4 citations · 5 across the 7 of their papers we have counts for
9 papers
Sparse cubical complexes for efficient topology-preservation in image data
Alexander H. Berger, Marco Fontana, Daniel Rueckert +3
Persistent homology (PH) is a frequently used tool for extracting and preserving topological information from image data, particularly in image segmentation, where preservation of…
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…
Temporally Consistent Graph Extraction and Matching for Longitudinal Angiographic Images
Linus Kreitner, Laurin Lux, Carmen Baumann +2
Recent advances in angiographic imaging have enabled longitudinal visualization of the microvasculature. Image processing pipelines based on vessel graphs are able to resolve subtl…
It depends: Incorporating correlations for joint aleatoric and epistemic uncertainties of high-dimensional output spaces
Leonhard F. Feiner, Manuel Nickel, Martin Menten +6
Uncertainty Quantification (UQ) plays a vital role in enhancing the reliability of deep learning model predictions, especially in scenarios with high-dimensional output spaces. Thi…
Towards Cardiac MRI Foundation Models: Comprehensive Visual-Tabular Representations for Whole-Heart Assessment and Beyond
Yundi Zhang, Paul Hager, Che Liu +4
Cardiac magnetic resonance imaging is the gold standard for non-invasive cardiac assessment, offering rich spatio-temporal views of the cardiac anatomy and physiology. Patient-leve…
A Tale of Two Classes: Adapting Supervised Contrastive Learning to Binary Imbalanced Datasets
David Mildenberger, Paul Hager, Daniel Rueckert +1
Supervised contrastive learning (SupCon) has proven to be a powerful alternative to the standard cross-entropy loss for classification of multi-class balanced datasets. However, it…
MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learning
Jiazhen Pan, Che Liu, Junde Wu +6
Reasoning is a critical frontier for advancing medical image analysis, where transparency and trustworthiness play a central role in both clinician trust and regulatory approval. A…
A Graph-Based Framework for Interpretable Whole Slide Image Analysis
Alexander Weers, Alexander H. Berger, Laurin Lux +3
The histopathological analysis of whole-slide images (WSIs) is fundamental to cancer diagnosis but is a time-consuming and expert-driven process. While deep learning methods show p…
Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations
Laurin Lux, Alexander H. Berger, Maria Romeo Tricas +8
Interpretability is crucial for utilizing machine learning models as clinical decision support tools for medical diagnostics. However, most state-of-the-art image classifiers based…