Nico Karssemeijer
Nico Karssemeijer
Professor of Computer Aided Diagnosis, Radboud University Nijmegen
Verified email at - Homepage
Cited by
Cited by
Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
BE Bejnordi, M Veta, PJ Van Diest, B Van Ginneken, N Karssemeijer, ...
Jama 318 (22), 2199-2210, 2017
Large scale deep learning for computer aided detection of mammographic lesions
T Kooi, G Litjens, B Van Ginneken, A Gubern-Mérida, CI Sánchez, ...
Medical image analysis 35, 303-312, 2017
Detection of stellate distortions in mammograms
N Karssemeijer, GM te Brake
IEEE Transactions on Medical Imaging 15 (5), 611-619, 1996
Automated classification of parenchymal patterns in mammograms
N Karssemeijer
Physics in medicine & biology 43 (2), 365, 1998
Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring
M Kallenberg, K Petersen, M Nielsen, AY Ng, P Diao, C Igel, CM Vachon, ...
IEEE transactions on medical imaging 35 (5), 1322-1331, 2016
Computer-aided detection of prostate cancer in MRI
G Litjens, O Debats, J Barentsz, N Karssemeijer, H Huisman
IEEE transactions on medical imaging 33 (5), 1083-1092, 2014
Volumetric breast density estimation from full-field digital mammograms
S van Engeland, PR Snoeren, H Huisman, C Boetes, N Karssemeijer
IEEE transactions on medical imaging 25 (3), 273-282, 2006
Robust Breast Composition Measurement - VolparaTM
R Highnam, M Brady, MJ Yaffe, N Karssemeijer, J Harvey
International workshop on digital mammography, 342-349, 2010
Staging urinary bladder cancer after transurethral biopsy: value of fast dynamic contrast-enhanced MR imaging.
JO Barentsz, GJ Jager, PB Van Vierzen, JA Witjes, SP Strijk, H Peters, ...
Radiology 201 (1), 185-193, 1996
A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography
S Timp, N Karssemeijer
Medical physics 31 (5), 958-971, 2004
Adaptive noise equalization and recognition of microcalcification clusters in mammograms
N Karssemeijer
International Journal of Pattern Recognition and Artificial Intelligence 7 …, 1993
Transfer learning for domain adaptation in mri: Application in brain lesion segmentation
M Ghafoorian, A Mehrtash, T Kapur, N Karssemeijer, E Marchiori, ...
International conference on medical image computing and computer-assisted …, 2017
Supplemental MRI screening for women with extremely dense breast tissue
MF Bakker, SV de Lange, RM Pijnappel, RM Mann, PHM Peeters, ...
New England Journal of Medicine 381 (22), 2091-2102, 2019
Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer
ML Giger, N Karssemeijer, JA Schnabel
Annual review of biomedical engineering 15, 327-357, 2013
Computer-aided diagnosis
ML Giger, K Suzuki
Biomedical information technology, 359-XXII, 2008
Location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities
M Ghafoorian, N Karssemeijer, T Heskes, IWM van Uden, CI Sanchez, ...
Scientific Reports 7 (1), 1-12, 2017
Stain specific standardization of whole-slide histopathological images
BE Bejnordi, G Litjens, N Timofeeva, I Otte-Höller, A Homeyer, ...
IEEE transactions on medical imaging 35 (2), 404-415, 2015
Single and multiscale detection of masses in digital mammograms
GM Te Brake, N Karssemeijer
IEEE transactions on medical imaging 18 (7), 628-639, 1999
Computer-aided detection versus independent double reading of masses on mammograms
N Karssemeijer, JDM Otten, ALM Verbeek, JH Groenewoud, ...
Radiology 227 (1), 192-200, 2003
Computer-aided diagnosis in medical imaging.
ML Giger, N Karssemeijer, SG Armato
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