142 citations · 213 across the 12 of their papers we have counts for
16 papers
MRI Embeddings Complement Clinical Predictors for Cognitive Decline Modeling in Alzheimer's Disease Cohorts
Nathaniel Putera, Daniel Vilet Rodríguez, Noah Videcrantz +2
Accurate modeling of cognitive decline in Alzheimer's disease is essential for early stratification and personalized management. While tabular predictors provide robust markers of…
Deep Learning-Based Regional White Matter Hyperintensity Mapping as a Robust Biomarker for Alzheimer's Disease
Julia Machnio, Mads Nielsen, Mostafa Mehdipour Ghazi
White matter hyperintensities (WMH) are key imaging markers in cognitive aging, Alzheimer's disease (AD), and related dementias. Although automated methods for WMH segmentation hav…
Towards Scalable and Robust White Matter Lesion Localization via Multimodal Deep Learning
Julia Machnio, Sebastian Nørgaard Llambias, Mads Nielsen +1
White matter hyperintensities (WMH) are radiological markers of small vessel disease and neurodegeneration, whose accurate segmentation and spatial localization are crucial for dia…
Robust Deep Learning for Myocardial Scar Segmentation in Cardiac MRI with Noisy Labels
Aida Moafi, Danial Moafi, Evgeny M. Mirkes +4
The accurate segmentation of myocardial scars from cardiac MRI is essential for clinical assessment and treatment planning. In this study, we propose a robust deep-learning pipelin…
A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning
Stefano Cerri, Asbjørn Munk, Sebastian Nørgaard Llambias +11
We present FOMO260K, a large-scale, heterogeneous dataset of 260,927 brain Magnetic Resonance Imaging (MRI) scans from 77,589 MRI sessions and 55,378 subjects, aggregated from 910…
Assessing the Efficacy of Classical and Deep Neuroimaging Biomarkers in Early Alzheimer's Disease Diagnosis
Milla E. Nielsen, Mads Nielsen, Mostafa Mehdipour Ghazi
Alzheimer's disease (AD) is the leading cause of dementia, and its early detection is crucial for effective intervention, yet current diagnostic methods often fall short in sensiti…
Non-Reference Quality Assessment for Medical Imaging: Application to Synthetic Brain MRIs
Karl Van Eeden Risager, Torkan Gholamalizadeh, Mostafa Mehdipour Ghazi
Generating high-quality synthetic data is crucial for addressing challenges in medical imaging, such as domain adaptation, data scarcity, and privacy concerns. Existing image quali…
Data Augmentation-Based Unsupervised Domain Adaptation In Medical Imaging
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 para…
FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Brain
Mostafa Mehdipour Ghazi, Mads Nielsen
Medical images used in clinical practice are heterogeneous and not the same quality as scans studied in academic research. Preprocessing breaks down in extreme cases when anatomy,…
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…
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…
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…
Visual Speech Recognition Using PCA Networks and LSTMs in a Tandem GMM-HMM System
Marina Zimmermann, Mostafa Mehdipour Ghazi, Hazım Kemal Ekenel +1
Automatic visual speech recognition is an interesting problem in pattern recognition especially when audio data is noisy or not readily available. It is also a very challenging tas…
Combining Multiple Views for Visual Speech Recognition
Marina Zimmermann, Mostafa Mehdipour Ghazi, Hazım Kemal Ekenel +1
Visual speech recognition is a challenging research problem with a particular practical application of aiding audio speech recognition in noisy scenarios. Multiple camera setups ca…
A Comprehensive Analysis of Deep Learning Based Representation for Face Recognition
Mostafa Mehdipour Ghazi, Hazim Kemal Ekenel
Deep learning based approaches have been dominating the face recognition field due to the significant performance improvement they have provided on the challenging wild datasets. T…