Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment.
| Publicado en: | European Radiology Vol. 31; no. 1; pp. 302 - 314 |
|---|---|
| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
2021
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=147734712&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 147734712 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: 2021 vid: 31 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 147734712 145015562 10.1007/s00330-020-07086-z 147734712 ppf: 302 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment. aug: au: Schelb, Patrick Wang, Xianfeng Radtke, Jan Philipp Wiesenfarth, Manuel Kickingereder, Philipp Stenzinger, Albrecht Hohenfellner, Markus Schlemmer, Heinz-Peter Maier-Hein, Klaus H. Bonekamp, David affil: Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
|---|