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Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI
by
Papp, L.
,
Spielvogel, C. P.
,
Grubmüller, B.
,
Grahovac, M.
,
Krajnc, D.
,
Ecsedi, B.
,
Sareshgi, R. A.M.
,
Mohamad, D.
,
Hamboeck, M.
,
Rausch, I.
,
Mitterhauser, M.
,
Wadsak, W.
,
Haug, A. R.
,
Kenner, L.
,
Mazal, P.
,
Susani, M.
,
Hartenbach, S.
,
Baltzer, P.
,
Helbich, T. H.
,
Kramer, G.
,
Shariat, S.F.
,
Beyer, T.
,
Hartenbach, M.
,
Hacker, M.
Published in
European journal of nuclear medicine and molecular imaging
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European Journal Of Nuclear Medicine And Molecular Imaging
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Advanced Image Analyses
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Antigens
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Biochemical Recurrence Prediction
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Biopsy
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Cancer Surgery
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Cardiology
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Classification
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Diffusion Coefficient
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Emission Analysis
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Imaging
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Learning Algorithms
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Lesion Risk Prediction
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Lesions
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Life Sciences & Biomedicine
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Machine Learning
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Magnetic Resonance Imaging
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