13 citations · 38 across the 30 of their papers we have counts for
10 papers · 1 filter
Evaluating Reasoning Faithfulness in Medical Vision-Language Models using Multimodal Perturbations
Johannes Moll, Markus Graf, Tristan Lemke +7
Vision-language models (VLMs) often produce chain-of-thought (CoT) explanations that sound plausible yet fail to reflect the underlying decision process, undermining trust in high-…
Unsupervised whole-heart function assessment
Yundi Zhang, Daniel Rueckert, Jiazhen Pan
Motivation: CMR is the golden standard for cardiac diagnosis, and medical data annotation is time-consuming. Thus, screening techniques from unlabeled data can help streamline the…
Reconstruction-free segmentation from undersampled k-space using transformers
Yundi Zhang, Nil Stolt-Ansó, Jiazhen Pan +3
Motivation: High acceleration factors place a limit on MRI image reconstruction. This limit is extended to segmentation models when treating these as subsequent independent process…
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…
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…
How Far Have Medical Vision-Language Models Come? A Comprehensive Benchmarking Study
Che Liu, Jiazhen Pan, Weixiang Shen +3
Vision-Language Models (VLMs) trained on web-scale corpora excel at natural image tasks and are increasingly repurposed for healthcare; however, their competence in medical tasks r…
Self-supervised feature learning for cardiac Cine MR image reconstruction
Siying Xu, Marcel Früh, Kerstin Hammernik +6
We propose a self-supervised feature learning assisted reconstruction (SSFL-Recon) framework for MRI reconstruction to address the limitation of existing supervised learning method…
Beyond Distillation: Pushing the Limits of Medical LLM Reasoning with Minimalist Rule-Based RL
Che Liu, Haozhe Wang, Jiazhen Pan +6
Improving performance on complex tasks and enabling interpretable decision making in large language models (LLMs), especially for clinical applications, requires effective reasonin…
Physics-Informed Implicit Neural Representations for Joint B0 Estimation and Echo Planar Imaging
Wenqi Huang, Nan Wang, Congyu Liao +4
Echo Planar Imaging (EPI) is widely used for its rapid acquisition but suffers from severe geometric distortions due to B0 inhomogeneities, particularly along the phase encoding di…
PISCO: Self-Supervised k-Space Regularization for Improved Neural Implicit k-Space Representations of Dynamic MRI
Veronika Spieker, Hannah Eichhorn, Wenqi Huang +9
Neural implicit k-space representations (NIK) have shown promising results for dynamic magnetic resonance imaging (MRI) at high temporal resolutions. Yet, reducing acquisition time…