Nico Karssemeijer

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Ritse MannBreast and Interventional Radiologist, RadboudumcVerified email at radboudumc.nl
Albert Gubern-MéridaScreenPoint MedicalVerified email at screenpointmed.com
Bram Platel, PhDDirector of Stichting MS in Beeld (Foundation for MS patients)Verified email at msinbeeld.nl
Bram van GinnekenProfessor of Medical Image Analysis, Radboud UniversityVerified email at radboudumc.nl
Henkjan HuismanAssociate Professor, Radboud University Medical Centre NijmegenVerified email at radboudumc.nl
Geert LitjensRadboud University Medical CenterVerified email at radboudumc.nl
Michiel G.J. KallenbergLaboratory of Geo-information Science and Remote Sensing, Wageningen UniversityVerified email at wur.nl
Jelle BarentszProfessor of Radiology, Chairman Radboud Prostate MRI Expert CenterVerified email at radboudumc.nl
Wouter J.H. VeldkampMedical Physicist, Leiden University Medical CenterVerified email at lumc.nl
Suzan VreemannPhD student Technical MedicineVerified email at radboudumc.nl
Jeroen van der LaakRadboud University Medical CenterVerified email at radboudumc.nl
Mireille BroedersAssociate Professor, Radboud universitair medisch centrum, NijmegenVerified email at radboudumc.nl
Tao TanSenior Scientist GEVerified email at ge.com
Thijs KooiLunit Inc.Verified email at thijskooi.com
Babak Ehteshami BejnordiResearch Scientist, Qualcomm AI Research, Qualcomm Technologies, Inc., NetherlandsVerified email at qti.qualcomm.com
Ioannis SechopoulosRadboud University Medical CenterVerified email at radboudumc.nl
Mads NielsenProfessor of Computer Science, University of CopenhagenVerified email at diku.edu
Marc LobbesAssistant professor RadiologyVerified email at mumc.nl
Wouter B. VeldhuisUtrecht University, UMC UtrechtVerified email at umcutrecht.nl
Robert Martí MarlyAssociate Professor. University of GironaVerified email at udg.edu

Nico Karssemeijer

Professor of Computer Aided Diagnosis, Radboud University Nijmegen

Verified email at radboudumc.nl - Homepage

Computer Aided Diagnosis Medical Image Analysis Pattern Recognition


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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	1761	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	753	2017
Detection of stellate distortions in mammograms N Karssemeijer, GM te Brake IEEE Transactions on Medical Imaging 15 (5), 611-619, 1996	390	1996
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	381	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	378	2014
Automated classification of parenchymal patterns in mammograms N Karssemeijer Physics in medicine & biology 43 (2), 365, 1998	364	1998
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	275	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	254	1996
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	252	2019
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	246	2017
Robust Breast Composition Measurement - Volpara^TM R Highnam, SM Brady, MJ Yaffe, N Karssemeijer, J Harvey International workshop on digital mammography, 342-349, 2010	245	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	233	2004
Adaptive noise equalization and recognition of microcalcification clusters in mammograms N Karssemeijer International Journal of Pattern Recognition and Artificial Intelligence 7 …, 1993	217	1993
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	206	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	205	2013
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	204	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	174	1999
An automatic method to discriminate malignant masses from normal tissue in digital mammograms1 GM te Brake, N Karssemeijer, JHCL Hendriks Physics in Medicine & Biology 45 (10), 2843, 2000	169	2000
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	167	2003
Computer-aided diagnosis in medical imaging ML Giger, N Karssemeijer, SGA III IEEE Trans. Med. Imaging 20 (12), 1205-1208, 2001	164	2001

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