Research Topic: Transfer learning and transferrable models in Digital Pathology for Gastrointestinal Cancers
Supervisors: Dr Derek Magee, Prof Heike Grabsch, Dr Nicholas P West
About Wilson: I have a background in Computer Science and have been working at the Leeds Institute for Data Analytics as a intern and research assistant since graduating in 2018. My previous work contributed to the development of novel simulation software’s for population projection and forecasting, network analysis with unsupervised machine learning in a comparative study of bike share schemes as well as writing my BSc dissertation on the application of Machine Learning and Data Science for analytics and diagnostics.
Project description: Digital Histopathology is the use of whole slide images of scanned stained tissue sections to diagnose and study diseases such as cancer. One issue with histologic imaging data is that tissue samples and acquired images may vary due to different methods of sample within the same hospital or between different hospitals. Additionally, samples from different parts of the body, different parts of the world, or from patients of different ages can have different appearance. The project aims to investigate and establish methods for automatically adapting models to new data sets including the way in which such unsupervised model transfer/learning could be automatically or semi-automatically validated. The research will have access to a dataset on 5000 patients with oesophagogastric cancer from over 150 hospitals in four countries around the world.