1 citations · 1 across the 6 of their papers we have counts for
7 papers
Focus, Align, and Diffuse: Time-Series-Aware Keyframe Diffusion for Cardiac Dynamic Synthesis from Sparse Observations
Junkai Liu, Haofan Wu, Nay Aung +3
Dense cardiac dynamics are essential for assessing cardiac function, yet in practice clinicians often observe only extremely sparse measurements. However, recovering full dynamics…
Metadata-Aware Adaptation of a Generative Foundation Model for Conditional CMR Synthesis
Marc Rodríguez, Grzegorz Skorupko, Nay Aung +3
Synthetic image generation is a promising strategy to address data scarcity and the underrepresentation of clinically important phenotypes in medical imaging, yet generating images…
Chain of Flow: ECG-Conditioned 4D Cardiac Cine Generation from Patient-Specific Anatomical Anchor
Haofan Wu, Nay Aung, Theodoros N. Arvanitis +3
Cardiac cine magnetic resonance imaging (MRI) is central to functional cardiac assessment, yet a full current cine sequence may not always be directly available at the point of ana…
Exploiting Completeness Perception with Diffusion Transformer for Unified 3D MRI Synthesis
Junkai Liu, Nay Aung, Theodoros N. Arvanitis +3
Missing data problems, such as missing modalities in multi-modal brain MRI and missing slices in cardiac MRI, pose significant challenges in clinical practice. Existing methods rel…
SAGCNet: Spatial-Aware Graph Completion Network for Missing Slice Imputation in Population CMR Imaging
Junkai Liu, Nay Aung, Theodoros N. Arvanitis +4
Magnetic resonance imaging (MRI) provides detailed soft-tissue characteristics that assist in disease diagnosis and screening. However, the accuracy of clinical practice is often h…
RefineSeg: Dual Coarse-to-Fine Learning for Medical Image Segmentation
Anghong Du, Nay Aung, Theodoros N. Arvanitis +4
High-quality pixel-level annotations of medical images are essential for supervised segmentation tasks, but obtaining such annotations is costly and requires medical expertise. To…
Fairness-Aware Data Augmentation for Cardiac MRI using Text-Conditioned Diffusion Models
Grzegorz Skorupko, Richard Osuala, Zuzanna Szafranowska +6
While deep learning holds great promise for disease diagnosis and prognosis in cardiac magnetic resonance imaging, its progress is often constrained by highly imbalanced and biased…