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Miguel Monteiro
About me
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Posts
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Blog Post number 4
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Blog Post number 1
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education
BSc Electrical and Computer Engineering
Instituto Superior Técnico, September 2011 - July 2014
Final Grade Average: 16.00 / 20 (top 4% of students)
Awarded three academic excellence diplomas (one for each academic year)
Dual MSc in Electrical and Computer Engineering
Instituto Superior Técnico & Royal Institute of Technology, August 2014 - November 2016
Final Grade Average: 18.00 / 20 (top 1%)
Awarded two academic excellence diplomas (one for each academic year)
PhD in Computing
Imperial College London, October 2019 - January 2023
Machine learning for medical imaging, probabilistic modelling and causal inference.
experience
Research Intern
Celfinet, April 2016 - December 2016
Worked as a Research Intern at Celfinet as part of my Master’s Thesis project. During the internship, I collaborated closely with the R&D team researching automatic load balancing for LTE networks and network traffic forecasting using time series analysis.
Researcher
INESC-ID, March 2017 - October 2018
Worked as a Researcher at INESC-ID focusing on using machine learning to predict the functional outcome of stroke patients, using both classical ML algorithms for tabular data and deep learning computer vision algorithms for brain MRI image.
Research Intern
Microsoft Research Cambridge, October 2020 - December 2020
Undertook a Research Internship at Microsoft Research Cambridge in the InnerEye project focusing on using machine learning for outlier detection and label noise removal.
ML Engineer/Consultant
Triradiate Industries A.I.R., January 2020 - December 2021
Worked remotely as freelance ML Engineer/Consultant for Triradiate Industries A.I.R. developing deep learning segmentation pipeline for medical imaging applications.
Research Assistant
Imperial College London, October 2018 - March 2023
During my PhD I worked as a Research Assistant at Imperial College London. In addition to the research facet I was the project lead for the CT lesion analysis pipeline for the EU FP7 project CENTER-TBI.
ML Engineer
Qureight, April 2023 - April 2023
Currently working as a Machine Learning Engineer and Scientist at Qureight.
publications
Forecasting Traffic and Balancing Load for Quality Driven LTE Networks
Miguel Monteiro
Published in Instituto Superior Técnico, 2016
Improving the Prediction of Functional Outcome in Ischemic Stroke Patients
Miguel Monteiro, Ana Catarina, Ana Teresa Freitas, Teresa Pinho e Melo, Alexandre P Francisco, José M Ferro, Arlindo L Oliveira
Published in International Workshop on Data Mining in Bioinformatics (BIOKDD), 2017
Fully convolutional neural network for 3D stroke lesion segmentation
Miguel Monteiro, Monteiro, Arlindo L Oliveira
Published in Ischemic Stroke Lesion Segmentation - ISLES challenge 2017, Quebec, Canada, 2017
Using Machine Learning to Improve the Prediction of Functional Outcome in Ischemic Stroke Patients
Miguel Monteiro, Ana Catarina, Ana Teresa Freitas, Teresa Pinho e Melo, Alexandre P Francisco, José M Ferro, Arlindo L Oliveira
Published in IEEE/ACM transactions on computational biology and bioinformatics, 2018
ISLES 2016 and 2017 - Benchmarking Ischemic Stroke Lesion Outcome Prediction Based on Multispectral MRI
Stefan Winzeck et al.
Published in Frontiers in Neurology, 2018
Conditional random fields as recurrent neural networks for 3d medical imaging segmentation
Miguel Monteiro, Mário AT Figueiredo, Arlindo L Oliveira
Published in MED-NEURIPS 2018, 2018
Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study
Miguel Monteiro, Virginia FJ Newcombe, Francois Mathieu, Krishma Adatia, Konstantinos Kamnitsas, Enzo Ferrante, Tilak Das, Daniel Whitehouse, Daniel Rueckert, David K Menon, Ben Glocker
Published in The Lancet Digital Health, 2020
TBI lesion segmentation in head CT: Impact of preprocessing and data augmentation
Miguel Monteiro, Konstantinos Kamnitsas, Enzo Ferrante, Francois Mathieu, Steven McDonagh, Sam Cook, Susan Stevenson, Tilak Das, Aneesh Khetani, Tom Newman, Fred Zeiler, Richard Digby, Jonathan P Coles, Daniel Rueckert, David K Menon, Virginia FJ Newcombe, Ben Glocker
Published in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. 5th International Workshop, BrainLes 2019, 2020
Relationship between measures of cerebrovascular reactivity and intracranial lesion progression in acute TBI patients: a CENTER-TBI study
Francois Mathieu, Frederick A Zeiler, Ari Ercole, Miguel Monteiro, Konstantinos Kamnitsas, Ben Glocker, Daniel P Whitehouse, Tilak Das, Peter Smieleweski, Peter John Hutchinson, Marek Czosnyka, Virginia Newcombe, David Menon
Published in Journal of Neurotrauma, 2020
Diffuse intracranial injury patterns are associated with impaired cerebrovascular reactivity in adult traumatic brain injury: a CENTER-TBI validation study
Frederick A Zeiler, François Mathieu, Miguel Monteiro, Ben Glocker, Ari Ercole, Erta Beqiri, Manuel Cabeleira, Nino Stocchetti, Peter Smielewski, Marek Czosnyka, Virginia Newcombe, David K Menon
Published in Journal of Neurotrauma, 2020
Impact of antithrombotic agents on radiological lesion progression in acute traumatic brain injury: A CENTER-TBI propensity-matched cohort analysis
Francois Mathieu, Helge Guting, Benjamin Gravesteijn, Miguel Monteiro, Ben Glocker, Evgenios N Kornaropoulos, Konstantinos Kamnitsas, Claudia S Robertson, Harvey Levin, Daniel P Whitehouse, Tilak Das, Hester F Lingsma, Marc Maegele, Virginia Newcombe, David Menon
Published in Journal of Neurotrauma, 2020
Stochastic segmentation networks: Modelling spatially correlated aleatoric uncertainty
Miguel Monteiro, Loic Le Folgoc, Daniel Coelho de Castro, Nick Pawlowski, Bernardo Marques, Konstantinos Kamnitsas, Mark van der Wilk, Ben Glocker
Published in Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 2020
Systemic Markers of Injury and Injury Response Are Not Associated with Impaired Cerebrovascular Reactivity in Adult Traumatic Brain Injury: A Collaborative European Neurotrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) Study
Frederick A Zeiler, François Mathieu, Miguel Monteiro, Ben Glocker, Ari Ercole, Manuel Cabeleira, Nino Stocchetti, Peter Smielewski, Marek Czosnyka, Virginia Newcombe, David K Menon, Collaborative European Neurotrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) High-Resolution ICU (HR ICU) Sub-Study Participants and Investigators
Published in Journal of Neurotrauma, 2021
Relationship of admission blood proteomic biomarkers levels to lesion type and lesion burden in traumatic brain injury: A CENTER-TBI study
Daniel P Whitehouse, Miguel Monteiro, Endre Czeiter, Thijs Vande Vyvere, Fernanda Valerio, Zheng Ye, Krisztina Amrein, Konstantinos Kamnitsas, Haiyan Xu, Zhihui Yang, Jan Verheyden, Tilak Das, Evgenios N Kornaropoulos, Ewout Steyerberg, Andrew IR Maas, Kevin KW Wang, András Büki, Ben Glocker, David K Menon, Virginia FJ Newcombe
Published in EBioMedicine, 2021
Active label cleaning for improved dataset quality under resource constraints
Mélanie Bernhardt, Daniel C Castro, Ryutaro Tanno, Anton Schwaighofer, Kerem C Tezcan, Miguel Monteiro, Shruthi Bannur, Matthew P Lungren, Aditya Nori, Ben Glocker, Javier Alvarez-Valle, Ozan Oktay
Published in Nature communications, 2022
Structured uncertainty in the observation space of variational autoencoders
James Langley, Miguel Monteiro, Charles Jones, Nick Pawlowski, Ben Glocker
Published in arXiv preprint, 2022
Automatic lesion analysis for increased efficiency in outcome prediction of traumatic brain injury
Margherita Rosnati, Eyal Soreq, Miguel Monteiro, Lucia Li, Neil SN Graham, Karl Zimmerman, Carlotta Rossi, Greta Carrara, Guido Bertolini, David J Sharp, Ben Glocker
Published in International Workshop on Machine Learning in Clinical Neuroimaging, 2022
Analysing the effectiveness of a generative model for semi-supervised medical image segmentation
Margherita Rosnati, Fabio De Sousa Ribeiro, Miguel Monteiro, Daniel Coelho de Castro, Ben Glocker
Published in arXiv preprint, 2022
Measuring axiomatic soundness of counterfactual image models
Miguel Monteiro, Fabio De Sousa Ribeiro, Nick Pawlowski, Daniel C Castro, Ben Glocker
Published in The Eleventh International Conference on Learning Representations (ICLR 2023), 2023
High fidelity image counterfactuals with probabilistic causal models
Fabio De Sousa Ribeiro, Tian Xia, Miguel Monteiro, Nick Pawlowski, Ben Glocker
Published in Proceedings of the 40th International Conference on Machine Learning (ICML 2023), 2023
Advancing probabilistic and causal deep learning in medical image analysis
Miguel Monteiro
Published in Imperial College London, 2023
Automatic localisation and per-region quantification of traumatic brain injury on head CT using atlas mapping
Carolina Piçarra, Stefan Winzeck, Miguel Monteiro, Francois Mathieu, Virginia FJ Newcombe, David K Menon, Ben Glocker
Published in European Journal of Radiology Open, 2023
talks
Deep Learning for Medical Imaging
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[slides]
Introduction to Deep Learning
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[slides]
Stochastic Segmentation Networks
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Uncertainty Modeling in Image Segmentation
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[video] [conference site] [publication] [slides (html)] [slides (pdf)]
Measuring axiomatic soundness of counterfactual image models - Causal ML Reading Group
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[slides] [publication]