We develop machine learning (ML) methods for medical image interpretation and analysis, driven by two main aims:

• To develop more reliable and transparent machine learning models to catalyse safer integration of the technology in real-world applications.
• To facilitate clinical research in a variety of applications (segmentation, detection, reconstruction, etc) and imaging modalities (MRI, CT, Mammography, X-rays, etc).

Therefore we investigate a variety of methodologies such as:

• State-of-the-art neural networks for image understanding and analysis
• Estimating model uncertainty or detecting potential failure of prediction for safe ML (due to corrupted input, unknown pathology, etc)
• Identifying and alleviating bias in a model for fair ML in healthcare (domain adaptation, causality, etc)
• Learning from decentralised data to enable international collaborations (federated learning, etc)
• How to learn useful information from unlabelled data, multi-modal data, and more.