Area of research
Computer Vision and Pattern Recognition · Pathology and Forensic Medicine
Research interest
Research interests include Medical Image Segmentation Techniques, Multiple Sclerosis Research Studies, Brain Tumor Detection and Classification, and Radiomics and Machine Learning in Medical Imaging.
Improving multiple sclerosis lesion segmentation across clinical sites: A federated learning approach with noise-resilient training
A real-world clinical validation for AI-based MRI monitoring in multiple sclerosis
Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies
Validation of Deep Learning Techniques for Quality Augmentation in Diffusion MRI for Clinical Studies
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation – Analysis of Ranking Scores and Benchmarking Results
Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applications
Deep Learning in Forestry Using UAV-Acquired RGB Data: A Practical Review
Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure