Hannes Nickisch

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Michael GrassPhilips Research, Hambug, GermanyVerified email at philips.com
Carl Edward RasmussenProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
Andrew Gordon WilsonNew York UniversityVerified email at cims.nyu.edu
Tobias KnoppProfessor for Biomedical Imaging, UKE and TUHH, HamburgVerified email at knoppweb.de
Matthias SeegerPrincipal Applied Scientist, Amazon, BerlinVerified email at amazon.de
Christoph H. LampertInstitute of Science and Technology Austria (ISTA)Verified email at ist.ac.at
Stefan HarmelingProfessor at Technische Universität DortmundVerified email at tu-dortmund.de
Ivo Matteo BaltruschatMedical Imaging Scientist, Bayer AGVerified email at bayer.com
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified email at tuebingen.mpg.de
Sven PrevrhalPhilips Research HamburgVerified email at philips.com
Michael MorlockProfessor for Biomechanics TUHHVerified email at tuhh.de
Daniel B. NeillNew York UniversityVerified email at nyu.edu
Moti FreimanBiomedical Engineering, Technion - Israel Institute of TechnologyVerified email at technion.ac.il
Gerhard AdamRadiologe, UKE, HamburgVerified email at uke.de
Seth Flaxmanassociate professor at OxfordVerified email at cs.ox.ac.uk
David DuvenaudAssociate Professor, University of TorontoVerified email at cs.toronto.edu
Christoph RhemannResearch Scientist, GoogleVerified email at google.com
Carsten RotherProfessor Uni Heidelberg / GermanyVerified email at iwr.uni-heidelberg.de
Alex SmolaBoson AIVerified email at smola.org
Jens von BergPhilips Research, HamburgVerified email at philips.com

Hannes Nickisch

Philips Research

Verified email at philips.com - Homepage

Medical Image Processing Probabilistic Pattern Recognition Machine Learning Biophysical Simulations Patient-Specific Models


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Learning to detect unseen object classes by between-class attribute transfer CH Lampert, H Nickisch, S Harmeling 2009 IEEE conference on computer vision and pattern recognition, 951-958, 2009	3016	2009
Attribute-based classification for zero-shot visual object categorization CH Lampert, H Nickisch, S Harmeling IEEE transactions on pattern analysis and machine intelligence 36 (3), 453-465, 2013	1941	2013
Gaussian processes for machine learning (GPML) toolbox CE Rasmussen, H Nickisch The Journal of Machine Learning Research 11, 3011-3015, 2010	1266	2010
Kernel interpolation for scalable structured Gaussian processes (KISS-GP) A Wilson, H Nickisch International conference on machine learning, 1775-1784, 2015	634	2015
Approximations for binary Gaussian process classification H Nickisch, CE Rasmussen Journal of Machine Learning Research 9 (Oct), 2035-2078, 2008	505	2008
Comparison of deep learning approaches for multi-label chest X-ray classification IM Baltruschat, H Nickisch, M Grass, T Knopp, A Saalbach Scientific reports 9 (1), 6381, 2019	485	2019
Additive gaussian processes DK Duvenaud, H Nickisch, C Rasmussen Advances in neural information processing systems 24, 2011	437	2011
Optimization of k‐space trajectories for compressed sensing by Bayesian experimental design M Seeger, H Nickisch, R Pohmann, B Schölkopf Magnetic Resonance in Medicine: An Official Journal of the International …, 2010	188	2010
Compressed sensing and Bayesian experimental design MW Seeger, H Nickisch Proceedings of the 25th international conference on Machine learning, 912-919, 2008	148	2008
Thoughts on massively scalable Gaussian processes AG Wilson, C Dann, H Nickisch arXiv preprint arXiv:1511.01870, 2015	125	2015
Fast Kronecker inference in Gaussian processes with non-Gaussian likelihoods S Flaxman, A Wilson, D Neill, H Nickisch, A Smola International conference on machine learning, 607-616, 2015	114	2015
Blind retrospective motion correction of MR images A Loktyushin, H Nickisch, R Pohmann, B Schölkopf Magnetic resonance in medicine 70 (6), 1608-1618, 2013	113	2013
Scalable log determinants for Gaussian process kernel learning K Dong, D Eriksson, H Nickisch, D Bindel, AG Wilson Advances in Neural Information Processing Systems 30, 2017	103	2017
Large scale Bayesian inference and experimental design for sparse linear models MW Seeger, H Nickisch SIAM Journal on Imaging Sciences 4 (1), 166-199, 2011	91	2011
Automatic classification of auroral images from the Oslo Auroral THEMIS (OATH) data set using machine learning LBN Clausen, H Nickisch Journal of Geophysical Research: Space Physics 123 (7), 5640-5647, 2018	77	2018
Method of determining the blood flow through coronary arteries M Grass, H Schmitt, H Nickisch US Patent 9,867,584, 2018	62	2018
Smart chest X-ray worklist prioritization using artificial intelligence: a clinical workflow simulation I Baltruschat, L Steinmeister, H Nickisch, A Saalbach, M Grass, G Adam, ... European radiology 31, 3837-3845, 2021	57	2021
Blind multirigid retrospective motion correction of MR images A Loktyushin, H Nickisch, R Pohmann, B Schölkopf Magnetic resonance in medicine 73 (4), 1457-1468, 2015	51	2015
Motion artifact recognition and quantification in coronary CT angiography using convolutional neural networks T Lossau, H Nickisch, T Wissel, R Bippus, H Schmitt, M Morlock, M Grass Medical image analysis 52, 68-79, 2019	48	2019
Motion estimation and correction in cardiac CT angiography images using convolutional neural networks T Lossau, H Nickisch, T Wissel, R Bippus, H Schmitt, M Morlock, M Grass Computerized Medical Imaging and Graphics 76, 101640, 2019	44	2019

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