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1
Maximizing the Predictive Power of Radiomic Features in SPE..:
, In:
2023 IEEE Nuclear Science Symposium, Medical Imaging Conference and International Symposium on Room-Temperature Semiconductor Detectors (NSS MIC RTSD)
,
Gharibi, O.
;
Sabouri, M.
;
Hajianfar, G.
... - p. 1-2 , 2023
Link:
https://doi.org/10.1109/NSSMICRTSD49126.2023.10338650
RT T1 <cTitle>2023 IEEE Nuclear Science Symposium, Medical Imaging Conference and International Symposium on Room-Temperature Semiconductor Detectors (NSS MIC RTSD)</cTitle>: T1 <Title>Maximizing the Predictive Power of Radiomic Features in SPECT Images: A Comparative Study of Reconstruction Algorithms Using Machine Learning</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10338650&Exemplar=1&LAN=DE A1 Gharibi, O. A1 Sabouri, M. A1 Hajianfar, G. A1 Mohebi, M. A1 Amini, M. A1 Bagheri, S. A1 Arian, F. A1 Yasemi, M. J. A1 Bitarafan-Rajabi, A. A1 Zaidi, H. A1 Rahmim, A. A1 Shiri, I. YR 2023 SN 2577-0829 K1 Microwave integrated circuits K1 Semiconductor detectors K1 Machine learning K1 Reconstruction algorithms K1 Feature extraction K1 Iterative methods K1 Single photon emission computed tomography SP 1 OP 2 LK http://dx.doi.org/https://doi.org/10.1109/NSSMICRTSD49126.2023.10338650 DO https://doi.org/10.1109/NSSMICRTSD49126.2023.10338650 SF ELIB - SuUB Bremen
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