Recovery of Noisy Pooled Tests via Learned Factor Graphs with Application to COVID-19 Testing

Eyal Fishel Ben-Knaan, Yonina C. Eldar, Nir Shlezinger
{"title":"Recovery of Noisy Pooled Tests via Learned Factor Graphs with Application to COVID-19 Testing","authors":"Eyal Fishel Ben-Knaan, Yonina C. Eldar, Nir Shlezinger","doi":"10.1109/icassp43922.2022.9747150","DOIUrl":null,"url":null,"abstract":"The ongoing pandemic and the necessity of frequent testing have spurred a growing interest in pooled testing. Conventional recovery methods from pooled tests are based on group testing or compressed sensing tools which rely on simplistic modeling of the pooling process, and may not be reliable in the presence of complex and noisy measurement procedures and highly infected populations. In this work, we propose a strategy for pooled testing designed for noisy settings, which bypasses the need for a tractable acquisition model. This is achieved by combining deep learning, for implicitly learning the measurement relationship from data, with factor graph inference, which exploits the structured known pooling pattern. Learned factor graphs provide a quantitative readout corresponding to the infection severity, as opposed to group testing which only detects the presence of infection. The proposed scheme is shown to achieve improved robustness to noise compared with previous approaches and to reliably estimate in highly infected populations.","PeriodicalId":272439,"journal":{"name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/icassp43922.2022.9747150","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

The ongoing pandemic and the necessity of frequent testing have spurred a growing interest in pooled testing. Conventional recovery methods from pooled tests are based on group testing or compressed sensing tools which rely on simplistic modeling of the pooling process, and may not be reliable in the presence of complex and noisy measurement procedures and highly infected populations. In this work, we propose a strategy for pooled testing designed for noisy settings, which bypasses the need for a tractable acquisition model. This is achieved by combining deep learning, for implicitly learning the measurement relationship from data, with factor graph inference, which exploits the structured known pooling pattern. Learned factor graphs provide a quantitative readout corresponding to the infection severity, as opposed to group testing which only detects the presence of infection. The proposed scheme is shown to achieve improved robustness to noise compared with previous approaches and to reliably estimate in highly infected populations.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于学习因子图的噪声池测试恢复及其在COVID-19测试中的应用
持续的大流行和频繁检测的必要性促使人们对集中检测越来越感兴趣。混合测试的常规恢复方法基于群体测试或压缩传感工具,这些工具依赖于混合过程的简单建模,在复杂和嘈杂的测量程序和高度感染的人群存在时可能不可靠。在这项工作中,我们提出了一种针对噪声设置的池化测试策略,该策略绕过了对可处理采集模型的需求。这是通过将深度学习(用于隐式地从数据中学习测量关系)与因子图推理(利用结构化已知池化模式)相结合来实现的。学习因子图提供了与感染严重程度相对应的定量读数,而不是仅检测感染存在的组测试。与以前的方法相比,所提出的方案具有更好的噪声鲁棒性,并且在高感染人群中可靠地进行估计。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Spatio-Temporal Attention Graph Convolution Network for Functional Connectome Classification Improving Biomedical Named Entity Recognition with a Unified Multi-Task MRC Framework Combining Multiple Style Transfer Networks and Transfer Learning For LGE-CMR Segmentation Sensors to Sign Language: A Natural Approach to Equitable Communication Estimation of the Admittance Matrix in Power Systems Under Laplacian and Physical Constraints
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1