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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

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)

21 September 2023

PhD position available on the CDT in Smart Medical ImagingFirst, do no harm: Developing fair AI techniques for medical imaging. This is for a February 2024 start and is open to UK, EU/EEA/Swiss and International applicants. Deadline for applications is 20 October 2023. Apply here.

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 Health2023. (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. (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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