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MOTION MODELLING & ANALYSIS GROUP

Artificial intelligence for medical image analysis

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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 updates

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. (Arxiv paper)

14 November 2022

PhD studentship available through the Smart Imaging CDT - application details, application deadline 7 December 2022 - First, do no harm: Developing fair AI techniques for medical imaging, supervised by Andrew King, Claudia Prieto and Bram Ruijsink - project details

6 November 2022

New journal paper by M. Ruthven, et al, "A Segmentation-informed Deep Learning Framework to Register Dynamic Two-dimensional Magnetic Resonance Images of the Vocal Tract During Speech", Biomedical Signal Processing and Control, 2022. (paper)

14 October 2022

Congratulations to Maram Alqarni for winning the prize for best poster/presentation at the UK & Ireland Conference on Prostate Brachytherapy!

1 October 2022

Welcome to new PhD students Hasara Wickremasinghe, Iman Islam and Tom Young!

13 September 2022

Congratulations to Nick Byrne for passing his PhD viva!

8 September 2022

New MICCAI workshop papers now have Arxiv papers online:

  • G. Morilhat, N. Kifle, S. FinesilverSmith, B. Ruijsink, V. Vergani, Habtamu T. D., Zerubabel T. D., E. Puyol-Antón, A. Carass, A. P. King, "Deep Learning-based Segmentation of Pleural Effusion From Ultrasound Using Coordinate Convolutions", Proceedings MICCAI FAIR, 2022. (Arxiv paper)

  • S. Ioannou, H. Chockler, A. Hammers, A. P. King, "A Study of Demographic Bias in CNN-based Brain MR Segmentation", Proceedings MICCAI MLCN, 2022. (Arxiv paper)

  • L. Humbert-Vidan, V. Patel, R. Andlauer, A. P King, T. G. Urbano, "Prediction of Mandibular ORN Incidence From 3D Radiation Dose Distribution Maps Using Deep Learning", Proceedings MICCAI AMAI, 2022. (Arxiv paper)

  • T. Lee, E. Puyol-Antón, B. Ruijsink, M. Shi, A. P. King, "A Systematic Study of Race and Sex Bias in CNN-based Cardiac MR Segmentation", Proceedings MICCAI STACOM, 2022. (Arxiv paper)

  • E. Puyol-Antón, B. Ruijsink, B. S. Sidhu, J. Gould, B. Porter, M. K. Elliott, V. Mehta, H. Gu, C. A. Rinaldi, M. Cowie, P. Chowienczyk, R. Razavi, A. P. King, "AI-enabled Assessment of Cardiac Systolic and Diastolic Function from Echocardiography", Proceedings MICCAI ASMUS, 2022. (Arxiv paper)

  • E. Chan, C. O'Hanlon, C. Asegurado Marquez, M. Petalcorin, J. Mariscal-Harana, H. Gu, R. J. Kim, R. M. Judd, P. Chowienczyk, J. A. Schnabel, R. Razavi, A. P. King, B. Ruijsink, E. Puyol-Antón, "Automated Quality Controlled Analysis of 2D Phase Contrast Cardiovascular Magnetic Resonance Imaging", Proceedings MICCAI STACOM, 2022. (Arxiv paper)

2 September 2022

New journal paper by N. Byrne, et al, "A Persistent Homology-based Topological Loss for CNN-based Multi-class Segmentation of CMR", IEEE Transactions on Medical Imaging, 2022. (paper)

27 April 2022

New journal paper by E. Puyol-Antón, et al, "A Multimodal Deep Learning Model for Cardiac Resynchronisation Therapy Response Prediction", Medical Image Analysis, 2022. (open access paper)

CONTACT US

5th Floor Becket House,

1 Lambeth Palace Road,

London,

SE1 7EU,

United Kingdom.

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