Area of research
Radiology, Nuclear Medicine and Imaging · Biomedical Engineering
Research interest
Research interests include Medical Imaging Techniques and Applications, Radiomics and Machine Learning in Medical Imaging, Advanced MRI Techniques and Applications, and MRI in cancer diagnosis.
Merlin: a computed tomography vision-language foundation model and dataset.
Holistic evaluation of large language models for medical tasks with MedHELM.
Multi-Frame Image Registration for Automated Ventricular Function Assessment in Single Breath-Hold Cine MRI Using Limited Labels.
CNN-based detection of pediatric lymphoma on whole body [<sup>18</sup>F]FDG-PET/MRI.
Pipeline evaluation of a state-of-the-art AI algorithm for detection of focal cortical dysplasia: insights into potential failure sources.
AutoPET Challenge on Fully Automated Lesion Segmentation in Oncologic PET/CT Imaging, Part 2: Domain Generalization.
Automated Coregistered Segmentation for Volumetric Analysis of Multiparametric Renal MRI.
Image Quality Evaluation of Neonatal Brain MRI Using a Deep Learning Reconstruction Algorithm: A Quantitative and Multireader Study Using Variable Denoising Levels at 3 Tesla
Pipeline Evaluation of a State-of-the-Art AI Algorithm for Detection of Focal Cortical Dysplasia: Insights into Potential Failure Sources
Foundation Models in Radiology: What, How, Why, and Why Not.
Best Practices for Large Language Models in Radiology.
Attention incorporated network for sharing low-rank, image and k-space information during MR image reconstruction to achieve single breath-hold cardiac Cine imaging.
A dataset and benchmark for hospital course summarization with adapted large language models.
<i>MedShapeNet</i> - a large-scale dataset of 3D medical shapes for computer vision.
AutoPET Challenge on Fully Automated Lesion Segmentation in Oncologic PET/CT Imaging, Part 2: Domain Generalization
External Validation of an Upgraded AI Model for Screening Ileocolic Intussusception Using Pediatric Abdominal Radiographs: Multicenter Retrospective Study
Determinants of ascending aortic morphology: cross-sectional deep learning-based analysis on 25 073 non-contrast-enhanced NAKO MRI studies
Determinants of ascending aortic morphology: cross-sectional deep learning-based analysis on 25 073 non-contrast-enhanced NAKO MRI studies.
A Machine Learning System to Automate Body Computed Tomography Protocoling.
Self-Supervised Feature Learning for Cardiac Cine MR Image Reconstruction.
Pediatric PET/MRI: Imaging Techniques, Indications, and Clinical Implementation.
Adapted large language models can outperform medical experts in clinical text summarization.
<i>MedShapeNet</i> – a large-scale dataset of 3D medical shapes for computer vision
Results from the autoPET challenge on fully automated lesion segmentation in oncologic PET/CT imaging
Tumour-informed liquid biopsies to monitor advanced melanoma patients under immune checkpoint inhibition
Tumour-informed liquid biopsies to monitor advanced melanoma patients under immune checkpoint inhibition
Optimizing adult-oriented artificial intelligence for pediatric chest radiographs by adjusting operating points
Improving assessment of lesions in longitudinal CT scans: a bi-institutional reader study on an AI-assisted registration and volumetric segmentation workflow
Applications of Artificial Intelligence for Pediatric Cancer Imaging.
Reliability of Automated RECIST 1.1 and Volumetric RECIST Target Lesion Response Evaluation in Follow-Up CT—A Multi-Center, Multi-Observer Reading Study