Usama Zidan is a Postdoctoral Researcher at the Nuffield Department of Surgical Sciences (NDS), University of Oxford, focusing on applying deep learning and machine learning to medical image analysis. His work is particularly focused on advancing research in image segmentation and image-to-image translation, with applications aimed at improving diagnostic accuracy and personalized healthcare. As part of his role, he collaborates with leading experts, including Professor Regent Lee and Professor Vicente Grau, on projects such as the Oxford Abdominal Aortic Aneurysm (OxAAA) study, which seeks to integrate AI technologies for improving the assessment and management of abdominal aortic aneurysms.

Prior to his current position, he completed a PhD in Computing at Birmingham City University. His doctoral research primarily addressed challenges in medical image classification and segmentation. He developed novel approaches to tackle class imbalance in datasets and introduced transformer-based architectures for histopathology image analysis.

My research focuses on developing advanced machine learning models, particularly deep learning architectures, for image analysis and medical applications. This research is crucial as it enables automated, accurate, and scalable solutions to complex problems in healthcare and other domains, improving efficiency and outcomes across industries.

I design and implement novel algorithms to analyse large-scale datasets, specifically using supervised and unsupervised machine learning techniques. My current projects involve image segmentation and image-to-image translation, with applications in medical imaging. The impact of my research extends to advancing the use of artificial intelligence in critical fields like radiology, where my work supports automated CT/X-ray analysis, improving diagnostic accuracy and supporting clinicians in making timely, informed decisions.