European Journal of Surgical Oncology

Predicting response to neoadjuvant therapy using image capture from diagnostic biopsies of oesophageal adenocarcinoma (Nov 2020)

Saqib Rahman, Joseph Early, Matt De Vries, Megan Lloyd, Ben Grace, Gopal Ramchurn, Timothy Underwood

BASO Raven Prize - Best Presentation Winner

The standard of care for locally advanced oesophageal adenocarcinoma is neoadjuvant chemotherapy (NACT) or chemoradiotherapy (NACRT) followed by surgery. Only a minority of patients (<25%) derive significant survival benefit from neoadjuvant treatment and there are no reliable means of establishing prior to treatment in whom this benefit will occur. In this study, we assessed the utility of features extracted from high-resolution digital microscopy of pre-treatment biopsies in predicting response to neoadjuvant therapy in a machine-learning based modelling framework.

arXiv Preprint

Reducing catastrophic forgetting when evolving neural networks (Apr 2019)

Joseph Early

A key stepping stone in the development of an artificial general intelligence (a machine that can perform any task), is the production of agents that can perform multiple tasks at once instead of just one. Unfortunately, canonical methods are very prone to catastrophic forgetting (CF) - the act of overwriting previous knowledge about a task when learning a new task. Recent efforts have developed techniques for overcoming CF in learning systems, but no attempt has been made to apply these new techniques to evolutionary systems. This research presents a novel technique, weight protection, for reducing CF in evolutionary systems by adapting a method from learning systems. It is used in conjunction with other evolutionary approaches for overcoming CF and is shown to be effective at alleviating CF when applied to a suite of reinforcement learning tasks. It is speculated that this work could indicate the potential for a wider application of existing learning-based approaches to evolutionary systems and that evolutionary techniques may be competitive with or better than learning systems when it comes to reducing CF.