H. Nickisch

发表

Approximations for Binary Gaussian Process Classification

C. Rasmussen, H. Nickisch, 2008 .

Scalable Log Determinants for Gaussian Process Kernel Learning

Andrew Gordon Wilson, Hannes Nickisch, David Eriksson, 2017, NIPS.

Learning to detect unseen object classes by between-class attribute transfer

Christoph H. Lampert, Hannes Nickisch, Stefan Harmeling, 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

Gaussian Processes for Machine Learning (GPML) Toolbox

Carl E. Rasmussen, Hannes Nickisch, C. Rasmussen, 2010, J. Mach. Learn. Res..

Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)

Andrew Gordon Wilson, Hannes Nickisch, A. G. Wilson, 2015, ICML.

Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces

Andrew Gordon Wilson, Eric P. Xing, Daniel B. Neill, 2015, AISTATS.

Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods

Alexander J. Smola, Andrew Gordon Wilson, Daniel B. Neill, 2015, ICML.

Attribute-Based Classification for Zero-Shot Visual Object Categorization

Christoph H. Lampert, Hannes Nickisch, Stefan Harmeling, 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Additive Gaussian Processes

Carl E. Rasmussen, Hannes Nickisch, David Duvenaud, 2011, NIPS.

Compressed sensing and Bayesian experimental design

Matthias W. Seeger, Hannes Nickisch, H. Nickisch, 2008, ICML '08.

Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification

Michael Grass, Hannes Nickisch, Tobias Knopp, 2018, Scientific Reports.

Regularized Sparse Kernel Slow Feature Analysis

Klaus Obermayer, Wendelin Böhmer, Hannes Nickisch, 2011, ECML/PKDD.

Multiple Kernel Learning: A Unifying Probabilistic Viewpoint

H. Nickisch, M. Seeger, 2011, 1103.0897.

Bayesian Experimental Design for Compressed Sensing

H. Nickisch, M. Seeger, 2008 .

Artifact Recognition and Quantification in Coronary CT Angiography using Convolutional Neural Networks

H. Nickisch, M. Grass, R. Bippus, 2018 .

Multi-Resolution 3D Convolutional Neural Networks for Automatic Coronary Centerline Extraction in Cardiac CT Angiography Scans pdf

Hannes Nickisch, Zohaib Salahuddin, Matthias Lenga, 2020, 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI).

A functionally personalized boundary condition model to improve estimates of fractional flow reserve with CT (CT‐FFR)

H. Nickisch, M. Freiman, M. Vembar, 2018, Medical physics.

Workshop on Machine Learning: Approaches to Representational Learning and Recognition in Vision

Bernhard Schölkopf, Rolf Pohmann, Matthias Seeger, 2008 .

Retrospective blind motion correction of MR images

H. Nickisch, A. Loktyushin, R. Pohmann, 2011 .

Learning to detect unseen object classes by between-class attribute transfer

Christoph H. Lampert, Hannes Nickisch, Stefan Harmeling, 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

Cascaded learning in intravascular ultrasound: coronary stent delineation in manual pullbacks

H. Nickisch, M. Grass, S. Blankenberg, 2022, Journal of medical imaging.

Title: Improving CCTA based lesions’ hemodynamic significance assessment by accounting for partial volume modeling in automatic coronary lumen segmentation Authors:

H. Nickisch, M. Freiman, M. Vembar, 2017 .

Causal and Probabilistic Inference Empirical and Theoretical Analysis of Bayesian Inference in Gaussian Process Models

C. Rasmussen, H. Nickisch, M. Seeger, 2009 .

Motion estimation and correction in cardiac CT angiography images using convolutional neural networks

Michael Grass, Hannes Nickisch, Rolf-Dieter Bippus, 2019, Comput. Medical Imaging Graph..

Bayesian inference and experimental design for large generalised linear models

Hannes Nickisch, H. Nickisch, 2010 .

Optimization of k‐space trajectories for compressed sensing by Bayesian experimental design

Bernhard Schölkopf, Hannes Nickisch, Rolf Pohmann, 2010, Magnetic resonance in medicine.

Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models

Matthias W. Seeger, Hannes Nickisch, H. Nickisch, 2008, Sampling-based Optimization in the Presence of Uncertainty.

Nearest neighbor 3D segmentation with context features

Heinrich Schulz, Hannes Nickisch, Tom Brosch, 2018, Medical Imaging.

Gaussian Mixture Modeling with Gaussian Process Latent Variable Models

Carl E. Rasmussen, Hannes Nickisch, C. Rasmussen, 2010, DAGM-Symposium.

Automatic Classification of Auroral Images From the Oslo Auroral THEMIS (OATH) Data Set Using Machine Learning

Hannes Nickisch, Lasse B. N. Clausen, H. Nickisch, 2018, Journal of Geophysical Research: Space Physics.

Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification

Ivo M. Baltruschat, H. Nickisch, M. Grass, 2018, Scientific Reports.

Blind retrospective motion correction of MR images

Bernhard Schölkopf, Hannes Nickisch, Rolf Pohmann, 2012, Magnetic resonance in medicine.

Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference

Matthias W. Seeger, Hannes Nickisch, H. Nickisch, 2010, AISTATS.

How to Learn from Unlabeled Volume Data: Self-supervised 3D Context Feature Learning

Hannes Nickisch, Mattias P. Heinrich, Maximilian Blendowski, 2019, MICCAI.

Motion artifact recognition and quantification in coronary CT angiography using convolutional neural networks

Michael Grass, Hannes Nickisch, Tobias Wissel, 2019, Medical Image Anal..

State Space Gaussian Processes with Non-Gaussian Likelihood

Arno Solin, Hannes Nickisch, Alexander Grigorevskiy, 2018, ICML.

Transfer Learning Aurora Image Classification and Magnetic Disturbance Evaluation

Hannes Nickisch, Lasse Boy Novock Clausen, Pascal Sado, 2021, Journal of geophysical research. Space physics.

Thoughts on Massively Scalable Gaussian Processes pdf

Andrew Gordon Wilson, Hannes Nickisch, Christoph Dann, 2015, ArXiv.

When Does Bone Suppression And Lung Field Segmentation Improve Chest X-Ray Disease Classification?

Michael Grass, Jens von Berg, Hannes Nickisch, 2018, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

Smart chest X-ray worklist prioritization using artificial intelligence: a clinical workflow simulation

Hannes Nickisch, Axel Saalbach, Ivo M. Baltruschat, 2020, European Radiology.

Generating anatomical models of the heart and the aorta from medical images for personalized physiological simulations

Alejandro F Frangi, H. Nickisch, J. Coatrieux, 2013, Medical and Biological Engineering and Computing.

From Image to Personalized Cardiac Simulation: Encoding Anatomical Structures into a Model-Based Segmentation Framework

Jürgen Weese, Olaf Dössel, Hannes Nickisch, 2012, STACOM.

Substorm Onset Prediction Using Machine Learning Classified Auroral Images

H. Nickisch, L. Clausen, W. Miloch, 2023, Space Weather.

Dynamic Pacemaker Artifact Removal (DyPAR) from CT Data using CNNs

Michael Grass, Hannes Nickisch, Michael M. Morlock, 2018, MIDL.

Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction pdf

Andrew Gordon Wilson, Daniel B. Neill, Hannes Nickisch, 2018, J. Mach. Learn. Res..

Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models

Matthias W. Seeger, Hannes Nickisch, H. Nickisch, 2011, SIAM J. Imaging Sci..

Deep-learning-based CT motion artifact recognition in coronary arteries

Michael Grass, Hannes Nickisch, Mani Vembar, 2018, Medical Imaging.

Learning metal artifact reduction in cardiac CT images with moving pacemakers

Hannes Nickisch, Tobias Wissel, Michael M. Morlock, 2020, Medical Image Anal..

Additive Gaussian Processes

D. Duvenaud, C. Rasmussen, H. Nickisch, 2011, NIPS.

Extraction of visual features from natural video data using Slow Feature Analysis

Hannes Nickisch, H. Nickisch, 2006 .

Attribute-Based Classification for Zero-Shot Visual Object Categorization

Christoph H. Lampert, H. Nickisch, S. Harmeling, 2022 .

Generating anatomical models of the heart and the aorta from medical images for personalized physiological simulations

Jürgen Weese, Alejandro F. Frangi, Christine Toumoulin, 2013, Medical & Biological Engineering & Computing.

The GPML Toolbox version 4 . 2

Hannes Nickisch, Carl Edward Rasmussen, C. Rasmussen, 2016 .

Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis

Klaus Obermayer, Wendelin Böhmer, Hannes Nickisch, 2012, Machine Learning.