90 citations · 142 across the 14 of their papers we have counts for
14 papers
A data- and compute-efficient chest X-ray foundation model beyond aggressive scaling
Chong Wang, Yabin Zhang, Yunhe Gao +9
Foundation models for medical imaging are typically pretrained on increasingly large datasets, following a "scale-at-all-costs" paradigm. However, this strategy faces two critical…
Unpaired Image-to-Image Translation via a Self-Supervised Semantic Bridge
Jiaming Liu, Felix Petersen, Yunhe Gao +6
Adversarial diffusion and diffusion-inversion methods have advanced unpaired image-to-image translation, but each faces key limitations. Adversarial approaches require target-domai…
A Probabilistic Generalization of the Mazur-Ulam Theorem
Justinas Zaliaduonis, Sergios Gatidis
The classical Mazur-Ulam theorem establishes that every surjective isometry between normed real vector spaces is an affine transformation. In various applied mathematical settings,…
MIMM-X: Disentangling Spurious Correlations for Medical Image Analysis
Louisa Fay, Hajer Reguigui, Bin Yang +2
Deep learning models can excel on medical tasks, yet often experience spurious correlations, known as shortcut learning, leading to poor generalization in new environments. Particu…
Retrospective motion correction in MRI using disentangled embeddings
Qi Wang, Veronika Ecker, Marcel Früh +2
Physiological motion can affect the diagnostic quality of magnetic resonance imaging (MRI). While various retrospective motion correction methods exist, many struggle to generalize…
Structuring Radiology Reports: Challenging LLMs with Lightweight Models
Johannes Moll, Louisa Fay, Asfandyar Azhar +5
Radiology reports are critical for clinical decision-making but often lack a standardized format, limiting both human interpretability and machine learning (ML) applications. While…
MedVAL: Toward Expert-Level Medical Text Validation with Language Models
Asad Aali, Vasiliki Bikia, Maya Varma +24
With the growing use of language models (LMs) in clinical environments, there is an immediate need to evaluate the accuracy and safety of LM-generated medical text. Currently, such…
MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks
Suhana Bedi, Hejie Cui, Miguel Fuentes +78
While large language models (LLMs) achieve near-perfect scores on medical licensing exams, these evaluations inadequately reflect the complexity and diversity of real-world clinica…
Adaptable Cardiovascular Disease Risk Prediction from Heterogeneous Data using Large Language Models
Frederike Lübeck, Jonas Wildberger, Frederik Träuble +4
Cardiovascular disease (CVD) risk prediction models are essential for identifying high-risk individuals and guiding preventive actions. However, existing models struggle with the c…
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…
Best Practices for Large Language Models in Radiology
Christian Bluethgen, Dave Van Veen, Cyril Zakka +7
At the heart of radiological practice is the challenge of integrating complex imaging data with clinical information to produce actionable insights. Nuanced application of language…
Foundation Models in Radiology: What, How, When, Why and Why Not
Magdalini Paschali, Zhihong Chen, Louis Blankemeier +6
Recent advances in artificial intelligence have witnessed the emergence of large-scale deep learning models capable of interpreting and generating both textual and imaging data. Su…
Evaluating and Improving the Effectiveness of Synthetic Chest X-Rays for Medical Image Analysis
Eva Prakash, Jeya Maria Jose Valanarasu, Zhihong Chen +9
Purpose: To explore best-practice approaches for generating synthetic chest X-ray images and augmenting medical imaging datasets to optimize the performance of deep learning models…
Highly efficient non-rigid registration in k-space with application to cardiac Magnetic Resonance Imaging
Aya Ghoul, Kerstin Hammernik, Andreas Lingg +4
In Magnetic Resonance Imaging (MRI), high temporal-resolved motion can be useful for image acquisition and reconstruction, MR-guided radiotherapy, dynamic contrast-enhancement, flo…