Area of research
Artificial Intelligence · Oncology
Research interest
Research interests include AI in cancer detection, Cutaneous Melanoma Detection and Management, Protease and Inhibitor Mechanisms, and Radiomics and Machine Learning in Medical Imaging.
Federated Learning for Decentralized Artificial Intelligence in Melanoma Diagnostics
Using multiple real-world dermoscopic photographs of one lesion improves melanoma classification via deep learning
A self-supervised vision transformer to predict survival from histopathology in renal cell carcinoma
Deep learning to predict breast cancer sentinel lymph node status on INSEMA histological images
Deep Learning to Predict Breast Cancer Sentinel Lymph Node Status on Insema Histological Images
Deep learning can predict survival directly from histology in clear cell renal cell carcinoma
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
Deep learning approach to predict sentinel lymph node status directly from routine histology of primary melanoma tumours
Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study
Integrating Patient Data Into Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review
Reducing the Impact of Confounding Factors on Skin Cancer Classification via Image Segmentation: Technical Model Study
Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective
Artificial Intelligence and Its Effect on Dermatologists’ Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study
Effects of Label Noise on Deep Learning-Based Skin Cancer Classification
Overdiagnosis of melanoma – causes, consequences and solutions
Reply to the letter to the editor: ‘Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images’
Überdiagnose von Melanomen – Ursachen, Konsequenzen und Lösungsansätze
Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images