Mads Nielsen | DeepAI

DeepAI AI Chat

Featured Co-authors

Sharky.TV
230 publications
Sébastien Ourselin
92 publications
Gang Chen
92 publications
Polina Golland
55 publications
M. Jorge Cardoso
51 publications
Daniel C. Alexander
50 publications
Dan Li
50 publications
Stefan Sommer
41 publications
Marleen de Bruijne
40 publications
Parashkev Nachev
34 publications
Christian Igel
33 publications

∙ 08/08/2023

Data Augmentation-Based Unsupervised Domain Adaptation In Medical Imaging

Deep learning-based models in medical imaging often struggle to generali...

0 Sebastian Nørgaard Llambias, et al. ∙

∙ 01/23/2023

Deep Learning-Based Assessment of Cerebral Microbleeds in COVID-19

Cerebral Microbleeds (CMBs), typically captured as hypointensities from ...

0 Neus Rodeja Ferrer, et al. ∙

∙ 12/27/2022

Robust Cross-vendor Mammographic Texture Models Using Augmentation-based Domain Adaptation for Long-term Breast Cancer Risk

The future of population-based breast cancer screening is likely persona...

0 Andreas D. Lauritzen, et al. ∙

∙ 08/30/2022

FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Brain

Medical images used in clinical practice are heterogeneous and not the s...

0 Mostafa Mehdipour Ghazi, et al. ∙

∙ 02/23/2022

Augmentation based unsupervised domain adaptation

The insertion of deep learning in medical image analysis had lead to the...

0 Mauricio Orbes-Arteaga, et al. ∙

∙ 06/24/2021

Information Bottleneck: Exact Analysis of (Quantized) Neural Networks

The information bottleneck (IB) principle has been suggested as a way to...

0 Stephan Sloth Lorenzen, et al. ∙

∙ 01/18/2021

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

Multimodal generative models should be able to learn a meaningful latent...

23 Svetlana Kutuzova, et al. ∙

∙ 05/20/2020

Lung Segmentation from Chest X-rays using Variational Data Imputation

Pulmonary opacification is the inflammation in the lungs caused by many ...

14 Raghavendra Selvan, et al. ∙

∙ 02/09/2020

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

We present the findings of "The Alzheimer's Disease Prediction Of Longit...

0 Razvan V. Marinescu, et al. ∙

∙ 12/22/2019

On the Initialization of Long Short-Term Memory Networks

Weight initialization is important for faster convergence and stability ...

28 Mostafa Mehdipour Ghazi, et al. ∙

∙ 08/16/2019

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

Supervised learning algorithms trained on medical images will often fail...

6 Mauricio Orbes-Arteaga, et al. ∙

∙ 08/16/2019

Knowledge distillation for semi-supervised domain adaptation

In the absence of sufficient data variation (e.g., scanner and protocol ...

1 Mauricio Orbes-Arteaga, et al. ∙

∙ 08/14/2019

Robust parametric modeling of Alzheimer's disease progression

Quantitative characterization of disease progression using longitudinal ...

0 Mostafa Mehdipour Ghazi, et al. ∙

∙ 03/17/2019

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

Disease progression modeling (DPM) using longitudinal data is a challeng...

10 Mostafa Mehdipour Ghazi, et al. ∙

∙ 10/03/2018

PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation

For proper generalization performance of convolutional neural networks (...

4 Mauricio Orbes-Arteaga, et al. ∙

∙ 09/21/2018

On Variational Methods for Motion Compensated Inpainting

We develop in this paper a generic Bayesian framework for the joint esti...

0 François Lauze, et al. ∙

∙ 08/20/2018

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

Segmenting vascular pathologies such as white matter lesions in Brain ma...

0 Mauricio Orbes-Arteaga, et al. ∙

∙ 08/16/2018

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

Disease progression modeling (DPM) using longitudinal data is a challeng...

0 Mostafa Mehdipour Ghazi, et al. ∙

∙ 04/03/2017

Convolutional neural networks for segmentation and object detection of human semen

We compare a set of convolutional neural network (CNN) architectures for...

0 Malte Stær Nissen, et al. ∙

∙ 07/23/2012

Towards a theory of statistical tree-shape analysis

In order to develop statistical methods for shapes with a tree-structure...

0 Aasa Feragen, et al. ∙

∙ 12/14/2011

Higher-Order Momentum Distributions and Locally Affine LDDMM Registration

To achieve sparse parametrizations that allows intuitive analysis, we ai...

0 Stefan Sommer, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world