Ullrich Köthe
Ullrich Köthe
Adjunct Professor of Computer Science, University of Heidelberg
Zweryfikowany adres z iwr.uni-heidelberg.de
Cytowane przez
Cytowane przez
Ilastik: interactive machine learning for (bio) image analysis
S Berg, D Kutra, T Kroeger, CN Straehle, BX Kausler, C Haubold, ...
Nature methods 16 (12), 1226-1232, 2019
Ilastik: Interactive learning and segmentation toolkit
C Sommer, C Straehle, U Koethe, FA Hamprecht
2011 IEEE international symposium on biomedical imaging: From nano to macro …, 2011
Analyzing inverse problems with invertible neural networks
L Ardizzone, J Kruse, S Wirkert, D Rahner, EW Pellegrini, RS Klessen, ...
arXiv preprint arXiv:1808.04730, 2018
On oblique random forests
BH Menze, BM Kelm, DN Splitthoff, U Koethe, FA Hamprecht
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2011
Guided image generation with conditional invertible neural networks
L Ardizzone, C Lüth, J Kruse, C Rother, U Köthe
arXiv preprint arXiv:1907.02392, 2019
Learning to count with regression forest and structured labels
L Fiaschi, U Köthe, R Nair, FA Hamprecht
Proceedings of the 21st International Conference on Pattern Recognition …, 2012
Edge and junction detection with an improved structure tensor
U Köthe
Joint Pattern Recognition Symposium, 25-32, 2003
Automated detection and segmentation of synaptic contacts in nearly isotropic serial electron microscopy images
A Kreshuk, CN Straehle, C Sommer, U Koethe, M Cantoni, G Knott, ...
PloS one 6 (10), e24899, 2011
Multicut brings automated neurite segmentation closer to human performance
T Beier, C Pape, N Rahaman, T Prange, S Berg, DD Bock, A Cardona, ...
Nature methods 14 (2), 101-102, 2017
Theoretical and experimental error analysis of continuous-wave time-of-flight range cameras
M Frank, M Plaue, H Rapp, U Köthe, B Jähne, FA Hamprecht
Optical Engineering 48 (1), 013602-013602-16, 2009
Segmentation of SBFSEM volume data of neural tissue by hierarchical classification
B Andres, U Köthe, M Helmstaedter, W Denk, FA Hamprecht
Pattern Recognition: 30th DAGM Symposium Munich, Germany, June 10-13, 2008 …, 2008
BayesFlow: Learning complex stochastic models with invertible neural networks
ST Radev, UK Mertens, A Voss, L Ardizzone, U Köthe
IEEE transactions on neural networks and learning systems 33 (4), 1452-1466, 2020
Probabilistic image segmentation with closedness constraints
B Andres, JH Kappes, T Beier, U Köthe, FA Hamprecht
2011 International Conference on Computer Vision, 2611-2618, 2011
Globally optimal closed-surface segmentation for connectomics
B Andres, T Kroeger, KL Briggman, W Denk, N Korogod, G Knott, ...
Computer Vision–ECCV 2012: 12th European Conference on Computer Vision …, 2012
Disentanglement by nonlinear ica with general incompressible-flow networks (gin)
P Sorrenson, C Rother, U Köthe
arXiv preprint arXiv:2001.04872, 2020
Can virtual contrast enhancement in brain MRI replace gadolinium?: a feasibility study
J Kleesiek, JN Morshuis, F Isensee, K Deike-Hofmann, D Paech, ...
Investigative radiology 54 (10), 653-660, 2019
Graphical model for joint segmentation and tracking of multiple dividing cells
M Schiegg, P Hanslovsky, C Haubold, U Koethe, L Hufnagel, ...
Bioinformatics 31 (6), 948-956, 2015
Toward digital staining using imaging mass spectrometry and random forests
M Hanselmann, U Kothe, M Kirchner, BY Renard, ER Amstalden, ...
Journal of proteome research 8 (7), 3558-3567, 2009
DALSA: Domain adaptation for supervised learning from sparsely annotated MR images
M Goetz, C Weber, F Binczyk, J Polanska, R Tarnawski, ...
IEEE transactions on medical imaging 35 (1), 184-196, 2015
Integrated edge and junction detection with the boundary tensor
U Koethe
Proceedings Ninth IEEE International Conference on Computer Vision 1, 424-431, 2003
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