Area of research
Radiology, Nuclear Medicine and Imaging · Pharmacology
Research interest
Research interests include Radiomics and Machine Learning in Medical Imaging, Microbial Natural Products and Biosynthesis, AI in cancer detection, and Traditional and Medicinal Uses of Annonaceae.
Unlocking the potential of digital pathology: Novel baselines for compression
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
Machine Learning-Based Radiomics for Bladder Cancer Staging: Evaluating the Role of Imaging Timing in Differentiating T2 from T3 Disease
Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics
Radiomics and Clinicopathological Characteristics for Predicting Lymph Node Metastasis in Testicular Cancer
Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning
CT Radiomics and Clinical Feature Model to Predict Lymph Node Metastases in Early-Stage Testicular Cancer
CT Radiomics and Clinical Feature Model to Predict Lymph Node Metastases in Early-Stage Testicular Cancer
RPTK: The Role of Feature Computation on Prediction Performance
Machine Learning Classifiers for Predictive Biomarkers Combining Clinical and Radiomic Data in Testicular Cancer
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
Deep Neural Networks and Machine Learning Radiomics Modelling for Prediction of Relapse in Mantle Cell Lymphoma
Longitudinal CT Imaging to Explore the Predictive Power of 3D Radiomic Tumour Heterogeneity in Precise Imaging of Mantle Cell Lymphoma (MCL)
DICOM Whole Slide Imaging for Computational Pathology Research in Kaapana and the Joint Imaging Platform
Deep Learning on Lossily Compressed Pathology Images: Adverse Effects for ImageNet Pre-trained Models
P-018: Automatic analysis of magnetic resonance imaging in multiple myeloma patients: deep-learning based pelvic bone marrow segmentation and radiomics analysis for prediction of plasma cell infiltration
Abstract: Data Augmentation for Information Transfer
The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping
Optimal Statistical Incorporation of Independent Feature Stability Information into Radiomics Studies
Pre-examinations Improve Automated Metastases Detection on Cranial MRI
Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values
PO-0981: Results from the Image Biomarker Standardisation Initiative
Correlation between genomic index lesions and mpMRI and 68Ga-PSMA-PET/CT imaging features in primary prostate cancer
Early postoperative delineation of residual tumor after low-grade glioma resection by probabilistic quantification of diffusion-weighted imaging
Abstract: Physiological Parameter Estimation from Multispectral Images Unleashed
Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma
Prediction of malignancy by a radiomic signature from contrast agent‐free diffusion MRI in suspicious breast lesions found on screening mammography.
Training mit positiven und unannotierten Daten für automatische Voxelklassifikation