10 papers · 1 filter
The Learnability Gap in Medical Latent Diffusion
Mischa Dombrowski, Felix Nützel, Bernhard Kainz
Generative data augmentation with latent diffusion models is a promising strategy for addressing class imbalance in medical imaging, yet current approaches focus on perceptual fide…
GRASP: Guided Residual Adapters with Sample-wise Partitioning
Felix Nützel, Mischa Dombrowski, Bernhard Kainz
Text-to-image flow matching transformers degrade sharply in long-tail settings: tail-class outputs collapse in fidelity and diversity, limiting their value as synthetic augmentatio…
LCMem: A Universal Model for Robust Image Memorization Detection
Mischa Dombrowski, Felix Nützel, Bernhard Kainz
Recent advances in generative image modeling have achieved visual realism sufficient to deceive human experts, yet their potential for privacy preserving data sharing remains insuf…
Video Dataset Condensation with Diffusion Models
Zhe Li, Hadrien Reynaud, Mischa Dombrowski +3
In recent years, the rapid expansion of dataset sizes and the increasing complexity of deep learning models have significantly escalated the demand for computational resources, bot…
Graph Conditioned Diffusion for Controllable Histopathology Image Generation
Sarah Cechnicka, Matthew Baugh, Weitong Zhang +5
Recent advances in Diffusion Probabilistic Models (DPMs) have set new standards in high-quality image synthesis. Yet, controlled generation remains challenging, particularly in sen…
Generate to Ground: Multimodal Text Conditioning Boosts Phrase Grounding in Medical Vision-Language Models
Felix Nützel, Mischa Dombrowski, Bernhard Kainz
Phrase grounding, i.e., mapping natural language phrases to specific image regions, holds significant potential for disease localization in medical imaging through clinical reports…