Area of research
Radiology, Nuclear Medicine and Imaging · Artificial Intelligence
Research interest
Research interests include Privacy-Preserving Technologies in Data, Radiomics and Machine Learning in Medical Imaging, COVID-19 diagnosis using AI, and Artificial Intelligence in Healthcare and Education.
FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Neural invasion severity is a strong predictor of local recurrence in pancreatic ductal adenocarcinoma
Local and Global Patterns Support Medical Imaging as a Biomarker of Ageing
Reconciling privacy and accuracy in AI for medical imaging
Heterogeneity-driven phenotypic plasticity and treatment response in branched-organoid models of pancreatic ductal adenocarcinoma
Prognostic value of deep learning-derived body composition in advanced pancreatic cancer—a retrospective multicenter study
Development of an image-based Random Forest classifier for prediction of surgery duration of laparoscopic sigmoid resections
Fair and Private CT Contrast Agent Detection
Encrypted federated learning for secure decentralized collaboration in cancer image analysis
Propagation and Attribution of Uncertainty in Medical Imaging Pipelines
The Liver Tumor Segmentation Benchmark (LiTS)
sPLINK: a hybrid federated tool as a robust alternative to meta-analysis in genome-wide association studies
Algorithmic transparency and interpretability measures improve radiologists’ performance in BI-RADS 4 classification
Encrypted federated learning for secure decentralized collaboration in cancer image analysis
Author Correction: Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study
Functional biomarkers derived from computed tomography and magnetic resonance imaging differentiate PDAC subgroups and reveal gemcitabine-induced hypo-vascularization
Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study
Hyperpolarized 13C pyruvate magnetic resonance spectroscopy for in vivo metabolic phenotyping of rat HCC
Künstliche Intelligenz und maschinelles Lernen in der onkologischen Bildgebung
Author Correction: Hyperpolarized 13C pyruvate magnetic resonance spectroscopy for in vivo metabolic phenotyping of rat HCC
Joint Imaging Platform for Federated Clinical Data Analytics
Deep Convolutional Neural Network-Assisted Feature Extraction for Diagnostic Discrimination and Feature Visualization in Pancreatic Ductal Adenocarcinoma (PDAC) versus Autoimmune Pancreatitis (AIP)
Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma
Implementing cell-free DNA of pancreatic cancer patient–derived organoids for personalized oncology
Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic and Image-Derived Parameters
Künstliche Intelligenz und maschinelles Lernen in der onkologischen Bildgebung
A machine learning algorithm predicts molecular subtypes in pancreatic ductal adenocarcinoma with differential response to gemcitabine-based versus FOLFIRINOX chemotherapy
A machine learning algorithm predicts molecular subtypes in pancreatic ductal adenocarcinoma with differential response to gemcitabine-based versus FOLFIRINOX chemotherapy
Wie funktioniert maschinelles Lernen?
Wie funktioniert Radiomics?