中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
Achieving fair medical image segmentation in foundation models with adversarial visual prompt tuning

文献类型：期刊论文

作者Li, Yuqi1; Li, Yanli2; Zhang, Kai3; Zhang, Fuyan; Yang, Chuanguang; Guo, Zhongliang4; Ding, Weiping2,5; Huang, Tingwen3
刊名INFORMATION SCIENCES
出版日期2025-12-01
卷号720页码:16
关键词Fairness Image segmentation Foundation models Visual prompt tuning Medicine data analysis
ISSN号0020-0255
DOI10.1016/j.ins.2025.122501
英文摘要Recent advances in deep learning have significantly enhanced medical image analysis capabilities. Medical image segmentation, a critical application in this domain, enables precise delineation of anatomical structures and pathological regions, substantially supporting clinical decision-making. However, current segmentation methods primarily optimize for overall performance without considering disparities across demographic groups, raising important fairness concerns. To address this gap, we propose Adversarial Visual Prompt Tuning (AdvVPT), a parameter-efficient approach that enhances fairness in foundation models for medical image segmentation. AdvVPT introduces trainable visual prompts within the image encoder while keeping the backbone frozen, requiring only 0.812M additional parameters. These prompts are optimized through adversarial training to absorb demographic-specific biased information from image embeddings, achieved by maximizing prediction errors for sensitive attributes and increasing embedding distances between visual prompts and image features. Experimental evaluation on the Harvard-FairSeg dataset demonstrates that AdvVPT achieves state-of-the-art fairness performance across multiple demographic attributes. For racial fairness, AdvVPT achieves an ES-Dice score of 0.8996 and an ES-IoU score of 0.8222 on optic cup segmentation, substantially outperforming existing methods. For gender fairness using the SAT backbone, AdvVPT achieves an ES-Dice of 0.9297 and ES-IoU of 0.8614, demonstrating both superior performance and improved balance between male and female subgroups.
资助项目National Key RD Plan of China[2024YFE0202700]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001533716700004
出版者ELSEVIER SCIENCE INC
源URL[http://119.78.100.204/handle/2XEOYT63/42034]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Ding, Weiping; Huang, Tingwen
作者单位1.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
2.Nantong Univ, Sch Artificial Intelligence & Comp Sci, Nantong 226019, Peoples R China
3.Shenzhen Univ Adv Technol, Shenzhen 518107, Peoples R China
4.Univ St Andrews, Sch Comp Sci, St Andrews, Scotland
5.City Univ Macau, Fac Data Sci, Macau 999078, Peoples R China
推荐引用方式
GB/T 7714		 Li, Yuqi,Li, Yanli,Zhang, Kai,et al. Achieving fair medical image segmentation in foundation models with adversarial visual prompt tuning[J]. INFORMATION SCIENCES,2025,720:16.
APA Li, Yuqi.,Li, Yanli.,Zhang, Kai.,Zhang, Fuyan.,Yang, Chuanguang.,...&Huang, Tingwen.(2025).Achieving fair medical image segmentation in foundation models with adversarial visual prompt tuning.INFORMATION SCIENCES,720,16.
MLA Li, Yuqi,et al."Achieving fair medical image segmentation in foundation models with adversarial visual prompt tuning".INFORMATION SCIENCES 720(2025):16.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。