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
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
Robust Breast Composition Measurement - VolparaTM
R Highnam, M Brady, MJ Yaffe, N Karssemeijer, J Harvey
International workshop on digital mammography, 342-349, 2010
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
Breast cancer screening with adjunctive ultrasound mammography
S Wang, D Chin, F Rao
US Patent 7,556,602, 2009
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
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
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
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
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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