Thomas Allcock

Research Topic: Explainable AI in Digital Pathology: The Role of Visualising Deep Neural Networks in Supporting the Augmented Pathologist

Supervisors: Dr Rebecca Brannan, Dr Andy Hanby, Professor Andy Bulpitt

About Tom: Tom completed undergraduate and masters degrees in Physics at Nottingham.

Project Description: Breast cancer is the most common cancer in the UK, affecting 54,500 women in 2016. The diagnosis of breast cancer is performed by pathologists who are under pressure with an increase in the volume and complexity of the work, and a lack of qualified doctors available to do it. Digital pathology offers a solution, by allowing pathologists to use a computer to view pathology slides and the possibility of using artificial intelligence to improve the diagnostic accuracy and speed of breast cancer diagnosis. In order for humans to trust such methods, we need to address their explainability. This project aims to create diagnostic models that are able to summarize the reasons for behaviour, gain the trust of users, and produce insights about the causes of their decisions when diagnosing breast cancer.