Area of research
Radiology, Nuclear Medicine and Imaging · Artificial Intelligence
Research interest
Research interests include Radiomics and Machine Learning in Medical Imaging, AI in cancer detection, Artificial Intelligence in Healthcare and Education, and Medical Imaging and Analysis.
Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence
Evaluating the effectiveness of biomedical fine-tuning for large language models on clinical tasks
Unlocking the potential of digital pathology: Novel baselines for compression
AutoPET Challenge on Fully Automated Lesion Segmentation in Oncologic PET/CT Imaging, Part 2: Domain Generalization
Leveraging Sarcopenia index by automated CT body composition analysis for pan cancer prognostic stratification
Contrastive virtual staining enhances deep learning‐based <scp>PDAC</scp> subtyping from H&E‐stained tissue cores
Abstract: Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting
Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo
Learned Image Compression for HE-Stained Histopathological Images via Stain Deconvolution
Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting
Back to the Future: Challenges of Sparse and Irregular Medical Image Time Series
Metrics reloaded: recommendations for image analysis validation
CellViT: Vision Transformers for precise cell segmentation and classification
Understanding metric-related pitfalls in image analysis validation
Privacy-preserving large language models for structured medical information retrieval
Medical large language models are susceptible to targeted misinformation attacks
Skeleton Recall Loss for Connectivity Conserving and Resource Efficient Segmentation of Thin Tubular Structures
Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy
Results from the autoPET challenge on fully automated lesion segmentation in oncologic PET/CT imaging
Prognostic value of deep learning-derived body composition in advanced pancreatic cancer—a retrospective multicenter study
Current State of Community-Driven Radiological AI Deployment in Medical Imaging
Real-world federated learning in radiology: hurdles to overcome and benefits to gain
Enhanced Data Augmentation Using Synthetic Data for Brain Tumour Segmentation
AI-derived body composition parameters as prognostic factors in patients with HCC undergoing TACE in a multicenter study
Early versus late response to PD-1-based immunotherapy in metastatic melanoma
Value of MRI - T2 Mapping to Differentiate Clinically Significant Prostate Cancer
From Text to Tables: A Local Privacy Preserving Large Language Model for Structured Information Retrieval from Medical Documents
An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids
Das Netzwerk Universitätsmedizin: Technisch-organisatorische Ansätze für Forschungsdatenplattformen
Understanding metric-related pitfalls in image analysis validation