Intelligence applied

Machine learning (ML) has enormous potential to act as a diagnostic adjunct within gynaecology to tackle two main issues: diagnostic uncertainty and delayed diagnoses.

We use computer vision techniques to analyse cardiac imaging in both health and disease. We use these models to understand the genetic basis of heart failure and to predict future health outcomes.

This initiative aims at using different frequency signals to achieve Synthetic UPsampling ARray (SUPAR) to improve angular resolution in multitude. Artificial intelligence (AI) tools will be employed for real time probing design and imaging.

“Human and machine auditory perception” Research into intelligent spatial audio and human perception using machine learning, neuroscience, signal processing, and applied mathematics.

The Research Initiative will focus on combining novel computational imaging methods with AI and deep learning techniques to address previously unsolvable or analytically intractable inverse problems in diverse fields such as realistic computer graphics, computer vision, signal processing, medical imaging and natural/physical sciences.

This initiative brings together researchers and scientists in the field of machine learning based image analysis. The aim is to develop new computational tools for improving image-based detection and diagnosis of disease.