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Department
  • Psychiatry
  • Research Focus

    Keywords

  • Deep Learning
  • Deep Neural Networks
  • Machine Learning
  • Artificial Intelligence
  • Uncertainty
  • Brain Image Processing
  • Brain Mapping
  • Brain Segmentation
  • Brain Tumours
  • Brain Development
  • Clinical Conditions

  • Bipolar disorder
  • Schizophrenia
  • Brain cancer
  • Equipment & Techniques

  • Magnetic resonance imaging (MRI)
  • Statistical analysis
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    Dr Michail (Mike) Mamalakis

    (he/him/his)
    University Position
    Research Associate
    Dr Michail (Mike) Mamalakis is pleased to consider applications from prospective PhD students.

    Interests

    My research aims to optimize simultaneous prediction of patients' survival risk and overall well-being using advanced techniques like graph neural networks trained on genomics data and biomarkers linked to brain region heterogeneity. I leverage deep learning computer vision methods (e.g., convolutional networks, transformers) applied to MRI data. Trustworthy AI systems require explainability and accurate prediction uncertainty estimation. I integrate Explainable AI (XAI) techniques to validate and enhance interpretability, increasing trust in deep learning models. My MRC grant role focuses on variable pattern learning in sulcal patterns for schizophrenic and bipolar patients.

    3D eXplainability Artificial Intelligence

    The 3D explainable framework that provides both local and global interpretations and explanations of our deep learning 3D classification network's results. The ratio of the aithfulness and complexity metrics are compute in that stage. In this example we include only the GradCam explainability method just for simplicity.

    Key Publications

    Publications

    Improving Prognostication in Pulmonary Hypertension Using AI-quantified Fibrosis and Radiologic Severity Scoring at Baseline CT.

    DOI: http://doi.org/10.1148/radiol.231718
    Journal: Radiology
    E-pub date: 1 Feb 2024
    Authors: K Dwivedi, M Sharkey, L Delaney, S Alabed, S Rajaram, C Hill, C Johns, A Rothman, M Mamalakis, AAR Thompson, J Wild, R Condliffe, DG Kiely, AJ Swift

    Intra-operative applications of augmented reality in glioma surgery: a systematic review.

    DOI: http://doi.org/10.3389/fsurg.2023.1245851
    Journal: Front Surg
    E-pub date: 1 Aug 2023
    Authors: A Ragnhildstveit, C Li, MH Zimmerman, M Mamalakis, VN Curry, W Holle, N Baig, AK Uğuralp, L Alkhani, Z Oğuz-Uğuralp, R Romero-Garcia, J Suckling

    A transparent artificial intelligence framework to assess lung disease in pulmonary hypertension.

    DOI: http://doi.org/10.1038/s41598-023-30503-4
    Journal: Sci Rep
    E-pub date: 7 Mar 2023
    Authors: M Mamalakis, K Dwivedi, M Sharkey, S Alabed, D Kiely, AJ Swift

    Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease.

    DOI: http://doi.org/10.3390/medsci11010020
    Journal: Med Sci (Basel)
    E-pub date: 24 Feb 2023
    Authors: G Doolub, M Mamalakis, S Alabed, RJ Van der Geest, AJ Swift, JCL Rodrigues, P Garg, NV Joshi, A Dastidar

    Automatic development of 3D anatomical models of border zone and core scar regions in the left ventricle.

    DOI: http://doi.org/10.1016/j.compmedimag.2022.102152
    Journal: Comput Med Imaging Graph
    E-pub date: 1 Jan 2023
    Authors: M Mamalakis, P Garg, T Nelson, J Lee, AJ Swift, JM Wild, RH Clayton

    A robust COVID-19 mortality prediction calculator based on Lymphocyte count, Urea, C-Reactive Protein, Age and Sex (LUCAS) with chest X-rays.

    DOI: http://doi.org/10.1038/s41598-022-21803-2
    Journal: Sci Rep
    E-pub date: 29 Oct 2022
    Authors: S Ray, A Banerjee, A Swift, JW Fanstone, M Mamalakis, B Vorselaars, C Wilkie, J Cole, LS Mackenzie, S Weeks

    Validation of Artificial Intelligence Cardiac MRI Measurements: Relationship to Heart Catheterization and Mortality Prediction.

    DOI: http://doi.org/10.1148/radiol.229014
    Journal: Radiology
    E-pub date: 1 Sep 2022
    Authors: S Alabed, F Alandejani, K Dwivedi, K Karunasaagarar, M Sharkey, P Garg, PJH de Koning, A Tóth, Y Shahin, C Johns, M Mamalakis, S Stott, D Capener, S Wood, P Metherall, AMK Rothman, R Condliffe, N Hamilton, JM Wild, DP O'Regan, H Lu, DG Kiely, RJ van der Geest, AJ Swift

    Fully automatic cardiac four chamber and great vessel segmentation on CT pulmonary angiography using deep learning.

    DOI: http://doi.org/10.3389/fcvm.2022.983859
    Journal: Front Cardiovasc Med
    E-pub date: 1 Aug 2022
    Authors: MJ Sharkey, JC Taylor, S Alabed, K Dwivedi, K Karunasaagarar, CS Johns, S Rajaram, P Garg, D Alkhanfar, P Metherall, DP O'Regan, RJ van der Geest, R Condliffe, DG Kiely, M Mamalakis, AJ Swift

    Deep Learning Approaches to Classify Lung Parenchymal Disease on CT Images

    DOI: http://doi.org/10.1164/ajrccm-conference.2022.205.1_meetingabstracts.a5431
    Journal: D106. NOE VALLEY: CLOTS, COVID, AND LUNG VASCULAR DISEASES
    E-pub date: 1 May 2022
    Authors: M Mamalakis, K Dwivedi, M Sharkey, S Alabed, P Metherall, D Kiely, A Swift

    Computed Tomography (CT) Features Are of Diagnostic Utility in Pre Diagnosis Idiopathic Pulmonary Arterial Hypertension (IPAH): A Case Controlled Study

    DOI: http://doi.org/10.1164/ajrccm-conference.2022.205.1_meetingabstracts.a3605
    Journal: B106. UNION SQUARE: OBSERVATIONAL STUDIES AND CLINICAL TRIALS IN PULMONARY HYPERTENSION
    E-pub date: 1 May 2022
    Authors: K Dwivedi, R Lewis, R Condliffe, MJ Sharkey, M Mamalakis, S Alabed, JM Wild, AJ Swift, D Kiely

    Fully Automatic Cardiac and Great Vessel Segmentation on CT Pulmonary Angiography (CTPA) Using Deep Learning

    DOI: http://doi.org/10.1164/ajrccm-conference.2022.205.1_meetingabstracts.a5437
    Journal: D106. NOE VALLEY: CLOTS, COVID, AND LUNG VASCULAR DISEASES
    E-pub date: 1 May 2022
    Authors: MJ Sharkey, K Karunasaagarar, C Johns, S Rajaram, D Alkhanfar, K Dwivedi, S Alabed, P Metherall, R Van Der Geest, M Mamalakis, D Kiely, AJ Swift

    Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements.

    DOI: http://doi.org/10.1186/s12968-022-00855-3
    Journal: J Cardiovasc Magn Reson
    E-pub date: 7 Apr 2022
    Authors: F Alandejani, S Alabed, P Garg, ZM Goh, K Karunasaagarar, M Sharkey, M Salehi, Z Aldabbagh, K Dwivedi, M Mamalakis, P Metherall, J Uthoff, C Johns, A Rothman, R Condliffe, A Hameed, A Charalampoplous, H Lu, S Plein, JP Greenwood, A Lawrie, JM Wild, PJH de Koning, DG Kiely, R Van Der Geest, AJ Swift

    DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays.

    DOI: http://doi.org/10.1016/j.compmedimag.2021.102008
    Journal: Comput Med Imaging Graph
    E-pub date: 1 Dec 2021
    Authors: M Mamalakis, AJ Swift, B Vorselaars, S Ray, S Weeks, W Ding, RH Clayton, LS Mackenzie, A Banerjee

    LUCAS: A highly accurate yet simple risk calculator that predicts survival of COVID-19 patients using rapid routine tests

    DOI: http://doi.org/10.1101/2021.04.27.21256196
    Journal:
    E-pub date: 1 Aug 2021
    Authors: S Ray, A Swift, JW Fanstone, A Banerjee, M Mamalakis, B Vorselaars, LS Mackenzie, S Weeks