Artificial intelligence and imaging for diagnostic and treatment challenges in breast care : second Deep Breast Workshop, Deep-Breath 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings. Dee...最新文献

筛选
英文 中文
Longitudinal Mammogram Exam-Based Breast Cancer Diagnosis Models: Vulnerability to Adversarial Attacks. 基于纵向乳房x光检查的乳腺癌诊断模型:对抗性攻击的脆弱性。
Zhengbo Zhou, Degan Hao, Dooman Arefan, Margarita Zuley, Jules Sumkin, Lars Grimm, James Joshi, Shandong Wu
{"title":"Longitudinal Mammogram Exam-Based Breast Cancer Diagnosis Models: Vulnerability to Adversarial Attacks.","authors":"Zhengbo Zhou, Degan Hao, Dooman Arefan, Margarita Zuley, Jules Sumkin, Lars Grimm, James Joshi, Shandong Wu","doi":"10.1007/978-3-032-05559-0_35","DOIUrl":"10.1007/978-3-032-05559-0_35","url":null,"abstract":"<p><p>In breast cancer detection and diagnosis, the longitudinal analysis of mammogram images is crucial. Contemporary models excel in detecting temporal imaging feature changes, thus enhancing the learning process over sequential imaging exams. Yet, the resilience of these longitudinal models against adversarial attacks remains underexplored. In this paper, we propose a novel blackbox attack approach that capitalizes on the feature-level relationship between two sequential mammogram exams of a longitudinal model, guided by both cross-entropy loss and distance metric learning, to achieve significant attack efficacy as implemented using attack transferring in a black-box attacking manner. We perform experiments on a cohort of 590 breast cancer patients (each has two sequential mammogram exams) in a case-control setting. Results show that our proposed method surpasses several state-of-the-art adversarial attacks in fooling the diagnosis models to give opposite outputs. Our method remains effective even if the model is trained by employing existing defense mechanisms for adversarial attacks.</p>","PeriodicalId":524259,"journal":{"name":"Artificial intelligence and imaging for diagnostic and treatment challenges in breast care : second Deep Breast Workshop, Deep-Breath 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings. Dee...","volume":"16142 ","pages":"351-361"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13678561/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149229846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
0
×
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×
相关产品
×
本文献相关产品
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
小红书