Area of research
Artificial Intelligence · Oncology
Research interest
Research interests include AI in cancer detection, Cutaneous Melanoma Detection and Management, Cell Image Analysis Techniques, and Radiomics and Machine Learning in Medical Imaging.
Sex Differences in Cancer Immunotherapy—Clinical Evidence and Mechanisms With a Focus on NSCLC
Celebrating Ulrik Ringborg: Multi-Omics-Based Patient Stratification for Precision Cancer Treatment
Clinical benefit of additional whole-exome sequencing over panel sequencing in an all-comer real-world molecular tumor board
Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma
Federated Learning for Decentralized Artificial Intelligence in Melanoma Diagnostics
Prospective multicenter study using artificial intelligence to improve dermoscopic melanoma diagnosis in patient care
Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology: A three center study
Patients’ and dermatologists’ preferences in artificial intelligence–driven skin cancer diagnostics: A prospective multicentric survey study
Predicting benefit from PARP inhibitors using deep learning on H&E-stained ovarian cancer slides
Using multiple real-world dermoscopic photographs of one lesion improves melanoma classification via deep learning
Evaluation of a medical student-delivered smoking prevention program utilizing a face-aging mobile app for secondary schools in Germany: The Education Against Tobacco cluster-randomized controlled trial
A self-supervised vision transformer to predict survival from histopathology in renal cell carcinoma
Multimodal integration of image, epigenetic and clinical data to predict BRAF mutation status in melanoma
Deep learning to predict breast cancer sentinel lymph node status on INSEMA histological images
Colorectal cancer risk stratification on histological slides based on survival curves predicted by deep learning
Deep Learning to Predict Breast Cancer Sentinel Lymph Node Status on Insema Histological Images
Explainable artificial intelligence in skin cancer recognition: A systematic review
Uncertainty Estimation in Medical Image Classification: Systematic Review
Deep learning can predict survival directly from histology in clear cell renal cell carcinoma
Model soups improve performance of dermoscopic skin cancer classifiers
Künstliche Intelligenz auf dem Vormarsch – Hohe Vorhersage-Genauigkeit bei der Früherkennung pigmentierter Melanome
Response to letter entitled: Re: Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review
Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts
Gastrointestinal cancer classification and prognostication from histology using deep learning: Systematic review
Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification
Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review
Deep learning can predict lymph node status directly from histology in colorectal cancer
Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer
Diagnostic performance of artificial intelligence for histologic melanoma recognition compared to 18 international expert pathologists