Australian Institute for Machine Learning - Operations
Division of Research and Innovation
Eligible to supervise Masters and PhD - email supervisor to discuss availability.
Gabriel Maicas is the AI Lead for the Women’s and Children’s Hospital (WCH) at The Australian Institute for Machine Learning (AIML). Gabriel leads the development and integration of state-of-the-art machine learning research into WCH to improve patient outcomes and experience, and hospital efficiency. Gabriel’s research focuses on a range of health fields where AI has the potential to benefit the broader society and have a greater impact on patients.
Previously, Gabriel was a research assistant at AIML focused on several health-AI areas including the early diagnosis of diseases and personalised medicine to improve treatment decisions. Gabriel obtained his PhD in Medical Image Analysis from The University of Adelaide (Australia) in 2018. Gabriel received his Master’s degree in Computer Vision from Universidad Rey Juan Carlos (Madrid, Spain) and graduated from Universidad Autonoma de Madrid (Madrid, Spain) with a double major in Mathematics and Computer Science.
Gabriel’s research focuses on a range of health fields where AI has the potential to benefit the broader society and have a greater impact on patients.
Date Position Institution name 2021 - ongoing AI Lead for Women's and Children's Hospital Australian Institute for Machine Learning 2019 - 2021 Research Fellow Australian Institute for Machine Learning
Awards and Achievements
Date Type Title Institution Name Country Amount 2018 Award Dean’s Commendation for Doctoral Thesis Excellence The University of Adelaide Australia - 2018 Award MICCAI Student Travel Award MICCAI United States -
Date Institution name Country Title 2018 Australian Institute for Machine Learning Australia PhD 2014 Universidad Rey Juan Carlos Spain MsC
Year Citation 2019 Maicas, G., Bradley, A. P., Nascimento, J. C., Reid, I., & Carneiro, G. (2019). Deep Reinforcement Learning for Detecting Breast Lesions from DCE-MRI. In L. Lu, X. Wang, G. Carneiro, & L. Yang (Eds.), Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics (pp. 163-178). Cham, Switzerland: Springer.
2016 Maicas Suso, G., Muñoz, A. I., Galiano, G., Ben Hamza, A., & Schiavi, E. (2016). Spectral shape analysis of the hippocampal structure for Alzheimer’s disease diagnosis. In F. Ortegon Gallego, M. Redondo Neble, & J. Rodriguez Galvan (Eds.), Trends in Differential Equations and Applications (Vol. 8, pp. 17-32). Switzerland: Springer International Publishing.
2021 - Center for Augmented Reasoning - Funding for 1 PhD
2020 - Endometriosis Australia (Co-Applicant) - $ 30,000
2019 - eHealth Innovation Royal Adelaide Hospital (Co-Applicant) - 59,900
2019 - Australian Gynaecologic Endoscopy Society (Co-Applicant) - $ 9,570
Past Higher Degree by Research Supervision (University of Adelaide)
Date Role Research Topic Program Degree Type Student Load Student Name 2019 - 2022 Co-Supervisor Weakly Supervised Localisation for Censor Aware Survival Prediction from Medical Images Doctor of Philosophy Doctorate Full Time Mr Renato Hermoza Aragones
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