Konstantinos Kamnitsas is Associate Professor of Engineering Science (Biomedical Imaging) at the Department of Engineering Science, and a Non-Tutorial Fellow at Wolfson College. His research focuses on machine-learning (ML) and primarily deep neural networks for medical image analysis. His work has two main goals:
a) Empower researchers and clinicians with ML methods and tools to better address their clinical research questions and needs of clinical workflows;
b) Develop more reliable, transparent and accountable models for safer use in real-world clinical applications.
Konstantinos completed his PhD in 2019 at Imperial College London, where he developed some of the first 3-dimensional neural networks for processing volumetric medical data, such as MRI and CT, and methods for improving generalization to heterogeneous data. His work has won various awards, among which two international competitions for brain cancer and ischemic stroke lesion segmentation. He also obtained an MSc in Computing Science in 2013, also from Imperial College, and the diploma in Electrical and Computer Engineering in 2010 from Aristotle University of Thessaloniki, Greece. He has also spent time conducting research in industry, such as at the Healthcare Intelligence team of Microsoft Research and Kheiron Medical Technologies.
He became a Lecturer in 2021 at the School of Computer Science of the University of Birmingham, where he retains a position as an Honorary Research Fellow since 2022, when he joined the University of Oxford as an Associate Professor.
View a list of Professor Kamnitsas’ publications on Google Scholar.
We develop machine learning (ML) methods for medical image interpretation and analysis, driven by two main aims:
- To facilitate clinical research in a variety of applications (segmentation, detection, reconstruction, etc) and imaging modalities (MRI, CT, Mammography, X-rays, etc).
- To develop more reliable and transparent machine learning models to catalyse safer integration of the technology in real-world applications.
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.
Interested in collaborating with us?
We are developing machine learning methods and tools to facilitate clinical research in a variety of applications. Do you have an interesting clinical research question and you think our algorithms could help? Then please do not hesitate to email us to discuss.
If you are interested in studying for a DPhil in Engineering Science in any of our areas of research, please email Dr Konstantinos Kamnitsas to discuss the possibility. Please insert “[DPhil EngSci KK]” in your email subject.