{"title":"PET放射组学中的无监督学习。","authors":"G Liu, S-Y Huang, B Franc, Y Seo, 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":"2017 ","pages":""},"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":"{\"title\":\"Unsupervised Learning in PET Radiomics.\",\"authors\":\"G Liu, S-Y Huang, B Franc, Y Seo, 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\":\"2017 \",\"pages\":\"\"},\"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}","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}
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.
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