44 citations · 52 across the 17 of their papers we have counts for
8 papers · 1 filter
What Makes a Good Medical Image Tokenizer? Rethinking Reconstruction and Generation in Medical Image Tokenization
Niklas Bubeck, Yundi Zhang, Vasiliki Sideri-Lampretsa +4
Latent diffusion models now dominate medical image generation, and every such pipeline rests on a \emph{tokenizer} that compresses images into the latent codes for image generation…
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…
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…
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…
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…
General Vision Encoder Features as Guidance in Medical Image Registration
Fryderyk Kögl, Anna Reithmeir, Vasiliki Sideri-Lampretsa +5
General vision encoders like DINOv2 and SAM have recently transformed computer vision. Even though they are trained on natural images, such encoder models have excelled in medical…
MAD: Modality Agnostic Distance Measure for Image Registration
Vasiliki Sideri-Lampretsa, Veronika A. Zimmer, Huaqi Qiu +2
Multi-modal image registration is a crucial pre-processing step in many medical applications. However, it is a challenging task due to the complex intensity relationships between d…
Investigating Pulse-Echo Sound Speed Estimation in Breast Ultrasound with Deep Learning
Walter A. Simson, Magdalini Paschali, Vasiliki Sideri-Lampretsa +2
Ultrasound is an adjunct tool to mammography that can quickly and safely aid physicians with diagnosing breast abnormalities. Clinical ultrasound often assumes a constant sound spe…