yin;Tiyao Liu;Shanchen Pang
{"title":"Scale Feature-Aware Generative Adversarial Network Improve MRI Device Data Imbalance for Healthy Consumption","authors":"Yawu Zhao;Shudong Wang;Sibo Qiao;Yulin Zhang;Jiehuan Wang;Wenjing Yin;Tiyao Liu;Shanchen Pang","doi":"10.1109/TCE.2025.3540776","DOIUrl":null,"url":null,"abstract":"In consumer healthcare, the imbalance between the imaging data of MRI devices creates a significant barrier to the generalization ability of medical image segmentation. We employ generative adversarial networks to address the challenges inherent in the heterogeneity of data distribution among different MRI devices. First, we propose the Scale Feature Awareness Module (SFAM), which can skillfully capture image details at different scales and broader contextual information. Then, we propose the Dynamic Scale Attention Module (DSAM), which aims to combine feature mappings at different scales for different paths dynamically. We can assign different dynamic weights to each scale to enhance the feature representation. Finally, we propose a multi-scale discriminator to guide the generation of aneurysm images with different diameters. Based on the above modules, we designed Scale Feature Aware Generative Adversarial Network (SFAGAN) to generate medical images with the same distribution. It is experimentally demonstrated that SFAGAN improves PSNR, SSIM, and FID values by 0.64, 0.62, and 7.99, respectively, over the SOTA method. In addition, we use the generated data for downstream segmentation tasks to demonstrate the quality of the generated images. Notably, our SFAGAN has significant performance, making its application in medical systems very feasible.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"984-996"},"PeriodicalIF":4.3000,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Consumer Electronics","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10879567/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

In consumer healthcare, the imbalance between the imaging data of MRI devices creates a significant barrier to the generalization ability of medical image segmentation. We employ generative adversarial networks to address the challenges inherent in the heterogeneity of data distribution among different MRI devices. First, we propose the Scale Feature Awareness Module (SFAM), which can skillfully capture image details at different scales and broader contextual information. Then, we propose the Dynamic Scale Attention Module (DSAM), which aims to combine feature mappings at different scales for different paths dynamically. We can assign different dynamic weights to each scale to enhance the feature representation. Finally, we propose a multi-scale discriminator to guide the generation of aneurysm images with different diameters. Based on the above modules, we designed Scale Feature Aware Generative Adversarial Network (SFAGAN) to generate medical images with the same distribution. It is experimentally demonstrated that SFAGAN improves PSNR, SSIM, and FID values by 0.64, 0.62, and 7.99, respectively, over the SOTA method. In addition, we use the generated data for downstream segmentation tasks to demonstrate the quality of the generated images. Notably, our SFAGAN has significant performance, making its application in medical systems very feasible.
规模特征感知生成对抗网络改善MRI设备健康消费数据不平衡
在消费者医疗保健中,MRI设备成像数据之间的不平衡对医学图像分割的泛化能力造成了很大的障碍。我们采用生成对抗网络来解决不同MRI设备之间数据分布异质性所固有的挑战。首先,我们提出了尺度特征感知模块(sfm),该模块可以巧妙地捕获不同尺度的图像细节和更广泛的上下文信息。然后,我们提出了动态尺度关注模块(Dynamic Scale Attention Module, DSAM),该模块旨在对不同路径的不同尺度特征映射进行动态组合。我们可以为每个尺度分配不同的动态权重来增强特征表示。最后,我们提出了一种多尺度判别器来指导不同直径动脉瘤图像的生成。在上述模块的基础上,我们设计了Scale Feature Aware Generative Adversarial Network (SFAGAN)来生成具有相同分布的医学图像。实验表明,与SOTA方法相比,SFAGAN方法的PSNR、SSIM和FID值分别提高了0.64、0.62和7.99。此外,我们将生成的数据用于下游分割任务,以演示生成图像的质量。值得注意的是,我们的SFAGAN具有显著的性能,使其在医疗系统中的应用非常可行。
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
CiteScore
7.70
自引率
9.30%
发文量
59
审稿时长
3.3 months
期刊介绍: The main focus for the IEEE Transactions on Consumer Electronics is the engineering and research aspects of the theory, design, construction, manufacture or end use of mass market electronics, systems, software and services for consumers.
×
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