6 papers
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…
Flow Matching with Optimized Subclass Priors for Medical Image Augmentation
Felix Nützel, Mischa Dombrowski, Bernhard Kainz
Rare diseases dominate the diagnostic challenge in medical imaging yet are severely underrepresented in clinical datasets, causing classifiers to fail on exactly the conditions whe…
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…
Ontology-Based Concept Distillation for Radiology Report Retrieval and Labeling
Felix Nützel, Mischa Dombrowski, Bernhard Kainz
Retrieval-augmented learning based on radiology reports has emerged as a promising direction to improve performance on long-tail medical imaging tasks, such as rare disease detecti…
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…