Geert Litjens
Geert Litjens
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Dealing with Label Scarcity in Computational Pathology: A Use Case in Prostate Cancer Classification
K. Dercksen
,
W. Bulten
,
G. Litjens
Automated Gleason Grading of Prostate Biopsies Using Deep Learning
W. Bulten
,
H. Pinckaers
,
C. Hulsbergen - van de Kaa
,
G. Litjens
Training convolutional neural networks with megapixel images
H. Pinckaers
,
G. Litjens
Unsupervised Prostate Cancer Detection on H&E using Convolutional Adversarial Autoencoders
W. Bulten
,
G. Litjens
Computer-aided Detection of Prostate Cancer in Multi-parametric Magnetic Resonance Imaging
G. Litjens
,
N. Karssemeijer
,
J. Barentsz
,
H. Huisman
Multiparametric MR imaging for the assessment of prostate cancer aggressiveness at 3 Tesla
E. Vos
,
T. Kobus
,
G. Litjens
,
T. Hambrock
,
C. de Kaa
,
M. Maas
,
T. Scheenen
Initial prospective evaluation of the prostate imaging reporting and data standard (PI-RADS): Can it reduce unnecessary MR guided biopsies?
G. Litjens
,
J. Barentsz
,
N. Karssemeijer
,
H. Huisman
Prostate Cancer localization with a Multiparametric MR Approach (PCaMAP): initial results of a multi-center study
M. Maas
,
M. Koopman
,
G. Litjens
,
A. Wright
,
K. Selnas
,
I. Gribbestad
,
M. Haider
,
K. Macura
,
D. Margolis
,
B. Kiefer
,
J. Futterer
,
T. Scheenen
Computerized characterization of central gland lesions using texture and relaxation features from T2-weighted prostate MRI
G. Litjens
,
J. Barentsz
,
N. Karssemeijer
,
H. Huisman
Dynamic contrast enhanced MR imaging for the assessment of prostate cancer aggressiveness at 3T
E. Vos
,
G. Litjens
,
T. Kobus
,
T. Hambrock
,
C. Hulsbergen - van de Kaa
,
H. Huisman
,
T. Scheenen
»
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