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Research projects

IVC Atract logo

ATRACT: A Trustworthy Autonomous system to support Casualty Triage

Dates: April 2023 – March 2026

Funding amount and source: £869,031 from Engineering & Physical Sciences Research Council (EPSRC)

People involved: Prof Ardhendu Behera

Description: The project aims to develop drone-based active sensing and simultaneous detection and monitoring of injured soldiers on a battlefield for rapid effective prioritisation in a trustworthy manner before the evacuation helicopter arrives. Beyond the battlefield, it can be adapted to civilian applications including search and rescue, ambulance emergency, and other multiple-casualty disaster situations. Its trustworthiness will be achieved by considering the legal, ethical, system, and human elements embedded in the design, development, and testing phases.

Academic partners: Loughborough University LondonUniversity of Brighton and University of Portsmouth

Lung Health AI

LungHealth AI: Using AI to improve early detection and diagnosis of lung cancer

Dates: November 2022 – March 2024

Funding amount and source: £75,000 from Cancer Research UK (CRUK)

People involved: Prof Ardhendu Behera

Description: Lung cancer is the 3rd most common cancer in the UK, accounting for 13% of all new cancer cases with abysmal survival and a large unmet clinical need. This dismal survival statistics could be improved through early detection and diagnosis which may allow more treatment options. Low-dose CT screening for lung cancer can facilitate early detection. However, several challenges remain including the unmet need of how best to select subjects to screen in a cost-effective manner due to a lack of established and validated tools. Our goal is to develop a ‘proof-of-concept’ involving an artificial intelligence (AI) system aiming to transform the early diagnosis of lung cancer, using longitudinal data (demographic, lifestyle, HES, etc.) to discover the hidden spatiotemporal evolving patterns within multimodal data. Our hypothesis is that such ‘hidden patterns’ perhaps not visible to the human eye, that are responsible for transforming pre-diagnosis to diagnosis. We hope to answer to ‘what’ patterns/relations and in ‘whom’ and ‘when’ this transformation is likely to occur.

Academic partners: University of Liverpool

AR Immersive logo

Augmented Reality (AR) to support the design and delivery of AR immersive learning spaces

Dates: November 2023 – October 2026

Funding amount and source: £205,332 from Innovate UK

People involved: Prof Ardhendu Behera, Prof Yonghuai Liu and Dr Peter Matthew

Description: Over the course of the KTP, Gener8 will collaborate with the University to put in place the core AR development frameworks, infrastructure and methodologies to be able to rapidly and repeatedly specify, develop, test and deploy AR-enriched learning simulations to customers. In establishing and embedding this ‘blueprint’, the KTP will also test, generate and commercialise a number of ‘fore-runner’ solutions which rely on different branch specialisms of AR and would be applicable for use in different learning environments. Executing this project represents a major shift in practice and culture within Gener8, in terms of their software development. The AR developer will create a layer of digital experience and be able to work with video game engines.

Industrial partner: Gener8 Spaces Ltd.


PANC-CYS-GAN: A Multimodal Longitudinal Generative Adversarial Network (GAN) to Discriminate High-risk Cysts for the Early Detection of Pancreatic Cancer

Dates: November 2021 – March 2023

Funding amount and source: £100,000, Cancer Research UK (CRUK), Pancreatic Cancer UK (PCUK), and Engineering & Physical Sciences Research Council (EPSRC) UK.

People involved: Prof Ardhendu Behera

Description: There have been major advances in AI in the last five years, and their impact is already visible in the entertainment, automotive and manufacturing industries, but its transformative potential in early detection of PC is yet to be realized. PANC-CYS-GAN aims to address this bottleneck by developing innovative computational analytics that can learn PC-related latent representation of messy and complicated distributions of longitudinal multimodal data. It aims to provide a range of short- and long-term measurable global benefits harnessing UK’s positioning in health-informatics and artificial intelligence at the world stage to develop health technology emphasising holistic approach. Information such as, but not limited to, demographic, lifestyle, medical conditions, pathology and blood/urine tests are routinely collected. We hope by integrating host of such routinely collected information with medical imaging data through a Generative Adversarial (GAN) model we can potentially help in triaging patients better.

Academic Partners: Quenn Mary University of London (QMUL); University College London (UCL); University of Hertfordshire and Manchester Metropolitan University (MMU)

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