most citedAddressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming

2 citations · 2 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2026

Evaluating the Impact of Medical Image Reconstruction on Downstream AI Fairness and Performance

Matteo Wohlrapp, Niklas Bubeck, Daniel Rueckert +1

AI-based image reconstruction models are increasingly deployed in clinical workflows to improve image quality from noisy data, such as low-dose X-rays or accelerated MRI scans. How…

cs.CV2026

No Image, No Problem: End-to-End Multi-Task Cardiac Analysis from Undersampled k-Space

Yundi Zhang, Sevgi Gokce Kafali, Niklas Bubeck +2

Conventional clinical CMR pipelines rely on a sequential "reconstruct-then-analyze" paradigm, forcing an ill-posed intermediate step that introduces avoidable artifacts and informa…

cs.CV2026

TumorFlow: Physics-Guided Longitudinal MRI Synthesis of Glioblastoma Growth

Valentin Biller, Niklas Bubeck, Lucas Zimmer +6

Glioblastoma exhibits diverse, infiltrative, and patient-specific growth patterns that are only partially visible on routine MRI, making it difficult to reliably assess true tumor…

eess.IV2025

A Biophysically-Conditioned Generative Framework for 3D Brain Tumor MRI Synthesis

Valentin Biller, Lucas Zimmer, Ayhan Can Erdur +4

Magnetic resonance imaging (MRI) inpainting supports numerous clinical and research applications. We introduce the first generative model that conditions on voxel-level, continuous…

eess.IV2025

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…

cs.LG2025★ 2 cited

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming

Jiazhen Pan, Bailiang Jian, Paul Hager +19

Large language models (LLMs) are increasingly used to answer health-related questions and support healthcare workflows, yet evidence for their safety still relies heavily on static…

eess.IV2025

Reconstruct or Generate: Exploring the Spectrum of Generative Modeling for Cardiac MRI

Niklas Bubeck, Yundi Zhang, Suprosanna Shit +2

In medical imaging, generative models are increasingly relied upon for two distinct but equally critical tasks: reconstruction, where the goal is to restore medical imaging (usuall…