Area of research
Radiology, Nuclear Medicine and Imaging · Artificial Intelligence
Research interest
Research interests include Advanced Neuroimaging Techniques and Applications, Advanced MRI Techniques and Applications, Radiomics and Machine Learning in Medical Imaging, and MRI in cancer diagnosis.
Advanced Automated Model for Robust Bone Marrow Segmentation in Whole-body MRI
Unlocking the potential of digital pathology: Novel baselines for compression
Automated Detection of Focal Bone Marrow Lesions From MRI: A Multi-center Feasibility Study in Patients with Monoclonal Plasma Cell Disorders
Gait examination in catatonia using 3D optical markerless motion tracking
Contrastive virtual staining enhances deep learning‐based <scp>PDAC</scp> subtyping from H&E‐stained tissue cores
Learned Image Compression for HE-Stained Histopathological Images via Stain Deconvolution
AI-Based screening for thoracic aortic aneurysms in routine breast MRI
u-LINNDA: A protocol for user-optimized lymphoma identification through neural network detection aid
Precision ICU Resource Planning
Automated radiomics model for prediction of therapy response and minimal residual disease from baseline MRI in multiple myeloma
LINNDA: Lymphoma identification through neural network detection aid
Machine learning in tractography
Tractography validation Part 2: The use of anatomical model systems and measures for validation
Tractography validation Part 1: Foundations, numerical simulations, and phantom models
Deep intravital brain tumor imaging enabled by tailored three-photon microscopy and analysis
Radiomic tractometry reveals tract-specific imaging biomarkers in white matter
<scp>Reproducible Radiomics Features from Multi‐MRI‐Scanner Test–Retest‐Study: Influence on Performance and Generalizability of Models</scp>
Real-world federated learning in radiology: hurdles to overcome and benefits to gain
Deciphering white matter microstructural alterations in catatonia according to ICD-11: replication and machine learning analysis
Deep learning aided preoperative diagnosis of primary central nervous system lymphoma
The potential of federated learning for self-configuring medical object detection in heterogeneous data distributions
Splenic T2 signal intensity loss on MRI is associated with disease burden in multiple myeloma
Q-Ball high-resolution fiber tractography: Optimizing corticospinal tract delineation near gliomas and its role in the prediction of postoperative motor deficits– A proof of concept study
Microstructural white matter biomarkers of symptom severity and therapy outcome in catatonia: Rationale, study design and preliminary clinical data of the whiteCAT study
atTRACTive: Semi-automatic White Matter Tract Segmentation Using Active Learning
Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning
Tensor- and high-resolution fiber tractography for the delineation of the optic radiation and corticospinal tract in the proximity of intracerebral lesions: a reproducibility and repeatability study
Radiomic tractometry: a rich and tract-specific class of imaging biomarkers for neuroscience and medical applications
Combining Deep Learning and Radiomics for Automated, Objective, Comprehensive Bone Marrow Characterization From Whole-Body MRI
In Vivo Repeatability and Multiscanner Reproducibility of MRI Radiomics Features in Patients With Monoclonal Plasma Cell Disorders