Project title: "Development of Data-driven Prediction Model using 3D Multimodal Deep Neural Networks for Estimating the Evolution of White Matter Hyperintensities Associated with Small Vessel Disease in Brain MRI"
Collaborating institutions: RIKEN Center for Brain Science (Japan), School of Informatics at The University of Edinburgh
About the project
Project title: "Development of Data-driven Prediction Model using 3D Multimodal Deep Neural Networks for Estimating the Evolution of White Matter Hyperintensities Associated with Small Vessel Disease in Brain MRI".
Prognosis of vascular disease is a need, and this collaboration is primarily focused on developing data-driven solutions to build predictive models for the progression of small vessel disease (SVD), a vascular disease that underpins ageing and dementia progression, using MRI scans as the main diagnostic tool. Specifically, the aim of this project is to develop algorithms for predicting the evolution of white matter hyperintensities, which are the key feature of this progressive disease.
RIKEN Center for Brain Science (Japan)
The University of Edinburgh (UK)
Dr Muhammad Febrian Rachmadi
Special Postdoctoral Researcher
Email Address: email@example.com
Dr Maria Valdes-Hernandez
Row Fogo Lecturer in Medical Image Analysis
Email address: M.Valdes-Hernan@ed.ac.uk
Professor Taku Komura
Professor of Computer Graphics
Email address: TKomura@inf.ed.ac.uk
This project is funded by the Grants-in-Aid for Scientific Research (KAKENHI) Program (project no 20K23356) - awarded 2,861,000 ¥ Jan 2021- Dec 2022.
Publication title: "Probabilistic Deep Learning with Adversarial Training and Volume Interval Estimation - Better Ways to Perform and Evaluate Predictive Models for White Matter Hyperintensities"
Authors: Muhammad Febrian Rachmadi, Maria del C. Valdes-Hernandez, Rizal Maulana, Joanna Wardlaw, Stephen Makin, and Henrik Skibbe
Publication date: September 2021
Recognitions and awards
Above publication received the Best Publication Award at 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021).
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Algorithm title: "Probabilistic Deep Learning with Adversarial Training and Volume Interval Estimation - Better Ways to Perform and Evaluate Predictive Models for White Matter Hyperintensities Evolution"
Authors: Muhammad Febrian Rachmadi
Topics: deep neural networks, deep learning, brain MRI, WMH segmentation, WMHs
Please, get in touch with Dr Maria Valdes-Hernandez for more information about this project and further collaboration.