Mads Nielsen

Christian Igel, Mads Nielsen, François Lauze, 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Special Issue on the Second International Conference on Scale Space Theory in Computer Vision: Guest Editors' Comments

Olivier D. Faugeras, Pietro Perona, Guillermo Sapiro, 2000, J. Vis. Commun. Image Represent..

Higher-Order Momentum Distributions and Locally Affine LDDMM Registration

Xavier Pennec, Stefan Sommer, Mads Nielsen, 2011, SIAM J. Imaging Sci..

A SVD based Image Complexity Measure

Mads Nielsen, David Gustavsson, Kim Steenstrup Pedersen, 2009, VISAPP.

Geometry and Statistics: Manifolds and Stratified Spaces

Mads Nielsen, François Lauze, Eva Bjørn Vedel Jensen, 2014, Journal of Mathematical Imaging and Vision.

A Total Variation Motion Adaptive Deinterlacing Scheme

Mads Nielsen, François Lauze, Sune Høgild Keller, 2005, Scale-Space.

Exploring Non-linear Diffusion: The Diffusion Echo

Mads Nielsen, Erik Dam, 2001, Scale-Space.

Automatic detection of diseased regions in knee cartilage

Mads Nielsen, Ole Fogh Olsen, Claus Christiansen, 2007, SPIE Medical Imaging.

Multi-hypothesis transform domain Wyner-Ziv video coding including optical flow

Mads Nielsen, François Lauze, Xin Huang, 2011, 2011 IEEE 13th International Workshop on Multimedia Signal Processing.

Locally Orderless Registration for Diffusion Weighted Images

Mads Nielsen, François Lauze, Sune Darkner, 2015, MICCAI.

Sparsity and scale: Compact representations of deformation for diffeomorphic registration

Xavier Pennec, Stefan Sommer, Mads Nielsen, 2012, 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis.

Gaussian Scale-Space Theory

Peter Johansen, Luc Florack, Mads Nielsen, 1997, Computational Imaging and Vision.

Proper scales of shapes - A curved scale space

Joachim Weickert, Mads Nielsen, Peter Johansen, 1999 .

From a 2D Shape to a String Structure Using the Symmetry Set

Philip Bille, Arjan Kuijper, Mads Nielsen, 2004, ECCV.

Binocular dense depth reconstruction using isotropy constraint

Rachid Deriche, Mads Nielsen, 1996 .

Medical Image Computing and Computer-Assisted Intervention MICCAI 2006: 9th International ConferenceCopenhagen, Denmark, October 1-6, 2006,Proceedings, Part II (Lecture Notes in Computer Science)

Rasmus Larsen, Mads Nielsen, Jon Sporring, 2006 .

Higher Order Kernels and Locally Affine LDDMM Registration pdf

Xavier Pennec, Stefan Sommer, Mads Nielsen, 2011, ArXiv.

Manifold Valued Statistics, Exact Principal Geodesic Analysis and the Effect of Linear Approximations

Søren Hauberg, Stefan Sommer, Mads Nielsen, 2010, ECCV.

A note on differential corner measures

Joachim Weickert, Jon Sporring, Mads Nielsen, 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

Automated Effect-Specific Mammographic Pattern Measures

Mads Nielsen, Marco Loog, Jakob Raundahl, 2008, IEEE Transactions on Medical Imaging.

Video Super-Resolution Using Simultaneous Motion and Intensity Calculations

Mads Nielsen, François Lauze, Sune Høgild Keller, 2011, IEEE Transactions on Image Processing.

Feature-Based Image Analysis

Mads Nielsen, Lewis D. Griffin, Martin Lillholm, 2003, International Journal of Computer Vision.

Computer Vision — ECCV 2002

J. van Leeuwen, Jan van Leeuwen, Mads Nielsen, 2002, Lecture Notes in Computer Science.

Asymmetry quantization and application to human mandibles

Sven Kreiborg, Jon Sporring, Mads Nielsen, 2004, SPIE Medical Imaging.

Isotropic Regularization

Mads Nielsen, 1993, BMVC.

Shape regression for vertebra fracture quantification

Marleen de Bruijne, Mads Nielsen, László B. Tankó, 2005, SPIE Medical Imaging.

Reconstructing the Optical Thickness from Hoffman Modulation Contrast Images

Jon Sporring, Mads Nielsen, Niels Holm Olsen, 2003, SCIA.

The Generic Structure of the Optic Flow Field

Mads Nielsen, Ole Fogh Olsen, O. F. Olsen, 2005, Journal of Mathematical Imaging and Vision.

Markov Random Fields on Triangle Meshes

Mads Nielsen, Henrik Aanæs, Andreas Bærentzen, 2010 .

A variational method for automatic localization of the most pathological ROI in the knee cartilage

Mads Nielsen, François Lauze, Marco Loog, 2008, SPIE Medical Imaging.

Training shortest-path tractography: Automatic learning of spatial priors

Mads Nielsen, Niklas Kasenburg, Aasa Feragen, 2016, NeuroImage.

Computer Vision — ECCV 2002

J. van Leeuwen, Jan van Leeuwen, Mads Nielsen, 2002, Lecture Notes in Computer Science.

Effect of projective viewpoint in detecting temporal density changes

Mads Nielsen, Ole Fogh Olsen, Jakob Raundahl, 2004, SPIE Medical Imaging.

On the Duality of Scalar and Density Flows

Luc Florack, Wiro J. Niessen, Mads Nielsen, 1997, Scale-Space.

Guest Editorial: Special Issue on Statistics of Shapes and Textures

Lars Kai Hansen, Jon Sporring, Mads Nielsen, 2004, Journal of Mathematical Imaging and Vision.

A splitting algorithm for directional regularization and sparsification

Mads Nielsen, Lars Lau Rakêt, 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

Special Issue on Tribute Workshop for Peter Johansen

Marleen de Bruijne, Jon Sporring, Mads Nielsen, 2008, Journal of Mathematical Imaging and Vision.

A Static SMC Sampler on Shapes for the Automated Segmentation of Aortic Calcifications

Mads Nielsen, Sami S. Brandt, Kersten Petersen, 2010, ECCV.

A Family of Principal Component Analyses for Dealing with Outliers

Marleen de Bruijne, Mads Nielsen, François Lauze, 2007, MICCAI.

Image segmentation by shape particle filtering

Marleen de Bruijne, Mads Nielsen, 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

Regularization, Scale-Space, and Edge Detection Filters

Rachid Deriche, Luc Florack, Mads Nielsen, 1996, ECCV.

MACD: an imaging marker for cardiovascular disease

Marleen de Bruijne, Mads Nielsen, Melanie Ganz, 2010, Medical Imaging.

Texture-Based Analysis of COPD: A Data-Driven Approach

Lauge Sørensen, Marleen de Bruijne, Mads Nielsen, 2012, IEEE Transactions on Medical Imaging.

Dense iterative contextual pixel classification using Kriging

Mads Nielsen, Sami S. Brandt, Marco Loog, 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

Scale-Space Theories in Computer Vision

Joachim Weickert, Mads Nielsen, Ole Fogh Olsen, 1999, Lecture Notes in Computer Science.

Lung Segmentation from Chest X-rays using Variational Data Imputation pdf

Akshay Pai, Mads Nielsen, Erik B. Dam, 2020, ArXiv.

Koenderink Corner Points

Mads Nielsen, Ole Fogh Olsen, Mikkel Sigurd, 2001, IWVF.

On Restricting Planar Curve Evolution to Finite Dimensional Implicit Subspaces with Non-Euclidean Metric

Stefan Sommer, Mads Nielsen, François Lauze, 2010, Journal of Mathematical Imaging and Vision.

Automatic Segmentation of Vertebrae from Radiographs: A Sample-Driven Active Shape Model Approach

Mads Nielsen, Martin Lillholm, Kersten Petersen, 2011, MLMI.

Locating articular cartilage in MR images

Mads Nielsen, Ole Fogh Olsen, Claus Christiansen, 2005, SPIE Medical Imaging.

Cascaded classifier for large-scale data applied to automatic segmentation of articular cartilage

Christian Igel, Mads Nielsen, François Lauze, 2012, Medical Imaging.

Prior knowledge regularization in statistical medical image tasks

Marleen de Bruijne, Jon Sporring, Mads Nielsen, 2009 .

Classification of Laser Induced Fluorescence spectra from normal and malignant tissues using Learning Vector Quantization neural network in bladder cancer diagnosis

Mads Nielsen, Vijendra Prabhu, Gopal Karemore, 2008, 2008 8th IEEE International Conference on BioInformatics and BioEngineering.

Mammographic density measured as changes in tissue structure caused by HRT

Mads Nielsen, Marco Loog, Jakob Raundahl, 2006, SPIE Medical Imaging.

Optimal Graph Based Segmentation Using Flow Lines with Application to Airway Wall Segmentation

Marleen de Bruijne, Mads Nielsen, Jens Petersen, 2011, IPMI.

Scale focusing of statistical shape models for breast region segmentation and pectoral muscle suppression

Mads Nielsen, Naga P. K. R. Dandu, 2009, Medical Imaging.

Exploring the representation capabilities of the HOG descriptor

Benjamin B. Kimia, Mads Nielsen, François Lauze, 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

Regularization, Scale-Space, and Edge Detection Filters

Rachid Deriche, Luc Florack, Mads Nielsen, 1996, Journal of Mathematical Imaging and Vision.

Supervised hub-detection for brain connectivity

Mads Nielsen, Niklas Kasenburg, Aasa Feragen, 2016, SPIE Medical Imaging.

Detection of interstitial lung disease in PA chest radiographs

Mads Nielsen, Bram van Ginneken, Marco Loog, 2004, SPIE Medical Imaging.

The Hausdorff Dimension and Scale-Space Normalization of Natural Images

Mads Nielsen, Kim Steenstrup Pedersen, 2000, J. Vis. Commun. Image Represent..

Optimal surface segmentation using flow lines to quantify airway abnormalities in chronic obstructive pulmonary disease

Marleen de Bruijne, Mads Nielsen, Jens Petersen, 2014, Medical Image Anal..

Computer Vision — ECCV 2002

Mads Nielsen, Peter Johansen, Anders Heyden, 2002, Lecture Notes in Computer Science.

Convolutional Neural Networks for Segmentation and Object Detection of Human Semen

Oswin Krause, Mads Nielsen, Malte Stær Nissen, 2017, SCIA.

Quantitative Vertebral Morphometry Using Neighbor-Conditional Shape Models

Marleen de Bruijne, Mads Nielsen, Paola Pettersen, 2006, MICCAI.

Towards exaggerated image stereotypes

Chen Chen, Christian Igel, Mads Nielsen, 2011, The First Asian Conference on Pattern Recognition.

Quantizing calcification in the lumbar aorta on 2-D lateral x-ray images

Mads Nielsen, François Lauze, László B. Tankó, 2005, SPIE Medical Imaging.

Markov Random Fields on 3D Polygonal Meshes

Mads Nielsen, Jakob Andreas Bærentzen, Henrik Aanæs, 2008 .

A framework for optimizing measurement weight maps to minimize the required sample size

Mads Nielsen, Marco Loog, Martin Lillholm, 2010, Medical Image Anal..

Weighting of the k-Nearest-Neighbors

Mads Nielsen, Konstantin Chernoff, 2010, 2010 20th International Conference on Pattern Recognition.

Non-rigid registration by geometry-constrained diffusion

Mads Nielsen, Per Rønsholt Andresen, M. Nielsen, 1999, Medical Image Anal..

Conditional Point Distribution Models

Mads Nielsen, Sami S. Brandt, Kersten Petersen, 2010, MCV.

Quantitative analysis of airway abnormalities in CT

Marleen de Bruijne, Mads Nielsen, Jens Petersen, 2010, Medical Imaging.

From Inpainting to Active Contours

Mads Nielsen, François Lauze, 2005, VLSM.

Generic Events for the Gradient Squared with Application to Multi-Scale Segmentation

Mads Nielsen, Ole Fogh Olsen, 1997, Scale-Space.

Deinterlacing Using Variational Methods

Mads Nielsen, François Lauze, Sune Høgild Keller, 2008, IEEE Transactions on Image Processing.

Computer vision -- ECCV 2002 : 7th European Conference on Computer Vision, Copenhagen, Denmark, May 28-31, 2002 : proceedings

Mads Nielsen, Peter Johansen, Anders Heyden, 2002, eccv 2002.

Curve Evolution in Subspaces

Mads Nielsen, François Lauze, Ole Fogh Olsen, 2007, SSVM.

Surface reconstruction: GNCs and MFA

Mads Nielsen, 1995, Proceedings of IEEE International Conference on Computer Vision.

Direct estimation of first order optic flow

Jon Sporring, Mads Nielsen, M. Nielsen, 1996 .

Automatic segmentation of high- and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative

Mads Nielsen, Erik B. Dam, Martin Lillholm, 2015, Journal of medical imaging.

Optimization over geodesics for exact principal geodesic analysis

Stefan Sommer, Mads Nielsen, François Lauze, 2010, Advances in Computational Mathematics.

A Bayesian Framework for Automated Cardiovascular Risk Scoring on Standard Lumbar Radiographs

Mads Nielsen, Sami S. Brandt, Alessandro Crimi, 2012, IEEE Transactions on Medical Imaging.

Semi-automatic detection of calcifications using nonlinear stretching on x-ray images

Maria Petrou, Marleen de Bruijne, Mads Nielsen, 2007, SPIE Medical Imaging.

Approximating Non-linear Diffusion

Mads Nielsen, Ole Fogh Olsen, Erik Dam, 2003, Scale-Space.

An Anatomically Oriented Breast Coordinate System for Mammogram Analysis

Nico Karssemeijer, Mads Nielsen, Sami S. Brandt, 2011, IEEE Transactions on Medical Imaging.

A unifying framework for automatic and semi-automatic segmentation of vertebrae from radiographs using sample-driven active shape models

Mads Nielsen, Martin Lillholm, Kersten Petersen, 2012, Machine Vision and Applications.

Means in spaces of tree-like shapes

Søren Hauberg, Mads Nielsen, François Lauze, 2011, 2011 International Conference on Computer Vision.

Quantifying Effect-Specific Mammographic Density

Mads Nielsen, Marco Loog, Jakob Raundahl, 2007, MICCAI.

Mammographic Parenchymal Texture Analysis for Estrogen-Receptor Subtype Specific Breast Cancer Risk Estimation

Mads Nielsen, Emily F. Conant, Despina Kontos, 2012, Digital Mammography / IWDM.

The Intrinsic Structure of Optic Flow Incorporating Measurement Duality

Luc Florack, Wiro J. Niessen, Mads Nielsen, 1998, International Journal of Computer Vision.

Vertebral fracture classification

Marleen de Bruijne, Mads Nielsen, László B. Tankó, 2007, SPIE Medical Imaging.

Weight Preserving Image Registration for Monitoring Disease Progression in Lung CT

Marleen de Bruijne, Mads Nielsen, Pechin Lo, 2008, MICCAI.

A PDE Solution of Brownian Warping

Mads Nielsen, Peter Johansen, P. Johansen, 2004, ECCV.

Graduated Nonconvexity by Functional Focusing

Mads Nielsen, 1997, IEEE Trans. Pattern Anal. Mach. Intell..

Scale-Space Generators and Functionals

Mads Nielsen, 1997, Gaussian Scale-Space Theory.

Image registration using stationary velocity fields parameterized by norm-minimizing Wendland kernel

Akshay Pai, Lauge Sørensen, Stefan Sommer, 2015, Medical Imaging.

Binocular Stereo from Grey-Scale Images

Luc Florack, Wiro J. Niessen, Mads Nielsen, 2004, Journal of Mathematical Imaging and Vision.

Detection and Localization of Random Signals

Jon Sporring, Mads Nielsen, Niels Holm Olsen, 2003, Scale-Space.

Toward a Theory of Statistical Tree-Shape Analysis

Marleen de Bruijne, Mads Nielsen, François Lauze, 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Learning Density Independent Texture Features

Nico Karssemeijer, Christian Igel, Mads Nielsen, 2016, Digital Mammography / IWDM.

What Do Features Tell about Images?

Mads Nielsen, Martin Lillholm, M. Nielsen, 2001, Scale-Space.

Mathematical Methods for Medical Imaging

Xavier Pennec, Mads Nielsen, Sarang C. Joshi, 2013, International Journal of Computer Vision.

The Structure of the Optic Flow Field

Mads Nielsen, Ole Fogh Olsen, O. F. Olsen, 1998, ECCV.

Quantitative vertebral morphometry using neighbor-conditional shape models

Marleen de Bruijne, Mads Nielsen, Paola Pettersen, 2007, Medical Image Anal..

Distribution, Size, and Shape of Abdominal Aortic Calcified Deposits and Their Relationship to Mortality in Postmenopausal Women

Marleen de Bruijne, Mads Nielsen, Erik Dam, 2012, Int. J. Biomed. Imaging.

Nonrigid Object Segmentation and Occlusion Detection in Image Sequences

Mads Nielsen, Niels Chr. Overgaard, Anders Heyden, 2008, VISAPP.

Evaluation of four mammographic density measures on HRT data

Mads Nielsen, Marco Loog, Jakob Raundahl, 2007, SPIE Medical Imaging.

Alternative 2D Shape Representations using the Symmetry Set

Arjan Kuijper, Mads Nielsen, Peter J. Giblin, 2006, Journal of Mathematical Imaging and Vision.

Temporal Super Resolution Using Variational Methods

Mads Nielsen, Sune H. Keller, François Lauze, 2010 .

Non-rigid Registration by Geometry-Constrained Diffusion

Mads Nielsen, Per Rønsholt Andresen, 1999, MICCAI.

On Variational Methods for Motion Compensated Inpainting pdf

Mads Nielsen, François Lauze, M. Nielsen, 2018, ArXiv.

Robust training of recurrent neural networks to handle missing data for disease progression modeling pdf

M. Jorge Cardoso, Mostafa Mehdipour-Ghazi, Akshay Pai, 2018, ArXiv.

Computing Optic Flow by Scale-Space Integration of Normal Flow

Mads Nielsen, Kim Steenstrup Pedersen, 2001, Scale-Space.

Lie Group Modeling of Nonlinear Point Set Shape Variability

Mads Nielsen, Niels Holm Olsen, 2000, AFPAC.

Proceedings of the 7th European Conference on Computer Vision-Part III

Mads Nielsen, Peter Johansen, Anders Heyden, 2002 .

Metric learning by directly minimizing the k-NN training error

Mads Nielsen, Marco Loog, Konstantin Chernoff, 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

Breast Tissue Segmentation and Mammographic Risk Scoring Using Deep Learning

Nico Karssemeijer, Mads Nielsen, Martin Lillholm, 2014, Digital Mammography / IWDM.

Combining the boundary shift integral and tensor-based morphometry for brain atrophy estimation

Akshay Pai, Lauge Sørensen, Stefan Sommer, 2016, SPIE Medical Imaging.

Scale-Time Kernels and Models

Luc Florack, Mads Nielsen, Bart M. ter Haar Romeny, 2001, Scale-Space.

On alpha Kernels, Lévy Processes, and Natural Image Statistics

Remco Duits, Mads Nielsen, Kim Steenstrup Pedersen, 2005, Scale-Space.

Eye tracking off the shelf

Mads Nielsen, John Paulin Hansen, Dan Witzner Hansen, 2004, ETRA.

Proceedings of the Fourth International Workshop on Mathematical Foundations of Computational Anatomy - Geometrical and Statistical Methods for Biological Shape Variability Modeling (MFCA 2013), Nagoya, Japan

Stanley Durrleman, Stefan Sommer, Mads Nielsen, 2013 .

Prediction of the location of the lumbar aorta using the first four lumbar vertebrae as a predictor

Mads Nielsen, Jakob Raundahl, László B. Tankó, 2004, SPIE Medical Imaging.

Sparse Multi-Scale Diffeomorphic Registration: The Kernel Bundle Framework

Xavier Pennec, Stefan Sommer, Mads Nielsen, 2012, Journal of Mathematical Imaging and Vision.

Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II

Rasmus Larsen, Jon Sporring, Mads Nielsen, 2006 .

Mass preserving registration for lung CT

Marleen de Bruijne, Jon Sporring, Mads Nielsen, 2009, Medical Imaging.

Gaussian Scale Space from Insufficient Image Information

Max A. Viergever, Mads Nielsen, Marco Loog, 2003, Scale-Space.

Breast tissue segmentation from x-ray radiographs.

Nico Karssemeijer, Mads Nielsen, Chen Chen, 2014, Physics in medicine and biology.

Characterization of Errors in Deep Learning-Based Brain MRI Segmentation

Joseph Blair, Akshay Pai, Christian Igel, 2017, Deep Learning for Medical Image Analysis.

Editorial

Carl-Fredrik Westin, Wiro J. Niessen, Mads Nielsen, 2009, International Journal of Computer Vision.

Stepwise Inverse Consistent Euler's Scheme for Diffeomorphic Image Registration

Akshay Pai, Lauge Sørensen, Stefan Sommer, 2014, WBIR.

Proceedings European Conference on Computer Vision 2002

Mads Nielsen, Peter Johansen, Anders Heyden, 2002 .

Branch Points in One-Dimensional Gaussian Scale Space

Mads Nielsen, Ole Fogh Olsen, Peter Johansen, 2000, Journal of Mathematical Imaging and Vision.

Non-linear Diffusion for Interactive Multi-scale Watershed Segmentation

Mads Nielsen, Erik Dam, M. Nielsen, 2000, MICCAI.

Patch-Based Generative Shape Model and MDL Model Selection for Statistical Analysis of Archipelagos

Mads Nielsen, Sami S. Brandt, Melanie Ganz, 2010, MLMI.

Towards exaggerated emphysema stereotypes

Lauge Sørensen, Marleen de Bruijne, Christian Igel, 2012, Medical Imaging.

Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics

M. Jorge Cardoso, Mert R. Sabuncu, Li Shen, 2017, Lecture Notes in Computer Science.

Fundamental Geodesic Deformations in Spaces of Treelike Shapes

Mads Nielsen, François Lauze, Aasa Feragen, 2010, 2010 20th International Conference on Pattern Recognition.

Understanding Hessian-Based Density Scoring

Mads Nielsen, Marco Loog, Jakob Raundahl, 2006, Digital Mammography / IWDM.

Maximum a Posteriori Estimation of Linear Shape Variation With Application to Vertebra and Cartilage Modeling

Marleen de Bruijne, Jon Sporring, Mads Nielsen, 2011, IEEE Transactions on Medical Imaging.

Increasing Accuracy of Optimal Surfaces Using Min-Marginal Energies

Marleen de Bruijne, Mads Nielsen, Jens Petersen, 2019, IEEE Transactions on Medical Imaging.

Fractal dimension and lacunarity analysis of mammographic patterns in assessing breast cancer risk related to HRT treated population: a longitudinal and cross-sectional study

Mads Nielsen, Gopal Karemore, 2009, Medical Imaging.

A Multi-scale Kernel Bundle for LDDMM: Towards Sparse Deformation Description across Space and Scales

Xavier Pennec, Stefan Sommer, Mads Nielsen, 2011, IPMI.

Motion Compensated Video Super Resolution

Mads Nielsen, François Lauze, Sune Høgild Keller, 2007, SSVM.

Brownian Warps: A Least Committed Prior for Non-rigid Registration

Benny Lautrup, Mads Nielsen, Peter Johansen, 2002, MICCAI.

Evaluation of Brownian warps for shape alignment

Mads Nielsen, 2007, SPIE Medical Imaging.

Feature displacement interpolation

Mads Nielsen, Per Rønsholt Andresen, M. Nielsen, 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

Bicycle chain shape models

Marleen de Bruijne, Chen Chen, Stefan Sommer, 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

Multimodal Variational Autoencoders for Semi-Supervised Learning: In Defense of Product-of-Experts pdf

Oswin Krause, Christian Igel, Mads Nielsen, 2021, ArXiv.

Lesion-wise evaluation for effective performance monitoring of small object segmentation

Akshay Pai, Marc Modat, Lauge Sørensen, 2021, Medical Imaging.

CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data pdf

Sébastien Ourselin, Mostafa Mehdipour-Ghazi, Lauge Sørensen, 2021, IEEE transactions on neural networks and learning systems.

4d Shape-preserving Modelling of Bone

Sven Kreiborg, Mads Nielsen, nsholt Andresen, 1998 .

Effect of inspiration on airway dimensions measured in maximal inspiration CT images of subjects without airflow limitation

Mads Nielsen, Aasa Feragen, Asger Dirksen, 2014, European Radiology.

VALIDATION OF HIPPOCAMPAL TEXTURE FOR EARLY ALZHEIMER'S DISEASE DETECTION: GENERALIZATION TO INDEPENDENT COHORTS AND EXTRAPOLATION TO VERY EARLY SIGNS OF DEMENTIA

Lauge Sørensen, Egill Rostrup, Christian Igel, 2014, Alzheimer's & Dementia.

Identification of progressors in osteoarthritis by combining biochemical and MRI-based markers

Mads Nielsen, Claus Christiansen, Marco Loog, 2009, Arthritis research & therapy.

A method to determine the mammographic regions that show early changes due to the development of breast cancer.

Nico Karssemeijer, Mads Nielsen, Gopal Karemore, 2014, Physics in medicine and biology.

Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients

Christina Lioma, Akshay Pai, Bulat Ibragimov, 2020, Scientific Reports.

Longitudinal Analysis of Airways using Registration

Mads Nielsen, Pechin Lo, Vladlena Gorbunova, 2011 .

Scale-space theories in computer vision : Second International Conference, Scale-Space '99, Corfu, Greece, September 26-27, 1999 : proceedings

Mads Nielsen, Scale-Space, 1999 .

Hippocampal texture predicts conversion from MCI to Alzheimer’s disease

Lauge Sørensen, Christian Igel, Mads Nielsen, 2013, Alzheimer's & Dementia.

Screening mammography: benefit of double reading by breast density

Mads Nielsen, Martin Lillholm, Elsebeth Lynge, 2018, Breast cancer research and treatment.

Non-rigid Registration by Geometry-constrained Diiusion

Mads Nielsen, nsholt Andresen, 1999 .

Mammographic density and structural features can individually and jointly contribute to breast cancer risk assessment in mammography screening: a case–control study

Michael Bachmann Nielsen, Mads Nielsen, Martin Lillholm, 2016, BMC Cancer.

Osteoarthritic cartilage is more homogeneous than healthy cartilage: identification of a superior region of interest colocalized with a major risk factor for osteoarthritis.

Mads Nielsen, Claus Christiansen, Erik B Dam, 2007, Academic radiology.

Early detection of Alzheimer's disease using MRI hippocampal texture

Lauge Sørensen, Egill Rostrup, Christian Igel, 2016, Human brain mapping.