Mariana Bento

Dr. Mariana Bento



Assistant Professor

Schulich School of Engineering, Department of Biomedical Engineering

Assistant Professor

Schulich School of Engineering, Department of Electrical and Software Engineering

Full Member

Hotchkiss Brain Institute

Child Health & Wellness Researcher

Alberta Children's Hospital Research Institute

Contact information

Web presence

Phone number

Office: 403.220.7073


Office: CCIT016

For media enquiries, contact

Joe McFarland
Media Relations and Communications Specialist

Cell: +1.403.671.2710


Educational Background

PhD Computer Engineering, University of Campinas (UNICAMP), 2016,

MSc Computer Engineering, University of Campinas (UNICAMP), 2012,

BSc Teleinformatics Engineering, Federal University of Ceara (UFC), 2010,


Mariana Bento is an Assistant Professor at the Electrical and Software at the University of Calgary. Previously, she served as a postdoctoral fellow in Radiology and Clinical Neuroscience at the University of Calgary. She did her doctorate (2016) in the Faculty of Electrical and Computer Engineering at the University of Campinas with an internship at the University of Calgary, and master's (2013) in the same faculty with an internship at the University of Pennsylvania. She finished her bachelor's in Teleinformatics Engineering at the top of her class at the Federal University of Ceará (2011). Since 2011, she has been working with machine learning, deep learning for medical imaging. Topics of interest are brain abnormalities, heterogeneous datasets, MR imaging quality assessment, and interpretability of deep learning approaches on medical imaging applications.


Areas of Research

Biomedical Engineering, Artificial Intelligence, Computer Vision, Brain Magnetic Resonance Imaging

Participation in university strategic initiatives


  • Research Scholar Fellowship, Principal, Canadian Open Neuroscience Platform. 2021
  • Prediction of multiple sclerosis disability worsening scores using multi-stream deep learning, Collaborator, Canadian Institutes of Health Research. 2020
  • Donald Burns and Louise Berlin Postdoctoral Fellowship in Dementia Research, Principal, Hotchkiss Brain Institute. 2017
  • Travel award to attend The International Society of Vascular Behavioral and Cognitive Disorders, Canadian Institutes of Health Research. 2018
  • CAPES Postdoctoral Fellowship, Principal, Special Visiting Professor Program. 2017
  • International Santander Mobility Scholarship, Internship at the University of Pennsylvania with Jayaram Udupa, PhD, Principal. 2012
  • Best Oral Presentation, Conference about Equipment Technology (COTEQ/ABENDE). 2009


  • Automatic MR image quality evaluation using a Deep CNN: A reference-free method to rate motion artifacts in neuroimaging. Fantini, I., Yasuda, C., Bento, M., et al. Computerized Medical Imaging and Graphics. (2021)
  • A framework for quality control of corpus callosum segmentation in large-scale studies. Herrera, W., Pereira, M., Bento, M., et al.. Elsevier Journal of Neuroscience Methods. (2020)
  • Dual-domain Cascade of U-nets for Multi-channel Magnetic Resonance Image Reconstruction. Souza, R., Bento, M., et al.. Elsevier Magnetic Resonance Imaging Access. (2020)
  • Automatic Identification of Atherosclerosis Patients in a Heterogeneous MR Brain Imaging Dataset. Bento, M., et al.. Magnetic Resonance Imaging. (2021)
  • Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities; Results of the WMH Segmentation Challenge. Kuijf, H., Biesbroek, J., Bresser, J., Heinen, R., Andermatt, S., Bento, M., et. al.. IEEE Transactions on Medical Imaging. (2019)
  • Reliability of Computer-Aided Diagnosis Tools with Multicenter MR Datasets: Impact of Training Protocol . Bento, M., Souza, R. and Frayne, R. . Oral presentation in SPIE Medical Imaging. (2019)
  • Multicenter Imaging Studies: Automated Approach to Evaluating Data Variability and the Role of Outliers. Bento, M., Souza, R. and Frayne, R. . Oral presentation in 31st Conference on Graphics, Patterns and Images. (2018)
  • Etiology-based classification of brain white matter hyperintensity on magnetic resonance imaging. Leite, M. P. B., et al. . Journal of Medical Imaging. (2015)