8 papers
Trustworthy MRI Reconstruction via Bayesian Uncertainty Quantification with Sparsity Prior Models
Ahmed Karam Eldaly, Matteo Figini, Daniel C. Alexander
We propose a novel Bayesian framework for joint image reconstruction and uncertainty quantification from compressed sensing magnetic resonance imaging data. The problem is formulat…
Causal Transfer in Medical Image Analysis
Mohammed M. Abdelsamea, Daniel Tweneboah Anyimadu, Tasneem Selim +4
Medical imaging models frequently fail when deployed across hospitals, scanners, populations, or imaging protocols due to domain shift, limiting their clinical reliability. While t…
Low-Field Magnetic Resonance Image Enhancement using Undersampled k-Space
Daniel Tweneboah Anyimadu, Mohammed Abdalla, Mohammed M. Abdelsamea +1
Low-field magnetic resonance imaging (MRI) offers a cost-effective alternative for medical imaging in resource-limited settings. However, its widespread adoption is hindered by two…
Low-Field Magnetic Resonance Image Quality Enhancement using Undersampled k-Space and Out-of-Distribution Generalisation
Daniel Tweneboah Anyimadu, Mohammed M. Abdelsamea, Ahmed Karam Eldaly
Low-field magnetic resonance imaging (MRI) offers affordable access to diagnostic imaging but faces challenges such as prolonged acquisition times and reduced image quality. Althou…
Bayesian Uncertainty-Aware MRI Reconstruction
Ahmed Karam Eldaly, Matteo Figini, Daniel C. Alexander
We propose a novel framework for joint magnetic resonance image reconstruction and uncertainty quantification using under-sampled k-space measurements. The problem is formulated as…
DeepHistoViT: An Interpretable Vision Transformer Framework for Histopathological Cancer Classification
Ravi Mosalpuri, Mohammed Abdelsamea, Ahmed Karam Eldaly
Histopathology remains the gold standard for cancer diagnosis because it provides detailed cellular-level assessment of tissue morphology. However, manual histopathological examina…