469 citations · 706 across the 59 of their papers we have counts for
18 papers · 1 filter
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…
InfoMotion: A Graph-Based Approach to Video Dataset Distillation for Echocardiography
Zhe Li, Hadrien Reynaud, Alberto Gomez +1
Echocardiography plays a critical role in the diagnosis and monitoring of cardiovascular diseases as a non-invasive real-time assessment of cardiac structure and function. However,…
Label-free Motion-Conditioned Diffusion Model for Cardiac Ultrasound Synthesis
Zhe Li, Hadrien Reynaud, Johanna P Müller +1
Ultrasound echocardiography is essential for the non-invasive, real-time assessment of cardiac function, but the scarcity of labelled data, driven by privacy restrictions and the c…
STARCaster: Spatio-Temporal AutoRegressive Video Diffusion for Identity- and View-Aware Talking Portraits
Foivos Paraperas Papantoniou, Stathis Galanakis, Rolandos Alexandros Potamias +2
This paper presents STARCaster, an identity-aware spatio-temporal video diffusion model that addresses both speech-driven portrait animation and dynamic viewpoint control, given an…
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…
Better Tokens for Better 3D: Advancing Vision-Language Modeling in 3D Medical Imaging
Ibrahim Ethem Hamamci, Sezgin Er, Suprosanna Shit +7
Recent progress in vision-language modeling for 3D medical imaging has been fueled by large-scale computed tomography (CT) corpora with paired free-text reports, stronger architect…
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…
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…
CTFlow: Video-Inspired Latent Flow Matching for 3D CT Synthesis
Jiayi Wang, Hadrien Reynaud, Franciskus Xaverius Erick +1
Generative modelling of entire CT volumes conditioned on clinical reports has the potential to accelerate research through data augmentation, privacy-preserving synthesis and reduc…
Latent Interpolation Learning Using Diffusion Models for Cardiac Volume Reconstruction
Niklas Bubeck, Suprosanna Shit, Chen Chen +8
Cardiac Magnetic Resonance (CMR) imaging is a critical tool for diagnosing and managing cardiovascular disease, yet its utility is often limited by the sparse acquisition of 2D sho…
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…
Enabling PSO-Secure Synthetic Data Sharing Using Diversity-Aware Diffusion Models
Mischa Dombrowski, Bernhard Kainz
Synthetic data has recently reached a level of visual fidelity that makes it nearly indistinguishable from real data, offering great promise for privacy-preserving data sharing in…
CRG Score: A Distribution-Aware Clinical Metric for Radiology Report Generation
Ibrahim Ethem Hamamci, Sezgin Er, Suprosanna Shit +3
Evaluating long-context radiology report generation is challenging. NLG metrics fail to capture clinical correctness, while LLM-based metrics often lack generalizability. Clinical…
NOVA: A Benchmark for Anomaly Localization and Clinical Reasoning in Brain MRI
Cosmin I. Bercea, Jun Li, Philipp Raffler +12
In many real-world applications, deployed models encounter inputs that differ from the data seen during training. Out-of-distribution detection identifies whether an input stems fr…
Dataset Distillation with Probabilistic Latent Features
Zhe Li, Sarah Cechnicka, Cheng Ouyang +3
As deep learning models grow in complexity and the volume of training data increases, reducing storage and computational costs becomes increasingly important. Dataset distillation…
Leveraging Multi-Modal Information to Enhance Dataset Distillation
Zhe Li, Hadrien Reynaud, Bernhard Kainz
Dataset distillation aims to create a small and highly representative synthetic dataset that preserves the essential information of a larger real dataset. Beyond reducing storage a…
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…
EchoFlow: A Foundation Model for Cardiac Ultrasound Image and Video Generation
Hadrien Reynaud, Alberto Gomez, Paul Leeson +2
Advances in deep learning have significantly enhanced medical image analysis, yet the availability of large-scale medical datasets remains constrained by patient privacy concerns.…