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Hannes Nickisch
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2020 – today
- 2021
- [c29]Zohaib Salahuddin
, Matthias Lenga, Hannes Nickisch:
Multi-Resolution 3D Convolutional Neural Networks for Automatic Coronary Centerline Extraction in Cardiac CT Angiography Scans. ISBI 2021: 91-95 - [c28]Nikolas Schnellbächer, Haissam Ragab, Hannes Nickisch
Machine-learning-based clinical plaque detection using a synthetic plaque lesion model for coronary CTA. Computer-Aided Diagnosis 2021 - [c27]Tobias Wissel, Katharina A. Riedl
Delineation of coronary stents in intravascular ultrasound pullbacks. Image-Guided Procedures 2021 - 2020
- [j11]Tanja Lossau, Hannes Nickisch
Learning metal artifact reduction in cardiac CT images with moving pacemakers. Medical Image Anal. 61: 101655 (2020) - [i14]Ivo M. Baltruschat, Leonhard Steinmeister, Hannes Nickisch, Axel Saalbach, Michael Grass, Gerhard Adam, Harald Ittrich, Tobias Knopp:
Intelligent Chest X-ray Worklist Prioritization by CNNs: A Clinical Workflow Simulation. CoRR abs/2001.08625 (2020) - [i13]Zohaib Salahuddin, Matthias Lenga, Hannes Nickisch:
Multi-Resolution 3D Convolutional Neural Networks for Automatic Coronary Centerline Extraction in Cardiac CT Angiography Scans. CoRR abs/2010.00925 (2020)
2010 – 2019
- 2019
- [j10]Tanja Lossau, Hannes Nickisch
Motion estimation and correction in cardiac CT angiography images using convolutional neural networks. Comput. Medical Imaging Graph. 76 (2019) - [j9]William Herlands, Daniel B. Neill, Hannes Nickisch, Andrew Gordon Wilson:
Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction. J. Mach. Learn. Res. 20: 99:1-99:51 (2019) - [j8]Tanja Lossau, Hannes Nickisch
Motion artifact recognition and quantification in coronary CT angiography using convolutional neural networks. Medical Image Anal. 52: 68-79 (2019) - [c26]Ivo M. Baltruschat, Leonhard Steinmeister, Harald Ittrich, Gerhard Adam, Hannes Nickisch, Axel Saalbach, Jens von Berg, Michael Grass, Tobias Knopp:
Abstract: Does Bone Suppression and Lung Detection Improve Chest Disease Classification? Bildverarbeitung für die Medizin 2019: 184 - [c25]Ivo M. Baltruschat, Leonhard Steinmeister, Harald Ittrich, Gerhard Adam, Hannes Nickisch
When Does Bone Suppression And Lung Field Segmentation Improve Chest X-Ray Disease Classification? ISBI 2019: 1362-1366 - [c24]Maximilian Blendowski
How to Learn from Unlabeled Volume Data: Self-supervised 3D Context Feature Learning. MICCAI (6) 2019: 649-657 - [c23]Tanja Lossau, Hannes Nickisch, Tobias Wissel, Samer Hakmi, Clemens Spink, Michael M. Morlock, Michael Grass:
Dynamic Pacemaker Artifact Removal (DyPAR) from CT Data using CNNs. MIDL 2019: 347-357 - [i12]Moti Freiman, Hannes Nickisch, Sven Prevrhal, Holger Schmitt, Mani Vembar, Pál Maurovich-Horvat, Patrick Donnelly, Liran Goshen:
Improving CCTA based lesions' hemodynamic significance assessment by accounting for partial volume modeling in automatic coronary lumen segmentation. CoRR abs/1906.09763 (2019) - [i11]Moti Freiman, Hannes Nickisch, Holger Schmitt, Pál Maurovich-Horvat, Patrick Donnelly, Mani Vembar, Liran Goshen:
Learning a sparse database for patch-based medical image segmentation. CoRR abs/1906.10338 (2019) - 2018
- [c22]Hannes Nickisch, Arno Solin, Alexander Grigorevskiy:
State Space Gaussian Processes with Non-Gaussian Likelihood. ICML 2018: 3786-3795 - [c21]Evelin Hristova, Heinrich Schulz, Tom Brosch, Mattias P. Heinrich
Nearest neighbor 3D segmentation with context features. Image Processing 2018: 105740M - [c20]Ivo M. Baltruschat, Axel Saalbach, Mattias P. Heinrich
Orientation regression in hand radiographs: a transfer learning approach. Image Processing 2018: 105741W - [c19]Tanja Elss, Hannes Nickisch, Tobias Wissel, Holger Schmitt, Mani Vembar, Michael M. Morlock, Michael Grass:
Deep-learning-based CT motion artifact recognition in coronary arteries. Image Processing 2018: 1057416 - [i10]Ivo M. Baltruschat, Hannes Nickisch, Michael Grass, Tobias Knopp, Axel Saalbach:
Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification. CoRR abs/1803.02315 (2018) - [i9]Ivo M. Baltruschat, Leonhard Steinmeister, Harald Ittrich, Gerhard Adam, Hannes Nickisch, Axel Saalbach, Jens von Berg, Michael Grass, Tobias Knopp:
When does Bone Suppression and Lung Field Segmentation Improve Chest X-Ray Disease Classification? CoRR abs/1810.07500 (2018) - [i8]William Herlands, Daniel B. Neill, Hannes Nickisch, Andrew Gordon Wilson:
Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction. CoRR abs/1810.11861 (2018) - 2017
- [c18]Moti Freiman
Learning a Sparse Database for Patch-Based Medical Image Segmentation. Patch-MI@MICCAI 2017: 47-54 - [c17]Kun Dong, David Eriksson, Hannes Nickisch, David Bindel, Andrew Gordon Wilson:
Scalable Log Determinants for Gaussian Process Kernel Learning. NIPS 2017: 6327-6337 - [i7]Kun Dong, David Eriksson, Hannes Nickisch, David Bindel, Andrew Gordon Wilson:
Scalable Log Determinants for Gaussian Process Kernel Learning. CoRR abs/1711.03481 (2017) - 2016
- [c16]William Herlands, Andrew Gordon Wilson, Hannes Nickisch, Seth R. Flaxman, Daniel B. Neill, Wilbert Van Panhuis, Eric P. Xing:
Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces. AISTATS 2016: 1013-1021 - [c15]Thomas Blaffert, Cristian Lorenz, Hannes Nickisch, Jochen Peters, Jürgen Weese:
SVM-based failure detection of GHT localizations. Image Processing 2016: 97841J - [c14]Moti Freiman
Automatic coronary lumen segmentation with partial volume modeling improves lesions' hemodynamic significance assessment. Image Processing 2016: 978403 - 2015
- [c13]Seth R. Flaxman, Andrew Gordon Wilson, Daniel B. Neill, Hannes Nickisch, Alexander J. Smola:
Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods. ICML 2015: 607-616 - [c12]Andrew Gordon Wilson, Hannes Nickisch:
Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP). ICML 2015: 1775-1784 - [c11]Hannes Nickisch, Yechiel Lamash, Sven Prevrhal, Moti Freiman
Learning Patient-Specific Lumped Models for Interactive Coronary Blood Flow Simulations. MICCAI (2) 2015: 433-441 - [i6]Andrew Gordon Wilson, Hannes Nickisch:
Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP). CoRR abs/1503.01057 (2015) - [i5]Andrew Gordon Wilson, Christoph Dann, Hannes Nickisch:
Thoughts on Massively Scalable Gaussian Processes. CoRR abs/1511.01870 (2015) - [i4]Vlado Menkovski, Zharko Aleksovski, Axel Saalbach, Hannes Nickisch:
Can Pretrained Neural Networks Detect Anatomy? CoRR abs/1512.05986 (2015) - 2014
- [j7]Christoph H. Lampert, Hannes Nickisch
Attribute-Based Classification for Zero-Shot Visual Object Categorization. IEEE Trans. Pattern Anal. Mach. Intell. 36(3): 453-465 (2014) - 2013
- [j6]Jürgen Weese, Alexandra Groth, Hannes Nickisch
Generating anatomical models of the heart and the aorta from medical images for personalized physiological simulations. Medical Biol. Eng. Comput. 51(11): 1209-1219 (2013) - 2012
- [j5]Pushmeet Kohli, Hannes Nickisch, Carsten Rother, Christoph Rhemann:
User-Centric Learning and Evaluation of Interactive Segmentation Systems. Int. J. Comput. Vis. 100(3): 261-274 (2012) - [j4]Hannes Nickisch:
glm-ie: Generalised Linear Models Inference & Estimation Toolbox. J. Mach. Learn. Res. 13: 1699-1703 (2012) - [j3]Wendelin Böhmer
Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis. Mach. Learn. 89(1-2): 67-86 (2012) - [c10]Hannes Nickisch, Hans Barschdorf, Frank M. Weber, Martin W. Krueger, Olaf Dössel
From Image to Personalized Cardiac Simulation: Encoding Anatomical Structures into a Model-Based Segmentation Framework. STACOM 2012: 278-287 - 2011
- [j2]Matthias W. Seeger, Hannes Nickisch:
Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models. SIAM J. Imaging Sci. 4(1): 166-199 (2011) - [c9]David Duvenaud, Hannes Nickisch, Carl Edward Rasmussen:
Additive Gaussian Processes. NIPS 2011: 226-234 - [c8]Wendelin Böhmer
Regularized Sparse Kernel Slow Feature Analysis. ECML/PKDD (1) 2011: 235-248 - [c7]Matthias W. Seeger, Hannes Nickisch:
Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference. AISTATS 2011: 652-660 - [i3]David Duvenaud, Hannes Nickisch, Carl Edward Rasmussen:
Additive Gaussian Processes. CoRR abs/1112.4394 (2011) - 2010
- [b1]Hannes Nickisch:
Bayesian inference and experimental design for large generalised linear models. Berlin Institute of Technology, 2010 - [j1]Carl Edward Rasmussen, Hannes Nickisch:
Gaussian Processes for Machine Learning (GPML) Toolbox. J. Mach. Learn. Res. 11: 3011-3015 (2010) - [c6]Hannes Nickisch, Carl Edward Rasmussen:
Gaussian Mixture Modeling with Gaussian Process Latent Variable Models. DAGM-Symposium 2010: 272-282 - [c5]Hannes Nickisch, Carsten Rother, Pushmeet Kohli, Christoph Rhemann:
Learning an interactive segmentation system. ICVGIP 2010: 274-281
2000 – 2009
- 2009
- [c4]Christoph H. Lampert, Hannes Nickisch, Stefan Harmeling:
Learning to detect unseen object classes by between-class attribute transfer. CVPR 2009: 951-958 - [c3]Hannes Nickisch, Matthias W. Seeger:
Convex variational Bayesian inference for large scale generalized linear models. ICML 2009: 761-768 - [i2]Matthias W. Seeger, Hannes Nickisch:
Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models. Sampling-based Optimization in the Presence of Uncertainty 2009 - [i1]Hannes Nickisch, Pushmeet Kohli, Carsten Rother:
Learning an Interactive Segmentation System. CoRR abs/0912.2492 (2009) - 2008
- [c2]Matthias W. Seeger, Hannes Nickisch:
Compressed sensing and Bayesian experimental design. ICML 2008: 912-919 - [c1]Matthias W. Seeger, Hannes Nickisch, Rolf Pohmann, Bernhard Schölkopf:
Bayesian Experimental Design of Magnetic Resonance Imaging Sequences. NIPS 2008: 1441-1448
Coauthor Index
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