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Johan de Mey
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Journal Articles
- 2024
- [j6]Camille Raets
, Chaïmae El Aisati
, Amir L. Rifi
, Mark De Ridder
, Koen Putman
, Johan de Mey
, Alexandra Sermeus
, Kurt Barbé
:
Optimizing Rectal Cancer Patient Care: Dworak TRG Prediction via Bayesian Evolutionary Fourier-Domain Random Subspace Forest. IEEE Trans. Instrum. Meas. 73: 1-10 (2024) - 2022
- [j5]Steven Raeymaeckers
, Yannick De Brucker
, Maurizio Tosi
, Nico Buls
, Johan de Mey
:
Relative Perfusion Differences between Parathyroid Adenomas and the Thyroid on Multiphase 4DCT. Int. J. Biomed. Imaging 2022: 2984789:1-2984789:7 (2022) - 2021
- [j4]Steven Raeymaeckers
, Yannick De Brucker
, Tim Vanderhasselt
, Nico Buls
, Johan de Mey
:
Detection of parathyroid adenomas with multiphase 4DCT: towards a true four-dimensional technique. BMC Medical Imaging 21(1): 64 (2021) - 2018
- [j3]Frederik Vandenbroucke
, Jef Vandemeulebroucke
, Nico Buls
, Ruedi F. Thoeni, Johan de Mey
:
Can tumor coverage evaluated 24 h post-radiofrequency ablation predict local tumor progression of liver metastases? Int. J. Comput. Assist. Radiol. Surg. 13(12): 1981-1989 (2018) - 2012
- [j2]Chris Baeken, Rudi De Raedt
, Peter Van Schuerbeek, Johan de Mey, Axel Bossuyt, Robert Luypaert:
The influence of emotional priming on the neural substrates of memory: A prospective fMRI study using portrait art stimuli. NeuroImage 61(4): 876-883 (2012) - 2011
- [j1]Yanfeng Shang, Rudi Deklerck, Edgard Nyssen, Aneta Markova, Johan de Mey, Xin Yang, Kun Sun:
Vascular Active Contour for Vessel Tree Segmentation. IEEE Trans. Biomed. Eng. 58(4): 1023-1032 (2011)
Conference and Workshop Papers
- 2020
- [c9]Jakub Ceranka, Frédéric Lecouvet, Johan de Mey
, Jef Vandemeulebroucke
:
Computer-aided detection of focal bone metastases from whole-body multi-modal MRI. Medical Imaging: Computer-Aided Diagnosis 2020 - 2019
- [c8]Redona Brahimetaj, Evgenia Papavasileiou, Frederik Temmermans, Bruno Cornelis, Inneke Willekens, Johan de Mey, Bart Jansen:
Computer Aided Detection and Diagnosis System for Breast Cancer Detection Based on High Resolution 3D micro-CT Breast Microcalcifications. BNAIC/BENELEARN 2019 - 2018
- [c7]Evgenia Papavasileiou
, Redona Brahimetaj, Frederik Temmermans
, Bruno Cornelis
, Inneke Willekens
, Johan de Mey
, Bart Jansen
:
Towards a CAD System for Breast Cancer Based on Individual Microcalcifications? HealthCom 2018: 1-5 - [c6]Pieter Thomas Boonen, Nico Buls
, Gert Van Gompel, Yannick De Brucker, Dimitri Aerden
, Johan de Mey
, Jef Vandemeulebroucke
:
Automated Quantification of Blood Flow Velocity from Time-Resolved CT Angiography. CVII-STENT/LABELS@MICCAI 2018: 11-18 - [c5]Jakub Ceranka, Sabrina Verga, Frédéric Lecouvet, Thierry Metens, Johan de Mey, Jef Vandemeulebroucke:
Intensity Standardization of Skeleton in Follow-Up Whole-Body MRI. CSI@MICCAI 2018: 77-89 - 2017
- [c4]Elhassan Abdou, Johan de Mey
, Mark De Ridder
, Jef Vandemeulebroucke
:
Accurate bolus arrival time estimation using piecewise linear model fitting. Medical Imaging: Image Processing 2017: 101332F - 2016
- [c3]Mathias Polfliet, Stefan Klein, Wyke Huizinga, Johan de Mey
, Jef Vandemeulebroucke
:
The pythagorean averages as group images in efficient groupwise registration. ISBI 2016: 1261-1264 - 2011
- [c2]Maxine Tan, Rudi Deklerck, An Tassenoy, Jan Cornelis
, Johan de Mey, Nico Buls:
Histogram analysis of CT scans for patients with post-mastectomy lymphedema. ICIP 2011: 1585-1588 - 2010
- [c1]Steven Raeymaeckers
, Nico Buls, Gert Van Gompel, Cathérine Breucq, Johan de Mey:
Image Quality Phantom in Close Compliance with the ICRU-44 Breast Tissue Substitute Standard and Realistic Background Noise Pattern for Digital Breast Tomosynthesis. Digital Mammography / IWDM 2010: 556-560
Informal and Other Publications
- 2020
- [i2]Sofie Tilborghs, Ine Dirks
, Lucas Fidon, Siri Willems, Tom Eelbode, Jeroen Bertels, Bart Ilsen
, Arne Brys, Adriana Dubbeldam, Nico Buls
, Panagiotis Gonidakis
, Sebastián Amador Sánchez
, Annemiek Snoeckx, Paul M. Parizel, Johan de Mey
, Dirk Vandermeulen, Tom Vercauteren, David Robben, Dirk Smeets, Frederik Maes, Jef Vandemeulebroucke
, Paul Suetens:
Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients. CoRR abs/2007.15546 (2020) - [i1]Abel Díaz Berenguer
, Hichem Sahli
, Boris Joukovsky
, Maryna Kvasnytsia
, Ine Dirks
, Mitchel Alioscha-Pérez
, Nikos Deligiannis
, Panagiotis Gonidakis
, Sebastián Amador Sánchez
, Redona Brahimetaj, Evgenia Papavasileiou
, Jonathan Cheung-Wai Chan, Fei Li, Shangzhen Song, Yixin Yang, Sofie Tilborghs, Siri Willems, Tom Eelbode, Jeroen Bertels, Dirk Vandermeulen, Frederik Maes, Paul Suetens, Lucas Fidon, Tom Vercauteren, David Robben, Arne Brys, Dirk Smeets, Bart Ilsen
, Nico Buls
, Nina Watté, Johan de Mey
, Annemiek Snoeckx, Paul M. Parizel, Julien Guiot, Louis Deprez, Paul Meunier, Stefaan Gryspeerdt, Kristof De Smet
, Bart Jansen
, Jef Vandemeulebroucke
:
Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging. CoRR abs/2011.11719 (2020)
Coauthor Index
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last updated on 2024-08-14 22:14 CEST by the dblp team
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