Jason Keighley
- Email: ll13jjk@leeds.ac.uk
- Thesis title: Can Deep Learning work on Reduced Data Sets? A Case Study on histopathological Data.
- Supervisor: Dr Marc de Kamps, Dr Alexander Wright, Dr Darren Treanor
Profile
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.
Research interests
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 focuses 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 including normalising flows.