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根据全国乳腺摄影数据库的临床资料开发计算机概率模型以进行乳腺X线征象分类 被引量:1
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作者 E.S.Burnside j.davis +5 位作者 J.Chhatwal O.A lagoz M.J.Lindstrom B.M.Geller 孙东辉(译) 万业达(校) 《国际医学放射学杂志》 2009年第4期388-389,共2页
目的确定经大型数据库中乳腺病人统计学危险因素和放射学医师对临床连续乳腺X线检查观察结果所开发的Bayesian网络对乳腺良恶性病变X线征象的判断是否优于放射学医师。方法机构审查委员会免除该项符合HIPAA的回顾性研究的知情同意书。... 目的确定经大型数据库中乳腺病人统计学危险因素和放射学医师对临床连续乳腺X线检查观察结果所开发的Bayesian网络对乳腺良恶性病变X线征象的判断是否优于放射学医师。方法机构审查委员会免除该项符合HIPAA的回顾性研究的知情同意书。收集从1999年4月5日-2004年2月9日期间连续48744筛查病例的结构化报告和18269例诊断性乳腺X线检查。X线表现与国家癌症登记相匹配,并以后者作为参考标准。使用10倍交叉验证, 展开更多
关键词 X线征象 数据库 乳腺摄影 概率模型 资料开发 计算机 乳腺X线检查 乳腺良恶性病变
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Search for two-neutrino double-beta decay of^(136)Xe to the 0^(+)_(1)excited state of 136Ba with the complete EXO-200 dataset
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作者 S.Al Kharusi G.Anton +104 位作者 I.Badhrees P.S.Barbeau D.Beck V.Belov T.Bhatta M.Breidenbach T.Brunner G.F.Cao W.R.Cen C.Chambers B.Cleveland M.Coon A.Craycraft T.Daniels L.Darroch S.J.Daugherty j.davis S.Delaquis A.Der Mesrobian-Kabakian R.DeVoe J.Dilling A.Dolgolenko M.J.Dolinski J.Echevers W.Fairbank Jr. D.Fairbank J.Farine S.Feyzbakhsh P.Fierlinger Y.S.Fu D.Fudenberg P.Gautam R.Gornea G.Gratta C.Hall E.V.Hansen J.Hoessl P.Hufschmidt M.Hughes A.Iverson A.Jamil C.Jessiman M.J.Jewell A.Johnson A.Karelin L.J.Kaufman T.Koffas R.Krücken A.Kuchenkov K.S.Kumar Y.Lan A.Larson B.G.Lenardo D.S.Leonard G.S.Li S.Li Z.Li C.Licciardi Y.H.Lin R.MacLellan T.McElroy T.Michel B.Mong D.C.Moore K.Murray O.Njoya O.Nusair A.Odian I.Ostrovskiy A.Perna A.Piepke A.Pocar F.Retière A.L.Robinson P.C.Rowson J.Runge S.Schmidt D.Sinclair K.Skarpaas A.K.Soma V.Stekhanov M.Tarka S.Thibado J.Todd T.Tolba T.I.Totev R.Tsang B.Veenstra V.Veeraraghavan P.Vogel J.-L.Vuilleumier M.Wagenpfeil J.Watkins M.Weber L.J.Wen U.Wichoski G.Wrede S.X.Wu Q.Xia D.R.Yahne L.Yang Y.-R.Yen O.Ya.Zeldovich T.Ziegler 《Chinese Physics C》 SCIE CAS CSCD 2023年第10期1-9,共9页
A new search for two-neutrino double-beta(2νββ)decay of^(136)Xe to the 0+1 excited state of 136Ba is performed with the full EXO-200 dataset.A deep learning-based convolutional neural network is used to discriminat... A new search for two-neutrino double-beta(2νββ)decay of^(136)Xe to the 0+1 excited state of 136Ba is performed with the full EXO-200 dataset.A deep learning-based convolutional neural network is used to discriminate signal from background events.Signal detection efficiency is increased relative to previous searches by EXO-200 by more than a factor of two.With the addition of the Phase II dataset taken with an upgraded detector,the median 90%confidence level half-life sensitivity of 2νββdecay to the 0+1 state of 136Ba is 2.9×10^(24)yr using a total^(136)Xe exposure of 234.1 kg yr.No statistically significant evidence for 2νββdecay to the 0^(+)_(1)state is observed,leading to a lower limit of T2ν1/2(0^(+)→0^(+)_(1))>1.4×10^(24)yr at 90%confidence level,improved by 70%relative to the current world's best constraint. 展开更多
关键词 EXO-200 experiment neutrinoless double beta decay excited state
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