Bram van Ginneken
Bram van Ginneken
Professor of Medical Image Analysis, Radboud University
Verified email at - Homepage
Cited by
Cited by
A survey on deep learning in medical image analysis
G Litjens, T Kooi, BE Bejnordi, AAA Setio, F Ciompi, M Ghafoorian, ...
Medical image analysis 42, 60-88, 2017
Ridge-based vessel segmentation in color images of the retina
J Staal, MD Abràmoff, M Niemeijer, MA Viergever, B Van Ginneken
IEEE transactions on medical imaging 23 (4), 501-509, 2004
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
Reflectance and texture of real-world surfaces
KJ Dana, B Van Ginneken, SK Nayar, JJ Koenderink
ACM Transactions On Graphics (TOG) 18 (1), 1-34, 1999
Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique
H Greenspan, B Van Ginneken, RM Summers
IEEE transactions on medical imaging 35 (5), 1153-1159, 2016
Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks
AAA Setio, F Ciompi, G Litjens, P Gerke, C Jacobs, SJ Van Riel, ...
IEEE transactions on medical imaging 35 (5), 1160-1169, 2016
Comparison and evaluation of methods for liver segmentation from CT datasets
T Heimann, B Van Ginneken, MA Styner, Y Arzhaeva, V Aurich, C Bauer, ...
IEEE transactions on medical imaging 28 (8), 1251-1265, 2009
Comparative study of retinal vessel segmentation methods on a new publicly available database
M Niemeijer, J Staal, B Van Ginneken, M Loog, MD Abramoff
Medical imaging 2004: image processing 5370, 648-656, 2004
Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
G Litjens, CI Sánchez, N Timofeeva, M Hermsen, I Nagtegaal, I Kovacs, ...
Scientific reports 6 (1), 1-11, 2016
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
CO-RADS: a categorical CT assessment scheme for patients suspected of having COVID-19—definition and evaluation
M Prokop, W Van Everdingen, T van Rees Vellinga, H Quarles van Ufford, ...
Radiology 296 (2), E97-E104, 2020
Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge
AAA Setio, A Traverso, T De Bel, MSN Berens, C Van Den Bogaard, ...
Medical image analysis 42, 1-13, 2017
Computer analysis of computed tomography scans of the lung: a survey
I Sluimer, A Schilham, M Prokop, B Van Ginneken
IEEE transactions on medical imaging 25 (4), 385-405, 2006
Active shape model segmentation with optimal features
B Van Ginneken, AF Frangi, JJ Staal, BM ter Haar Romeny, MA Viergever
Medical Imaging, IEEE Transactions on 21 (8), 924-933, 2002
Automatic detection of red lesions in digital color fundus photographs
M Niemeijer, B Van Ginneken, J Staal, MSA Suttorp-Schulten, ...
IEEE Transactions on medical imaging 24 (5), 584-592, 2005
Computer-aided diagnosis in chest radiography: a survey
B Van Ginneken, BMTH Romeny, MA Viergever
IEEE Transactions on medical imaging 20 (12), 1228-1241, 2001
A large annotated medical image dataset for the development and evaluation of segmentation algorithms
AL Simpson, M Antonelli, S Bakas, M Bilello, K Farahani, B Van Ginneken, ...
arXiv preprint arXiv:1902.09063, 2019
The liver tumor segmentation benchmark (lits)
P Bilic, P Christ, HB Li, E Vorontsov, A Ben-Cohen, G Kaissis, A Szeskin, ...
Medical Image Analysis 84, 102680, 2023
Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge
G Litjens, R Toth, W Van De Ven, C Hoeks, S Kerkstra, B van Ginneken, ...
Medical image analysis 18 (2), 359-373, 2014
Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database
B Van Ginneken, MB Stegmann, M Loog
Medical image analysis 10 (1), 19-40, 2006
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