10 papers
What Cohort INRs Encode and Where to Freeze Them
Vasiliki Sideri-Lampretsa, Sophie Starck, Robbie Holland +2
Reusing the early layers of cohort-trained INRs as initialization for new signals has been shown to accelerate and improve signal fitting, yet it remains unclear which layers of th…
Sparse Autoencoders for Interpretable Medical Image Representation Learning
Philipp Wesp, Robbie Holland, Vasiliki Sideri-Lampretsa +1
Vision foundation models (FMs) achieve state-of-the-art performance in medical imaging. However, they encode information in abstract latent representations that clinicians cannot i…
Evaluation of Deformable Image Registration under Alignment-Regularity Trade-off
Vasiliki Sideri-Lampretsa, Daniel Rueckert, Huaqi Qiu
Evaluating deformable image registration (DIR) is challenging due to the inherent trade-off between achieving high alignment accuracy and maintaining deformation regularity. Howeve…
Diff-Def: Diffusion-Generated Deformation Fields for Conditional Atlases
Sophie Starck, Vasiliki Sideri-Lampretsa, Bernhard Kainz +3
Anatomical atlases are widely used for population studies and analysis. Conditional atlases target a specific sub-population defined via certain conditions, such as demographics or…
CINeMA: Conditional Implicit Neural Multi-Modal Atlas for a Spatio-Temporal Representation of the Perinatal Brain
Maik Dannecker, Vasiliki Sideri-Lampretsa, Sophie Starck +5
Magnetic resonance imaging of fetal and neonatal brains reveals rapid neurodevelopment marked by substantial anatomical changes unfolding within days. Studying this critical stage…
Interpretable deformable image registration: A geometric deep learning perspective
Vasiliki Sideri-Lampretsa, Nil Stolt-Ansó, Huaqi Qiu +4
Deformable image registration poses a challenging problem where, unlike most deep learning tasks, a complex relationship between multiple coordinate systems has to be considered. A…