MOTION MODELLING & ANALYSIS GROUP
Artificial Intelligence for Medical Image Analysis
About us
The Motion Modelling and Analysis Group (MMAG) is an academic research group based within the School of Biomedical Engineering and Imaging Sciences of King's College London. The group is based at St. Thomas' Hospital in central London. The MMAG includes researchers from a diverse range of backgrounds and is proud to foster an inclusive research environment to allow all to thrive and reach their potential.
The MMAG has worked in the past on the imaging, modelling and estimation of repetitive motion. Currently, the group works more widely on the use of artificial intelligence (specifically machine and deep learning) for the analysis of imaging data with the aim of extracting clinically useful information. Cardiology is one of the main application areas but the group is also involved in developing machine learning solutions for other clinical application areas, such as radiotherapy and vocal tract imaging. The aim is to develop novel but clinically-driven machine learning solutions and translate them into equitable patient benefit.
Latest news
17 February 2024: PhD position available on Fairness in Brain MR Image Analysis. This 4-year scholarship is available through the King's College London DRIVE-Health CDT. See the DRIVE-Health web site for project details (Project #88) and entry requirements/how to apply. Application deadline: Sunday 10th March at 23:59 GMT.
2 January 2024: New blog post on the future for Fair AI by Andrew King.
2 December 2023: New journal paper and speech MRI dataset released by Matthieu Ruthven:
M. Ruthven, A. M. Peplinski, D. M. Adams, A. P. King, M. E. Miquel, "Real-time Speech MRI Datasets With Corresponding Articulator Ground-truth Segmentations", Scientific Data, 2023. (paper)
3 October 2023: New journal paper by Ines Machado now available:
I. Machado, E. Puyol-Antón, K. Hammernik, G. Cruz, D. Ugurlu, I. Olakorede, I. Oksuz, B. Ruijsink, M. Castelo-Branco, A. Young, C. Prieto, J. Schnabel, A. P. King, "A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis", IEEE Transactions on Biomedical Engineering, 2023. (paper)
2 October 2023: New Arxiv papers available for three papers to be presented at the MICCAI Workshop on Fairness of AI in Medical Imaging (FAIMI):
M. Huti, T. Lee, E. Sawyer, A. P. King, "An Investigation Into Race Bias in Random Forest Models Based on Breast DCE-MRI Derived Radiomics Features", Proceedings MICCAI FAIMI, 2023. (Arxiv paper)
C. I. Bercea, E. Puyol-Antón, B. Wiestler, D. Rueckert, J. A. Schnabel, A. P. King, "Bias in Unsupervised Anomaly Detection in Brain MRI", Proceedings MICCAI FAIMI, 2023. (Arxiv paper)
T. Lee, E. Puyol-Antón, B. Ruijsink, K. Aitcheson, M. Shi, A. P. King, "An Investigation Into the Impact of Deep Learning Model Choice on Sex and Race Bias in Cardiac MR Segmentation", Proceedings MICCAI FAIMI, 2023. (Arxiv paper)
7 August 2023: New section on this web site on Talks given by group members!
21 July 2023: New journal paper by Jorge Mariscal-Harana now available online:
J. Mariscal-Harana, C. Asher, V. Vergani, M. Rizvi, L. Keehn, R. J. Kim, R. M. Judd, S. E. Petersen, R. Razavi, A. P. King, B. Ruijsink, E. Puyol-Antón, "An AI Tool for Automated Analysis of Large-scale Unstructured Clinical Cine CMR Databases", European Heart Journal Digital Health, 2023. (paper, see also press release)
3 July 2023: New journal paper by Nhat Phung now available online:
P. T. H. Nhat, N. V. Hao, P. V. Tho, H. Kerdegari, L. Pisani, L. N. M. Thu, L. T. Phuong, H. T. H. Duong, D. B. Thuy, A. McBride, M. Xochicale, M. J. Schultz, R. Razavi, A. P. King, L. Thwaites, N. V. V. Chau, S. Yacoub, VITAL Consortium & A. Gomez, "Clinical Benefit of AI-assisted Lung Ultrasound in a Resource-limited Intensive Care Unit", Critical Care, 2023. (paper)
6 June 2023: New journal paper by Tareen Dawood now available online:
T. Dawood, C. Chen, B. S. Sidhu, B. Ruijsink, J. Gould, B. Porter, M. K. Elliot, V. Mehta, C. A. Rinaldi, E. Puyol-Antón, R. Razavi, A. P. King, "Uncertainty Aware Training to Improve Deep Learning Model Calibration for Classification of Cardiac MR Images", Medical Image Analysis, 2023. (paper)
4 May 2023: New conference paper by Shaheim Ogbomo-Harmitt now available online:
S. Ogbomo-Harmitt, J. Grzelak, A. Qureshi, A. P. King, O. Aslanidi, "TESSLA: Two-Stage Ensemble Scar Segmentation for the Left Atrium", Proceedings MICCAI STACOM Challenge on Left Atrial and Scar Quantification and Segmentation, 2023. (paper)
6 February 2023: New conference paper by Tareen Dawood accepted at ISBI 2023:
T. Dawood, E. Chan, R. Razavi, A. P. King, E. Puyol-Antón, "Addressing Deep Learning Model Calibration Using Evidential Neural Networks and Uncertainty-Aware Training", Proceedings ISBI, 2023. (paper)