Jason Keighley

Research Topic: Can Deep Learning work on Reduced Data Sets? A Case Study on histopathological Data.

Supervisors: Dr Darren Treanor, Alexander Wright, Dr Marc de Kamps

About Jason: Jason completed a BSc in Computer Forensics and Security at Leeds Beckett University and achieved a distinction in an Advanced Computer Science masters at the University of Leeds.

Project description: Digital pathology slides have very high resolutions, increasing neural network layer sizes and training times, with patch learning reducing image context. My research aims to focus on digital pathology slide dimensional reduction using Variational Autoencoders (VAE) and their different varieties. With other focusses on image patch context learning, network parallelisation using cloud services, pathology slide data creation using VAE latent space, transfer learning within latent space and focussing neural networks. Another possible area of research is segmentation, classification and anomaly detection using VAE latent space, by using outlier detection and manipulation techniques. The research will also aim to create documentation on the current state of the research area, including taxonomies of VAEs and other novel techniques.