469 citations · 706 across the 59 of their papers we have counts for
66 papers
Semantic Latent Alignment for Flow Matching with Invertible Bi-Lipschitz Adapters
Felix Nützel, Mischa Dombrowski, Luca Hagen +1
Pretrained latent spaces trade compression, reconstruction, and the learnability of the coordinates a downstream generator has to work in, which complicates adapting these spaces t…
The Predictability Bias of Joint-Embedding Predictive Learning in Medical Imaging
Fabian Drexel, Bernhard Kainz, Jakob N. Kather +4
Joint-embedding predictive architectures (JEPAs) learn by predicting teacher representations of masked regions from visible context. Point prediction summarizes the conditional tar…
The Text Beside the Image: Detection, Utility and Leakage for Trustworthy Multimodal Medical Data and Beyond
Andreas Maier, Monica Hinrichs-Mayer, Franziska Weber +4
Medical images are released with the reports that describe them, and protecting the image does not protect the report. This paper measures the text component of such releases. We m…
A Data-Interventional Framework for Auditing Privacy and Fairness in Generative Medical Imaging
Mischa Dombrowski, Bernhard Kainz
Diffusion-based synthetic data generation offers a promising route for sharing medical imaging data without releasing sensitive patient records. However, generative models face a f…
MMAP: Multimodal Missing-Aware Pretraining for Longitudinal Alzheimer's Prediction
Fiona Kekwick, Matthew Baugh, Bernhard Kainz +2
Clinical decision making heavily relies on predicting the disease progression trajectory by seeking to understand patient's health status which is characterised by multimodal medic…
A Principled Approach to Unsupervised Anomaly Detection
James Myles, Matthew Baugh, Johanna P. Müller +2
Traditional unsupervised anomaly detection (UAD) methods are designed to flag or localise deviations from a normative distribution, ignoring the underlying generative mechanisms of…
Self-Supervised Cardiac Phase Detection via Single-Parameter Latent Orbits
John Bonnici, Matthew Baugh, Aleksandra Kulbaka +3
Accurate identification of end-diastole (ED) and end-systole (ES) in echocardiography underpins the quantification of ventricular function, yet manual selection of these key frames…
Frozen DINO Localizes Image Edits Without a Localizer
Zane Kumar, Vishal Jain, Bernhard Kainz
Localized image edits can change a photograph's meaning while leaving most of it authentic, so forensic analysis must identify where an edit occurred. We show that patch-level pert…
From Training to Deployment: Post-Hoc Causal Feature Identification via Sensitivity Ratios
Athanasios Vlontzos, Giorgos Papanastasiou, Bernhard Kainz +1
Given a model that is already trained, which features does it rely on causally versus spuriously? Existing methods require access to the training procedure and cannot answer this p…
Entangled by Design: Spurious Intra-Variable Signal Routing in Tabular In-Context Learners
Athanasios Vlontzos, Giorgos Papanastasiou, Bernhard Kainz +1
Consider a model trained at a single hospital to predict patient recovery, where the measured feature bundles the patient's true health signal () with a systematic artefact…
Distribution-Aware Diffusion-LLM for Robust Ultra-Long-Term Time Series Forecasting
Falguni Ghosh, Vahid Hashemi, Bernhard Kainz
Time series forecasting is a fundamental machine learning task. Recent work has explored Large Language Models (LLMs) for this purpose due to their strong generalization, pattern r…
Wasserstein-Aligned Localisation for VLM-Based Distributional OOD Detection in Medical Imaging
Bernhard Kainz, Johanna P Mueller, Matthew Baugh +1
Zero-shot anomaly localisation via vision-language models (VLMs) offers a compelling approach for rare pathology detection, yet its performance is fundamentally limited by the abse…
Vision-Language Models as Zero-Annotation Oracles in Histopathology
Vishal Jain, Giorgio Buzzanca, Sarah Cechnicka +6
Foreground segmentation is the critical first step of every computational pathology pipeline, yet existing methods rely on hand-tuned heuristics or supervised models that overfit t…
Wasserstein Equilibrium Decoding for Reliable Medical Visual Question Answering
Luca Hagen, Johanna P. Müller, Weitong Zhang +2
Small vision-language models (2-8B) are well-suited for clinical deployment due to privacy constraints, limited connectivity, and low-latency requirements favouring on-device or on…
Multi-Camera AR Guidance System for Surgical Instrument Handling and Assembly: Investigating Workload and Efficiency
Shiyu Li, Julian Kreimeier, Hannah Schieber +4
The handling and assembly of instruments during surgery imposes high cognitive demands on scrub nurses, particularly when instruments are unfamiliar. We present a supporting guidan…
Stain-Aware Wavelet Regularization for Instant Adversarial Purification in Histopathology
Zhe Li, Bernhard Kainz
Deep learning has become prevalent in computational pathology pipelines that support tasks such as cancer screening and digital pathology analysis. However, the susceptibility of n…
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…
Geometry-Aware Uncertainty Coresets for Robust Visual In-Context Learning in Histopathology
Franciskus Xaverius Erick, Johanna Paula Müller, Bernhard Kainz
Vision-language models (VLMs) can couple visual perception with open-ended clinical reasoning, making them attractive for computational histopathology. However, fine-tuning billion…
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM
Jingxuan Kang, Ziqi Zhang, Shaoming Zheng +9
Segmentation is central to clinical diagnosis and monitoring, yet the reliability of modern foundation models in medical imaging still depends on the availability of precise prompt…
SigVLP: Sigmoid Volume-Language Pre-Training for Self-Supervised CT-Volume Adaptive Representation Learning
Jiayi Wang, Hadrien Reynaud, Ibrahim Ethem Hamamci +4
Large-scale, volumetric medical imaging datasets typically aggregate scans from different vendors and devices, resulting in highly variable resolution, slice thicknesses, and numbe…
Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture
Zhe Li, Bernhard Kainz
Deep neural networks demonstrate impressive performance in visual recognition but remain highly vulnerable to imperceptible adversarial attacks. Existing defense strategies such as…
Measuring and Aligning Abstraction in Vision-Language Models with Medical Taxonomies
Ben Schaper, Maxime Di Folco, Bernhard Kainz +2
Vision-Language Models show strong zero-shot performance for chest X-ray classification, but standard flat metrics fail to distinguish between clinically minor and severe errors. T…
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…