7 papers
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…
MRI Embeddings Complement Clinical Predictors for Cognitive Decline Modeling in Alzheimer's Disease Cohorts
Nathaniel Putera, Daniel Vilet RodrÃguez, 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…
To Label or Not to Label: PALM -- A Predictive Model for Evaluating Sample Efficiency in Active Learning Models
Julia Machnio, Mads Nielsen, Mostafa Mehdipour Ghazi
Active learning (AL) seeks to reduce annotation costs by selecting the most informative samples for labeling, making it particularly valuable in resource-constrained settings. Howe…
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…