142 citations · 258 across the 16 of their papers we have counts for
19 papers
Augmentation based unsupervised domain adaptation
Mauricio Orbes-Arteaga, Thomas Varsavsky, Lauge Sorensen +5
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 a…
CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data
Mostafa Mehdipour Ghazi, Lauge Sørensen, Sébastien Ourselin +1
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 d…
Multimodal Variational Autoencoders for Semi-Supervised Learning: In Defense of Product-of-Experts
Svetlana Kutuzova, Oswin Krause, Douglas McCloskey +2
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). Many app…
Lung Segmentation from Chest X-rays using Variational Data Imputation
Raghavendra Selvan, Erik B. Dam, Nicki S. Detlefsen +4
Pulmonary opacification is the inflammation in the lungs caused by many respiratory ailments, including the novel corona virus disease 2019 (COVID-19). Chest X-rays (CXRs) with suc…
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young +93
We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international…
On the Initialization of Long Short-Term Memory Networks
Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai +4
Weight initialization is important for faster convergence and stability of deep neural networks training. In this paper, a robust initialization method is developed to address the…
Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning
Mauricio Orbes-Arteaga, Thomas Varsavsky, Carole H. Sudre +9
Supervised learning algorithms trained on medical images will often fail to generalize across changes in acquisition parameters. Recent work in domain adaptation addresses this cha…
Knowledge distillation for semi-supervised domain adaptation
Mauricio Orbes-Arteaga, Jorge Cardoso, Lauge Sørensen +5
In the absence of sufficient data variation (e.g., scanner and protocol variability) in annotated data, deep neural networks (DNNs) tend to overfit during training. As a result, th…
Robust parametric modeling of Alzheimer's disease progression
Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai +4
Quantitative characterization of disease progression using longitudinal data can provide long-term predictions for the pathological stages of individuals. This work studies the rob…
Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling
Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai +4
Disease progression modeling (DPM) using longitudinal data is a challenging machine learning task. Existing DPM algorithms neglect temporal dependencies among measurements, make pa…
PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation
Mauricio Orbes Arteaga, Lauge Sørensen, M. Jorge Cardoso +6
For proper generalization performance of convolutional neural networks (CNNs) in medical image segmentation, the learnt features should be invariant under particular non-linear sha…
On Variational Methods for Motion Compensated Inpainting
Francois Lauze, Mads Nielsen
We develop in this paper a generic Bayesian framework for the joint estimation of motion and recovery of missing data in a damaged video sequence. Using standard maximum a posterio…
Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs
Mauricio Orbes-Arteaga, M. Jorge Cardoso, Lauge Sørensen +4
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 whi…
Robust training of recurrent neural networks to handle missing data for disease progression modeling
Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai +4
Disease progression modeling (DPM) using longitudinal data is a challenging task in machine learning for healthcare that can provide clinicians with better tools for diagnosis and…
A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images
Akshay Pai, Stefan Sommer, Lars Lau Raket +4
Template estimation plays a crucial role in computational anatomy since it provides reference frames for performing statistical analysis of the underlying anatomical population var…
Convolutional neural networks for segmentation and object detection of human semen
Malte Stær Nissen, Oswin Krause, Kristian Almstrup +3
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. In contrast t…
Towards a theory of statistical tree-shape analysis
Aasa Feragen, Pechin Lo, Marleen de Bruijne +2
In order to develop statistical methods for shapes with a tree-structure, we construct a shape space framework for tree-like shapes and study metrics on the shape space. This shape…
Higher-Order Momentum Distributions and Locally Affine LDDMM Registration
Stefan Sommer, Mads Nielsen, Sune Darkner +1
To achieve sparse parametrizations that allows intuitive analysis, we aim to represent deformation with a basis containing interpretable elements, and we wish to use elements that…
Optimization over Geodesics for Exact Principal Geodesic Analysis
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…