Thomas de Bel
Thomas de Bel
PhD Candidate, Radboud University Medical Center, Computational Pathology Group
Verified email at radboudumc.nl
Title
Cited by
Cited by
Year
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
3392017
Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study
W Bulten, H Pinckaers, H van Boven, R Vink, T de Bel, B van Ginneken, ...
The Lancet Oncology 21 (2), 233-241, 2020
64*2020
Deep learning–based histopathologic assessment of kidney tissue
M Hermsen, T de Bel, M Den Boer, EJ Steenbergen, J Kers, S Florquin, ...
Journal of the American Society of Nephrology 30 (10), 1968-1979, 2019
422019
Stain-Transforming Cycle-Consistent Generative Adversarial Networks for Improved Segmentation of Renal Histopathology.
T de Bel, M Hermsen, J Kers, J van der Laak, GJS Litjens
MIDL, 151-163, 2019
172019
Automatic segmentation of histopathological slides of renal tissue using deep learning
T de Bel, M Hermsen, B Smeets, L Hilbrands, J van der Laak, G Litjens
Medical Imaging 2018: Digital Pathology 10581, 1058112, 2018
172018
Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer
Z Swiderska-Chadaj, T de Bel, L Blanchet, A Baidoshvili, D Vossen, ...
Scientific RepoRtS 10 (1), 1-14, 2020
22020
Renal phospholipidosis and impaired magnesium handling in high‐fat‐diet–fed mice
S Kurstjens, B Smeets, C Overmars-Bos, HB Dijkman, DJW den Braanker, ...
The FASEB Journal 33 (6), 7192-7201, 2019
22019
Structure Instance Segmentation in Renal Tissue: A Case Study on Tubular Immune Cell Detection
T de Bel, M Hermsen, G Litjens, J van der Laak
Computational Pathology and Ophthalmic Medical Image Analysis, 112-119, 2018
12018
Discrimination of benign breast disease from normal lobules using an automated computational pathology algorithm
AC Degnim, T de Bel, ME Sherman, DC Radisky, SJ Winham, TL Hoskin, ...
Cancer Research 80 (16 Supplement), 2113-2113, 2020
2020
Development of an Automated Computational Pathology Algorithm Using Maching Learning to Quantify Levels of Breast Lobular Involution
AC Degnim, T de Bel, ME Sherman, DC Radisky, SJ Winham, TL Hoskin, ...
ANNALS OF SURGICAL ONCOLOGY 27 (SUPPL 1), S107-S108, 2020
2020
DEEP-LEARNING BASED HISTOPATHOLOGICAL ASSESSMENT OF RENAL TISSUE AS AN AID FOR KIDNEY TRANSPLANT RESEARCH
M Hermsen, T de Bel, M den Boer, E Steenbergen, J Kers, S Florquin, ...
TRANSPLANT INTERNATIONAL 32, 133-133, 2019
2019
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