Search Results for author: Mads Nielsen

Found 25 papers, 7 papers with code

MDD-UNet: Domain Adaptation for Medical Image Segmentation with Theoretical Guarantees, a Proof of Concept

1 code implementation19 Dec 2023 Asbjørn Munk, Ao Ma, Mads Nielsen

The current state-of-the art techniques for image segmentation are often based on U-Net architectures, a U-shaped encoder-decoder networks with skip connections.

Hippocampus Image Segmentation +3

Data Augmentation-Based Unsupervised Domain Adaptation In Medical Imaging

no code implementations8 Aug 2023 Sebastian Nørgaard Llambias, Mads Nielsen, Mostafa Mehdipour Ghazi

Deep learning-based models in medical imaging often struggle to generalize effectively to new scans due to data heterogeneity arising from differences in hardware, acquisition parameters, population, and artifacts.

Data Augmentation MRI segmentation +2

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

no code implementations23 Jan 2023 Neus Rodeja Ferrer, Malini Vendela Sagar, Kiril Vadimovic Klein, Christina Kruuse, Mads Nielsen, Mostafa Mehdipour Ghazi

Cerebral Microbleeds (CMBs), typically captured as hypointensities from susceptibility-weighted imaging (SWI), are particularly important for the study of dementia, cerebrovascular disease, and normal aging.

Augmentation based unsupervised domain adaptation

no code implementations23 Feb 2022 Mauricio Orbes-Arteaga, Thomas Varsavsky, Lauge Sorensen, Mads Nielsen, Akshay Pai, Sebastien Ourselin, Marc Modat, M Jorge Cardoso

The insertion of deep learning in medical image analysis had lead to the development of state-of-the art strategies in several applications such a disease classification, as well as abnormality detection and segmentation.

Anomaly Detection Segmentation +1

Information Bottleneck: Exact Analysis of (Quantized) Neural Networks

1 code implementation ICLR 2022 Stephan Sloth Lorenzen, Christian Igel, Mads Nielsen

In this setting, we observed a fitting phase for all layers and a compression phase for the output layer in all experiments; the compression in the hidden layers was dependent on the type of activation function.

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

no code implementations8 Apr 2021 Mostafa Mehdipour Ghazi, Lauge Sørensen, Sébastien Ourselin, Mads Nielsen

Learning temporal patterns from multivariate longitudinal data is challenging especially in cases when data is sporadic, as often seen in, e. g., healthcare applications where the data can suffer from irregularity and asynchronicity as the time between consecutive data points can vary across features and samples, hindering the application of existing deep learning models that are constructed for complete, evenly spaced data with fixed sequence lengths.

ICU Mortality Representation Learning +2

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

1 code implementation18 Jan 2021 Svetlana Kutuzova, Oswin Krause, Douglas McCloskey, Mads Nielsen, Christian Igel

Multimodal generative models should be able to learn a meaningful latent representation that enables a coherent joint generation of all modalities (e. g., images and text).

Lung Segmentation from Chest X-rays using Variational Data Imputation

3 code implementations20 May 2020 Raghavendra Selvan, Erik B. Dam, Nicki S. Detlefsen, Sofus Rischel, Kaining Sheng, Mads Nielsen, Akshay Pai

Pulmonary opacification is the inflammation in the lungs caused by many respiratory ailments, including the novel corona virus disease 2019 (COVID-19).

Data Augmentation Image Segmentation +2

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

4 code implementations9 Feb 2020 Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young, Esther E. Bron, Arthur W. Toga, Michael W. Weiner, Frederik Barkhof, Nick C. Fox, Arman Eshaghi, Tina Toni, Marcin Salaterski, Veronika Lunina, Manon Ansart, Stanley Durrleman, Pascal Lu, Samuel Iddi, Dan Li, Wesley K. Thompson, Michael C. Donohue, Aviv Nahon, Yarden Levy, Dan Halbersberg, Mariya Cohen, Huiling Liao, Tengfei Li, Kaixian Yu, Hongtu Zhu, Jose G. Tamez-Pena, Aya Ismail, Timothy Wood, Hector Corrada Bravo, Minh Nguyen, Nanbo Sun, Jiashi Feng, B. T. Thomas Yeo, Gang Chen, Ke Qi, Shiyang Chen, Deqiang Qiu, Ionut Buciuman, Alex Kelner, Raluca Pop, Denisa Rimocea, Mostafa M. Ghazi, Mads Nielsen, Sebastien Ourselin, Lauge Sorensen, Vikram Venkatraghavan, Keli Liu, Christina Rabe, Paul Manser, Steven M. Hill, James Howlett, Zhiyue Huang, Steven Kiddle, Sach Mukherjee, Anais Rouanet, Bernd Taschler, Brian D. M. Tom, Simon R. White, Noel Faux, Suman Sedai, Javier de Velasco Oriol, Edgar E. V. Clemente, Karol Estrada, Leon Aksman, Andre Altmann, Cynthia M. Stonnington, Yalin Wang, Jianfeng Wu, Vivek Devadas, Clementine Fourrier, Lars Lau Raket, Aristeidis Sotiras, Guray Erus, Jimit Doshi, Christos Davatzikos, Jacob Vogel, Andrew Doyle, Angela Tam, Alex Diaz-Papkovich, Emmanuel Jammeh, Igor Koval, Paul Moore, Terry J. Lyons, John Gallacher, Jussi Tohka, Robert Ciszek, Bruno Jedynak, Kruti Pandya, Murat Bilgel, William Engels, Joseph Cole, Polina Golland, Stefan Klein, Daniel C. Alexander

TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease.

Alzheimer's Disease Detection Disease Prediction

Knowledge distillation for semi-supervised domain adaptation

no code implementations16 Aug 2019 Mauricio Orbes-Arteaga, Jorge Cardoso, Lauge Sørensen, Christian Igel, Sebastien Ourselin, Marc Modat, Mads Nielsen, Akshay Pai

As a result, their performance is significantly lower on data from unseen sources compared to the performance on data from the same source as the training data.

Domain Adaptation Knowledge Distillation +1

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

no code implementations16 Aug 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

Inspired by recent work in semi-supervised learning we introduce a novel method to adapt from one source domain to $n$ target domains (as long as there is paired data covering all domains).

Domain Adaptation

Robust parametric modeling of Alzheimer's disease progression

no code implementations14 Aug 2019 Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai, Marc Modat, M. Jorge Cardoso, Sébastien Ourselin, Lauge Sørensen

Different M-estimators and logistic functions, including a novel type proposed in this study, called modified Stannard, are evaluated on the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) for robust modeling of volumetric MRI and PET biomarkers, CSF measurements, as well as cognitive tests.

Density Estimation

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

no code implementations17 Mar 2019 Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai, M. Jorge Cardoso, Marc Modat, Sebastien Ourselin, Lauge Sørensen

The proposed LSTM algorithm is applied to model the progression of Alzheimer's disease (AD) using six volumetric magnetic resonance imaging (MRI) biomarkers, i. e., volumes of ventricles, hippocampus, whole brain, fusiform, middle temporal gyrus, and entorhinal cortex, and it is compared to standard LSTM networks with data imputation and a parametric, regression-based DPM method.

Hippocampus Imputation

PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation

no code implementations3 Oct 2018 Mauricio Orbes Arteaga, Lauge Sørensen, M. Jorge Cardoso, Marc Modat, Sebastien Ourselin, Stefan Sommer, Mads Nielsen, Christian Igel, Akshay Pai

For proper generalization performance of convolutional neural networks (CNNs) in medical image segmentation, the learnt features should be invariant under particular non-linear shape variations of the input.

Image Segmentation Medical Image Segmentation +1

On Variational Methods for Motion Compensated Inpainting

no code implementations21 Sep 2018 Francois Lauze, Mads Nielsen

Using standard maximum a posteriori to variational formulation rationale, we derive generic minimum energy formulations for the estimation of a reconstructed sequence as well as motion recovery.

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

no code implementations20 Aug 2018 Mauricio Orbes-Arteaga, M. Jorge Cardoso, Lauge Sørensen, Marc Modat, Sébastien Ourselin, Mads Nielsen, Akshay Pai

Segmenting vascular pathologies such as white matter lesions in Brain magnetic resonance images (MRIs) require acquisition of multiple sequences such as T1-weighted (T1-w) --on which lesions appear hypointense-- and fluid attenuated inversion recovery (FLAIR) sequence --where lesions appear hyperintense--.

Imputation Segmentation

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

no code implementations1 May 2017 Akshay Pai, Stefan Sommer, Lars Lau Raket, Line Kühnel, Sune Darkner, Lauge Sørensen, Mads Nielsen

Template estimation plays a crucial role in computational anatomy since it provides reference frames for performing statistical analysis of the underlying anatomical population variability.

Anatomy Image Registration

Convolutional neural networks for segmentation and object detection of human semen

no code implementations3 Apr 2017 Malte Stær Nissen, Oswin Krause, Kristian Almstrup, Søren Kjærulff, Torben Trindkær Nielsen, Mads Nielsen

We compare a set of convolutional neural network (CNN) architectures for the task of segmenting and detecting human sperm cells in an image taken from a semen sample.

object-detection Object Detection

Optimization over Geodesics for Exact Principal Geodesic Analysis

1 code implementation11 Aug 2010 Stefan Sommer, François Lauze, Mads Nielsen

In fields ranging from computer vision to signal processing and statistics, increasing computational power allows a move from classical linear models to models that incorporate non-linear phenomena.

Computational Geometry

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