Area of research
Radiology, Nuclear Medicine and Imaging · Pulmonary and Respiratory Medicine
Research interest
Research interests include Radiomics and Machine Learning in Medical Imaging, Prostate Cancer Diagnosis and Treatment, MRI in cancer diagnosis, and Prostate Cancer Treatment and Research.
Empowering cancer research in Europe: the EUCAIM cancer imaging infrastructure
Leveraging foundation models for content-based image retrieval in radiology
End-to-end machine learning based discrimination of neoplastic and non-neoplastic intracerebral hemorrhage on computed tomography
Comparing the Prognostic Value of Quantitative Response Assessment Tools and LIRADS Treatment Response Algorithm in Patients with Hepatocellular Carcinoma Following Interstitial High-Dose-Rate Brachytherapy and Conventional Transarterial Chemoembolization
Cross-institutional automated multilabel segmentation for acute intracerebral hemorrhage, intraventricular hemorrhage, and perihematomal edema on CT
International Retrospective Observational Study of Continual Learning for AI on Endotracheal Tube Placement from Chest Radiographs
Comparison of Multiple State-of-the-Art Large Language Models for Patient Education Prior to CT and MRI Examinations
Comparing performance of seven fine-tuned open-source large language models in summarizing and predicting outcome-relevant information from mechanical thrombectomy reports in patients with acute ischemic stroke
Reliability and predictors of automated volume quantification with neural networks in intracerebral hemorrhage
First effectiveness data of lenvatinib and pembrolizumab as first-line therapy in advanced anaplastic thyroid cancer: a retrospective cohort study
Oncological Safety of MRI-Informed Biopsy Decision-Making in Men With Suspected Prostate Cancer
Current State of Community-Driven Radiological AI Deployment in Medical Imaging
Reduction of false positives using zone-specific prostate-specific antigen density for prostate MRI-based biopsy decision strategies
Real-world federated learning in radiology: hurdles to overcome and benefits to gain
Clinical and imaging manifestations of intracerebral hemorrhage in brain tumors and metastatic lesions: a comprehensive overview
Value of MRI - T2 Mapping to Differentiate Clinically Significant Prostate Cancer
Cooperative AI training for cardiothoracic segmentation in computed tomography: An iterative multi-center annotation approach
Deep learning enabled near-isotropic CAIPIRINHA VIBE in the nephrogenic phase improves image quality and renal lesion conspicuity
Interactive Explainable Deep Learning Model Informs Prostate Cancer Diagnosis at MRI
A machine learning tool to improve prediction of mediastinal lymph node metastases in non-small cell lung cancer using routinely obtainable [18F]FDG-PET/CT parameters
Das Netzwerk Universitätsmedizin: Technisch-organisatorische Ansätze für Forschungsdatenplattformen
Automated deep-learning system in the assessment of MRI-visible prostate cancer: comparison of advanced zoomed diffusion-weighted imaging and conventional technique
Metadata-independent classification of MRI sequences using convolutional neural networks: Successful application to prostate MRI
External Validation and Retraining of DeepBleed: The First Open-Source 3D Deep Learning Network for the Segmentation of Spontaneous Intracerebral and Intraventricular Hemorrhage
Intermittent body composition analysis as monitoring tool for muscle wasting in critically ill COVID-19 patients
Pericardial Effusion Predicts Clinical Outcomes in Patients with COVID-19: A Nationwide Multicenter Study
External validation of the diagnostic value of perihematomal edema characteristics in neoplastic and non‐neoplastic intracerebral hemorrhage
Non-contrast computed tomography features predict intraventricular hemorrhage growth
Efficient Large Scale Medical Image Dataset Preparation for Machine Learning Applications
A concurrent, deep learning–based computer-aided detection system for prostate multiparametric MRI: a performance study involving experienced and less-experienced radiologists