6 papers
MeDUET: Disentangled Unified Pretraining for 3D Medical Image Synthesis and Analysis
Junkai Liu, Ling Shao, Le Zhang
Self-supervised learning (SSL) and diffusion models have respectively advanced representation learning and generative modeling for high-dimensional 3D visual data, yet they are oft…
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…
ReportMedSAM: Guiding Segmentation Through Radiology Reports
Anghong Du, Theodoros N. Arvanitis, Colin Watts +2
Free-form radiology reports contain rich clinical descriptions, yet converting them for reliable segmentation remains challenging due to the inherent variability of natural languag…
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…