Muhammad Moazam Fraz completed his PhD (Computer Science) from Kingston University, London in 2013. His area of research is applied deep learning, retinal image analysis, computer vision and pattern recognition. After completing his PhD, he worked as a research fellow at Kingston University in collaboration with St George’s University of London and UK BioBank on the development of an automated software tool for epidemiologists to quantify and measure retinal vessels morphology and size; determine the width ratio of arteries and veins as well as the vessel tortuosity index on very large datasets, to enable them to link systemic and cardiovascular disease to the retinal vessel characteristics.
He has successfully supervised several Master’s theses. Prior to his PhD, he worked as a software development engineer at Elixir Technologies Corporation, a California-based software company. He served with Elixir from 2003-2010 in various roles and capacities. He now works as an Assistant Professor at the School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad, Pakistan.
Deep learning-based histopathological analytics for oral cancer detection and estimation. The aim is to develop novel deep learning methods for the analysis of whole-slide images (WSI) of oral cancer tissue slides, particularly for segmentation of tumour-rich regions in these images and quantification of important histological patterns (such as vascular invasion and perineural invasion).
Accurate identification and quantification of histological patterns and tumour areas in the oral cancer WSIs are crucial for determining the cancer grade in an objective manner and for better, systematic stratification of patients for personalised treatment of cancer. This fellowship will contribute towards world-leading expertise in the area of cancer image analytics at the Turing, with health and wellbeing as the main targeted application area of the research activity.