Get our free extension to see links to code for papers anywhere online!

Add to Chrome

Add to Firefox

Picture for Mads Nielsen

Mads Nielsen

Information Bottleneck: Exact Analysis of (Quantized) Neural Networks

Jun 24, 2021
Stephan Sloth Lorenzen, Christian Igel, Mads Nielsen

Figure 1 for Information Bottleneck: Exact Analysis of (Quantized) Neural Networks

Figure 2 for Information Bottleneck: Exact Analysis of (Quantized) Neural Networks

Figure 3 for Information Bottleneck: Exact Analysis of (Quantized) Neural Networks

Figure 4 for Information Bottleneck: Exact Analysis of (Quantized) Neural Networks

Access Paper or Ask Questions

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

Apr 08, 2021
Mostafa Mehdipour Ghazi, Lauge Sørensen, Sébastien Ourselin, Mads Nielsen

Figure 1 for CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data

Figure 2 for CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data

Figure 3 for CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data

Figure 4 for CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data

Access Paper or Ask Questions

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

Jan 18, 2021
Svetlana Kutuzova, Oswin Krause, Douglas McCloskey, Mads Nielsen, Christian Igel

Figure 1 for Multimodal Variational Autoencoders for Semi-Supervised Learning: In Defense of Product-of-Experts

Figure 2 for Multimodal Variational Autoencoders for Semi-Supervised Learning: In Defense of Product-of-Experts

Figure 3 for Multimodal Variational Autoencoders for Semi-Supervised Learning: In Defense of Product-of-Experts

Figure 4 for Multimodal Variational Autoencoders for Semi-Supervised Learning: In Defense of Product-of-Experts

Access Paper or Ask Questions

Lung Segmentation from Chest X-rays using Variational Data Imputation

May 20, 2020
Raghavendra Selvan, Erik B. Dam, Sofus Rischel, Kaining Sheng, Mads Nielsen, Akshay Pai

Figure 1 for Lung Segmentation from Chest X-rays using Variational Data Imputation

Figure 2 for Lung Segmentation from Chest X-rays using Variational Data Imputation

Figure 3 for Lung Segmentation from Chest X-rays using Variational Data Imputation

Figure 4 for Lung Segmentation from Chest X-rays using Variational Data Imputation

* Source code, training data and the trained models are available here: https://github.com/raghavian/lungVAE/

Access Paper or Ask Questions

Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning

Sep 17, 2019
Mauricio Orbes-Arteaga, Thomas Varsavsky, Carole H. Sudre, Zach Eaton-Rosen, Lewis J. Haddow, Lauge Sørensen, Mads Nielsen, Akshay Pai, Sébastien Ourselin, Marc Modat, Parashkev Nachev, M. Jorge Cardoso

Figure 1 for Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning

Figure 2 for Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning

Figure 3 for Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning

* Accepted at 1st International Workshop on Domain Adaptation and Representation Transfer held at MICCAI 2019

Access Paper or Ask Questions

Knowledge distillation for semi-supervised domain adaptation

Aug 16, 2019
Mauricio Orbes-Arteaga, Jorge Cardoso, Lauge Sørensen, Christian Igel, Sebastien Ourselin, Marc Modat, Mads Nielsen, Akshay Pai

Figure 1 for Knowledge distillation for semi-supervised domain adaptation

Figure 2 for Knowledge distillation for semi-supervised domain adaptation

Figure 3 for Knowledge distillation for semi-supervised domain adaptation

Figure 4 for Knowledge distillation for semi-supervised domain adaptation

* MLCN MICCAI workshop

Access Paper or Ask Questions

Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling

Mar 17, 2019
Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai, M. Jorge Cardoso, Marc Modat, Sebastien Ourselin, Lauge Sørensen

Figure 1 for Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling

Figure 2 for Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling

Figure 3 for Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling

Figure 4 for Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling

* Medical Image Analysis, Volume 53, Pages 39-46, 2019
* arXiv admin note: substantial text overlap with arXiv:1808.05500

Access Paper or Ask Questions

On Variational Methods for Motion Compensated Inpainting

Sep 21, 2018
Francois Lauze, Mads Nielsen

Figure 1 for On Variational Methods for Motion Compensated Inpainting

Figure 2 for On Variational Methods for Motion Compensated Inpainting

Figure 3 for On Variational Methods for Motion Compensated Inpainting

Figure 4 for On Variational Methods for Motion Compensated Inpainting

* DIKU Technical report 2009 with some small corrections

Access Paper or Ask Questions

Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs

Aug 20, 2018
Mauricio Orbes-Arteaga, M. Jorge Cardoso, Lauge Sørensen, Marc Modat, Sébastien Ourselin, Mads Nielsen, Akshay Pai

Figure 1 for Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs

Figure 2 for Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs

Figure 3 for Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs

Figure 4 for Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs

* Conference on Medical Imaging with Deep Learning MIDL 2018

Access Paper or Ask Questions

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

Aug 16, 2018
Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai, M. Jorge Cardoso, Marc Modat, Sebastien Ourselin, Lauge Sørensen

Figure 1 for Robust training of recurrent neural networks to handle missing data for disease progression modeling

Figure 2 for Robust training of recurrent neural networks to handle missing data for disease progression modeling

Figure 3 for Robust training of recurrent neural networks to handle missing data for disease progression modeling

Figure 4 for Robust training of recurrent neural networks to handle missing data for disease progression modeling

* 9 pages, 1 figure, MIDL conference

Access Paper or Ask Questions

A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images

May 01, 2017
Akshay Pai, Stefan Sommer, Lars Lau Raket, Line Kühnel, Sune Darkner, Lauge Sørensen, Mads Nielsen

Figure 1 for A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images

Figure 2 for A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images

Access Paper or Ask Questions

Convolutional neural networks for segmentation and object detection of human semen

Apr 03, 2017
Malte Stær Nissen, Oswin Krause, Kristian Almstrup, Søren Kjærulff, Torben Trindkær Nielsen, Mads Nielsen

Figure 1 for Convolutional neural networks for segmentation and object detection of human semen

Figure 2 for Convolutional neural networks for segmentation and object detection of human semen

Figure 3 for Convolutional neural networks for segmentation and object detection of human semen

Figure 4 for Convolutional neural networks for segmentation and object detection of human semen

* Submitted for Scandinavian Conference on Image Analysis 2017

Access Paper or Ask Questions

Higher-Order Momentum Distributions and Locally Affine LDDMM Registration

Nov 29, 2012
Stefan Sommer, Mads Nielsen, Sune Darkner, Xavier Pennec

Figure 1 for Higher-Order Momentum Distributions and Locally Affine LDDMM Registration

Figure 2 for Higher-Order Momentum Distributions and Locally Affine LDDMM Registration

Figure 3 for Higher-Order Momentum Distributions and Locally Affine LDDMM Registration

Figure 4 for Higher-Order Momentum Distributions and Locally Affine LDDMM Registration

Access Paper or Ask Questions

Towards a theory of statistical tree-shape analysis

Jul 23, 2012
Aasa Feragen, Pechin Lo, Marleen de Bruijne, Mads Nielsen, Francois Lauze

Figure 1 for Towards a theory of statistical tree-shape analysis

Figure 2 for Towards a theory of statistical tree-shape analysis

Figure 3 for Towards a theory of statistical tree-shape analysis

Figure 4 for Towards a theory of statistical tree-shape analysis

* 36 pages, 15 figures

Access Paper or Ask Questions