Brain and Brain PET 2022 May 29, 2022 - Jun 01, 2022 — Glasgow
2022 OHBM Annual Meeting Jun 19, 2022 - Jun 23, 2022 — Glasgow, SEC

eLearning

SINAPSE experts from around Scotland have developed ten online modules designed to explain medical imaging. They are freely available and are intended for non-specialists. **Unfortunately these do not currently work in browsers**


Edinburgh Imaging Academy at the University of Edinburgh offers the following online programmes through a virtual learning environment:

Neuroimaging for Research MSc/Dip/Cert

Imaging MSc/Dip/Cert

PET-MR Principles & Applications Cert

Applied Medical Image Analysis Cert

Online Short Courses

Dr Carlos Moreno-Garcia

Home page: Go to homepage
Position: Senior Lecturer

Institute: Robert Gordon University

Department: School of Computing


Research Themes
  • Pattern Recognition
  • Computer Vision
  • Medical Image Analysis
  • Document Image Analysis
  • Graph Representations
  • Machine Learning
  • Data Science
  • Natural Language Processing
Key Publications

T. Dang, T. T. Nguyen, C. F. Moreno-García, E. Elyan & J. McCall, “Weighted ensemble of deep learning models based on comprehensive learning particle swarm optimization for medical image segmentation”, Proceedings of 2021 IEEE Congress on Evolutionary Computation (CEC 2021), 28 June - 1 July 2021, Virtual conference, pp. 744-751. https://doi.org/10.1109/CEC45853.2021.9504929.

 

T. Dang, T. Nguyen, J. McCall, E. Elyan & C. F. Moreno-García, “Two layer ensemble of deep learning models for medical image segmentation”, arXiv e-prints, 2021. https://arxiv.org/abs/2104.04809.

 

C. F. Moreno-García, T. Dang, K. Martin, et. al, “Assessing the clinicians’ pathway to embed artificial intelligence for assisted diagnostics of fracture detection”, Proceedings of the 5th International Workshop on Knowledge Discovery in Healthcare Data, co-located with 24th European Conference on Artificial Intelligence (ECAI 2020), Santiago de Compostela, Spain, Volume 2675, pp. 63–70. http://ceur-ws.org/Vol-2675/paper10.pdf.