Unsupervised Learning in PET Radiomics.

G Liu, S-Y Huang, B Franc, Y Seo, D Mitra
{"title":"Unsupervised Learning in PET Radiomics.","authors":"G Liu,&nbsp;S-Y Huang,&nbsp;B Franc,&nbsp;Y Seo,&nbsp;D Mitra","doi":"10.1109/NSSMIC.2017.8532959","DOIUrl":null,"url":null,"abstract":"<p><p>In this study, we investigated large scale radoimics on 116 breast cancer patients. We are particularly interested in unsupervised learning to bicluster patients and features in order to associate such biclusters with the disease characteristics. The results show that radiomics features with wavelet features have a better biclustering ability. And 172 radiomics features have shown a better classification capability.</p>","PeriodicalId":73298,"journal":{"name":"IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/NSSMIC.2017.8532959","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NSSMIC.2017.8532959","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2018/11/15 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In this study, we investigated large scale radoimics on 116 breast cancer patients. We are particularly interested in unsupervised learning to bicluster patients and features in order to associate such biclusters with the disease characteristics. The results show that radiomics features with wavelet features have a better biclustering ability. And 172 radiomics features have shown a better classification capability.

Abstract Image

Abstract Image

Abstract Image

PET放射组学中的无监督学习。
在这项研究中,我们对116名乳腺癌患者进行了大规模的放射学研究。我们对无监督学习对患者和特征进行双聚类特别感兴趣,以便将这种双聚类与疾病特征联系起来。结果表明,结合小波特征的放射组学特征具有较好的聚类能力。172个放射组学特征显示出较好的分类能力。
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
自引率
0.00%
发文量
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×
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:481959085
Book学术官方微信