Student Profiles

2019 Cohort

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.
Andrew Broad
Research Topic: Can attention-like Mechanisms help in a Semantic understanding of Imaging Data?
Supervisors: Dr Darren Treanor, Dr Marc de Kamps
About Andrew: Andrew graduated in Electronic Engineering at Leeds and retrained as a software engineer. Most recently he worked at LTHT, in the team developing the award-winning patient record system, PPM+.
Project Description: If a patient is suspected of having cancer, a tissue sample may be taken for further investigation. This biopsy is processed and scanned to yield a high-resolution digital image which is examined by a histopathologist. My PhD project will explore whether Artificial Intelligence can support the pathologist in reaching a diagnosis. The problem is that existing AI algorithms would struggle to process such large images quickly and accurately. This project will examine attention in artificial neural networks, to see whether emulating visual attention processes in the human eye and brain can identify areas of the biopsy image that are most likely to contain cancer cells. These regions can then be examined in greater detail, to deliver a more accurate diagnosis more rapidly.
Lucy Godson
Research Topic: Predicting Cancer Outcomes through integration and analysis of molecular, cellular and clinical data
Supervisors: Professor Graham Cook, Dr Ali Gooya
About Lucy: Lucy achieved a 1st class honours degree in Biochemistry with industrial experience at the University of Manchester.
Project Description: Malignant melanoma has a poor prognosis for patients and presents as a highly heterogeneous disease. Therefore, it is hoped that through using different deep learning techniques more can be understood about the disease progression and how it affects different individuals. The project aims to combine medical imaging, genetic, clinical and lifestyle data to gain better biological insight of this complex disease and be able to stratify patients into treatment groups.
Rachael Harkness
Research Topic: Deep learning based Lung Nodule Detection and Cancer risk stratification through the effective Integration of Imaging and Electronic Medical Records (EMRs)
Supervisors: Professor Geoff Hall, Professor Alex Frangi
About Rachael: Rachael graduated with a BSc Hons in Biomedical Science and an MSc in Bioinformatics from Newcastle University.
Project description: Lung cancer is the leading cause of cancer related deaths in the UK with very low five- and ten-year survival rates. This is attributed to the fact that the cancer is typically diagnosed at an advanced stage as early detection is particularly challenging. Consequently, there is a clear need for automated and robust systems that facilitate its early detection, diagnosis and treatment. The goal of this project is to develop a system to improve patient management in the context of both lung cancer screening and incidental nodule discovery. The project objectives are defined to this end: · Develop a fully supervised lung nodule detection framework for automatic assessment of low dose chest CTs. · Develop a cancer risk stratification system based on the official clinical guidelines. · Investigate the use of NLP tools for extracting useful information from electronic medical records (EMRs), for subsequent integration with the image analytics. · Develop a weakly supervised nodule detection algorithm for the effective integration of NLP-derived data from radiology reports with imaging data (low-dose chest CTs).
Sara Jones
Research Topic: Using Behavioural AI for the Early Detection of Brain Cancer
Supervisors: Dr Ryan Matthews, Dr He Wang
About Sara Sara graduated with a BSc in Computing from the University of Worcester in 2018 before pursuing an MSc (Eng.) in Mechatronics and Robotics at the University of Leeds.
Project Description This PhD project aims to explore the feasibility of a custom behavioural AI tool as an early detector of a brain tumour. We propose that since brain cancer is a solid, structural lesion affecting a person’s most functionally eloquent organ, its presence will cause differences in language processing and cognition, and that this can be detected using AI techniques that are adequately trained to detect changes in personality, cognition, language processing (including voice and facial expressions) and prosodic and linguistic features of the patient’s speech. Using natural language processing techniques and Scaled Insights core neural net technology, survey answers, audio samples and transcripts will be analysed to generate markers pertaining to sample syntax, grammar, cohesion, discourse and content described.
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.
Anna-Grace Linton
Research Topic: Predicted Long Term Outcomes following Cancer from early self-evaluated Quality of Survival
Supervisors: Professor Vania Dimitrova, Professor Adam Glaser, Amy Dowling
About Anna: Anna was the first student representative for the UKRI CDT for Artificial Intelligence for Medical Diagnosis and Care. She graduated from University of Bristol with a Masters in Neuroscience.
Project Description: Patient Reported Outcome Measures (PROMs) are patient self-reports which provide information on perceptions and experiences of treatment through Likert scales responses and free text comments. In assessing cancer survival, PROMs are an increasingly valuable tool to investigate the factors that influence the quality of survival of cancer patients. This has implications for long term outcomes such as health service utilisation and mortality. My PhD project will apply Artificial Intelligence (AI) methods to PROMs datasets to produce predictive models for long term outcomes following cancer treatment. This will be enriched by automated analysis of the patients’ unstructured free text comments using natural language processing (NLP). The project will further the understanding of the factors that are key predictors of outcomes of cancer patients and, in turn, improve survival and their quality of life following treatment.
Sam Llanwarne
Research Topic: Using AI to combine genomic with pathology Images to improve Lymphoma Diagnosis
Supervisors: Dr Sharon Barrance, Dr Cathy Burton, Professor David Westhead
About Sam: Sam studied Physics and Astronomy at Durham University followed by a Masters in AI and Computer Science at St Andrews University.
Project Description: In my project I will be looking at lymphoma image and genetic data, with the intention to contribute to the understanding of different sub-types of lymphoma and to improve diagnosis. Lymphoma arises from excessive mutations in lymphocytes. The pathogenic variants often occur during lymphocyte differentiation, where induced variation is crucial. Currently, researchers have defined over 60 sub-types of lymphoma, although these are usually grouped into broader categories. In this project I hope to utilise a range of tools based on deep learning and supervised and unsupervised techniques to identify lymphoma sub-groups and better understand the disease. For this, over 1500 data samples have been made available. Each data sample has genetic data and image data, where each image of the tissue sample is high resolution, featuring microscopic detail, resolving features down to cell nuclei.
Joe Sims
Research Topic: Spatial Data Mining in Histopathology
Supervisors: Dr Heike Grabsch, Dr Derek Magee
About Joe: Before joining the CDT, Joe graduated with a masters in Astrophysics from Liverpool.
Project description If you are diagnosed with stage II/stage IIIb gastric cancer and live Japan, you will most certainly be treated with surgery followed by adjuvant chemotherapy. In many countries, it is common practice to undergo this treatment without question, but what if a subset of patients were able to recover from surgery alone without the addition of chemotherapy? As part of the CLASSIC Trial, hundreds of tissue samples containing tumour were extracted from patients with stage II/stage IIIb gastric cancer, half of which received surgery and the other half received surgery and chemotherapy. Using a combination of deep learning and spatial analytical techniques, we aim to identify the patients who won’t benefit from the addition of chemotherapy and provide a pathological explanation as a result.