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From quantitative metrics to clinical success: assessing th..:
Yeung, Chris
;
Ungi, Tamas
;
Hu, Zoe
...
International Journal of Computer Assisted Radiology and Surgery. 19 (2024) 6 - p. 1193-1201 , 2024
Link:
https://doi.org/10.1007/s11548-024-03133-y
RT Journal T1 <Title>From quantitative metrics to clinical success: assessing the utility of deep learning for tumor segmentation in breast surgery</Title> UL https://suche.suub.uni-bremen.de/peid=cr-10.1007_s11548-024-03133-y&Exemplar=1&LAN=DE A1 Yeung, Chris A1 Ungi, Tamas A1 Hu, Zoe A1 Jamzad, Amoon A1 Kaufmann, Martin A1 Walker, Ross A1 Merchant, Shaila A1 Engel, Cecil Jay A1 Jabs, Doris A1 Rudan, John A1 Mousavi, Parvin A1 Fichtinger, Gabor PB Springer Science and Business Media LLC YR 2024 SN 1861-6429 JF International Journal of Computer Assisted Radiology and Surgery VO 19 IS 6 SP 1193 OP 1201 LK http://dx.doi.org/https://doi.org/10.1007/s11548-024-03133-y DO https://doi.org/10.1007/s11548-024-03133-y SF ELIB - SuUB Bremen
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