Matthew Blaschko

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Maxim BermanGoogleAdresse e-mail validée de google.com
Christoph H. LampertInstitute of Science and Technology Austria (ISTA)Adresse e-mail validée de ist.ac.at
Amal Rannen TrikiResearch Scientist at Google DeepMindAdresse e-mail validée de google.com
Esa RahtuProfessor, Tampere University, FinlandAdresse e-mail validée de tuni.fi
Tinne TuytelaarsKU Leuven - PSI, BelgiumAdresse e-mail validée de esat.kuleuven.be
Juho KannalaAssociate Professor, Aalto University & University of Oulu, FinlandAdresse e-mail validée de aalto.fi
Arthur GrettonGatsby Computational Neuroscience Unit and Google DeepmindAdresse e-mail validée de gatsby.ucl.ac.uk
Andrea VedaldiUniversity of OxfordAdresse e-mail validée de robots.ox.ac.uk
José Ignacio OrlandoAssociate Researcher, CONICET Pladema-UNICEN / Director of AI Labs Arionkoder Global LLCAdresse e-mail validée de conicet.gov.ar
Eugene BelilovskyAssistant Professor, Concordia University and Mila Quebec AI InstituteAdresse e-mail validée de concordia.ca
Subhransu MajiAssociate Professor, University of Massachusetts, AmherstAdresse e-mail validée de cs.umass.edu
Joao Barbosa BredaMD, PhD KU Leuven/University of PortoAdresse e-mail validée de kuleuven.be
Patrick De BoeverHasselt University, Centre for Environmental SciencesAdresse e-mail validée de uhasselt.be
Bart ElenPrincipal deep learning research scientist at VITO HealthAdresse e-mail validée de vito.be
Ruben HemelingsPhD, Singapore Eye Research InstituteAdresse e-mail validée de kuleuven.be
Jeroen BertelsKU LeuvenAdresse e-mail validée de kuleuven.be
Elena ProkofyevaScientific Institute of Public Health/ FAMHPAdresse e-mail validée de biomed-engineering.de
Junyi ZhuSamsung Research UK (SRUK)Adresse e-mail validée de samsung.com
Aleksei TiulpinUniversity of OuluAdresse e-mail validée de oulu.fi
Tom EelbodePost-doc researcher, KU LeuvenAdresse e-mail validée de kuleuven.be

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Matthew Blaschko

KU Leuven

Adresse e-mail validée de esat.kuleuven.be - Page d'accueil

Machine Learning Computer Vision Medical Image Analysis


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Fine-grained visual classification of aircraft S Maji, E Rahtu, J Kannala, M Blaschko, A Vedaldi arXiv preprint arXiv:1306.5151, 2013	2517	2013
The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks M Berman, A Rannen Triki, MB Blaschko Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018	1119	2018
Beyond sliding windows: Object localization by efficient subwindow search CH Lampert, MB Blaschko, T Hofmann 2008 IEEE conference on computer vision and pattern recognition, 1-8, 2008	1050	2008
A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images JI Orlando, E Prokofyeva, MB Blaschko IEEE transactions on Biomedical Engineering 64 (1), 16-27, 2016	555	2016
Efficient subwindow search: A branch and bound framework for object localization CH Lampert, MB Blaschko, T Hofmann IEEE transactions on pattern analysis and machine intelligence 31 (12), 2129 …, 2009	511	2009
Learning to localize objects with structured output regression MB Blaschko, CH Lampert Computer Vision–ECCV 2008: 10th European Conference on Computer Vision …, 2008	477	2008
Encoder based lifelong learning A Rannen, R Aljundi, MB Blaschko, T Tuytelaars Proceedings of the IEEE international conference on computer vision, 1320-1328, 2017	428	2017
Optimizing the dice score and jaccard index for medical image segmentation: Theory and practice J Bertels, T Eelbode, M Berman, D Vandermeulen, F Maes, R Bisschops, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019	417	2019
Optimization for medical image segmentation: theory and practice when evaluating with dice score or jaccard index T Eelbode, J Bertels, M Berman, D Vandermeulen, F Maes, R Bisschops, ... IEEE transactions on medical imaging 39 (11), 3679-3690, 2020	361	2020
An ensemble deep learning based approach for red lesion detection in fundus images JI Orlando, E Prokofyeva, M Del Fresno, MB Blaschko Computer methods and programs in biomedicine 153, 115-127, 2018	340	2018
Metrics reloaded: recommendations for image analysis validation L Maier-Hein, A Reinke, P Godau, MD Tizabi, F Buettner, E Christodoulou, ... Nature methods, 1-18, 2024	317*	2024
Combining local and global image features for object class recognition DA Lisin, MA Mattar, MB Blaschko, EG Learned-Miller, MC Benfield 2005 IEEE computer society conference on computer vision and pattern …, 2005	317	2005
Correlational spectral clustering MB Blaschko, CH Lampert 2008 IEEE conference on computer vision and pattern recognition, 1-8, 2008	276	2008
Unsupervised object discovery: A comparison T Tuytelaars, CH Lampert, MB Blaschko, W Buntine International journal of computer vision 88, 284-302, 2010	258	2010
Common limitations of image processing metrics: A picture story A Reinke, MD Tizabi, CH Sudre, M Eisenmann, T Rädsch, M Baumgartner, ... arXiv preprint arXiv:2104.05642, 2021	236*	2021
Learning a category independent object detection cascade E Rahtu, J Kannala, M Blaschko 2011 international conference on Computer Vision, 1052-1059, 2011	220	2011
Convolutional neural network transfer for automated glaucoma identification JI Orlando, E Prokofyeva, M del Fresno, MB Blaschko 12th international symposium on medical information processing and analysis …, 2017	167	2017
Learning fully-connected CRFs for blood vessel segmentation in retinal images JI Orlando, M Blaschko Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: 17th …, 2014	166	2014
R-gap: Recursive gradient attack on privacy J Zhu, M Blaschko International Conference on Learning Representations (ICLR), 2021	155	2021
B-test: A non-parametric, low variance kernel two-sample test W Zaremba, A Gretton, M Blaschko Advances in neural information processing systems 26, 2013	147	2013

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