Publication associated with RIKEN and The School of Informatics collaboration received Best Publication Award at MICCAI 2021
Publication, titled "Probabilistic Deep Learning with Adversarial Training and Volume Interval Estimation - Better Ways to Perform and Evaluate Predictive Models for White Matter Hyperintensities Evolution", received Best Publication Award at the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021).
The publication is a result of close project collabration, titled "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 scientists include SVD Research scientist and the Row Fogo Centre Lecturer Dr Maria Valdés-Hernández with scientists from the School of Informatics at the University of Edinburgh (UK) and RIKEN Center for Brain Science (Japan).
Co-authors affilated with The University of Edinburgh include Professor Joanna Wardlaw who is the Chair of Applied Neuroimaging, Head of Neuroimaging Sciences and Edinburgh Imaging, and the Director for the Row Fogo Centre, and Stephen Making, who closely worked with Mild Stroke Study 2.
Read awarded publication
Publication 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, Maria del C. Valdes-Hernandez, Rizal Maulana, Joanna Wardlaw, Stephen Makin, and Henrik Skibbe
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".
Recognising that prognosis of vascular disease is a need, 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.
This project is funded by the Grants-in-Aid for Scientific Research (KAKENHI) Program.
Research outputs, aside of publication, include publically accessible algorithms.
About MICCAI 2021
The annual MICCAI conference was attended by world leading biomedical scientists, engineers, and clinicians from a wide range of disciplines associated with medical imaging and computer assisted intervention. The conference series offered scientific sessions including oral presentations and poster sessions, workshops, tutorials, and challenges are held on the days preceding and succeeding the conference.
The Conference was organised by the local team in Strasbourg, and held online between September 27th to October 1st 2021.
About Dr Maria Valdés-Hernández
Dr Valdés Hernández is a Lecturer in Image Processing and Analysis of The Row Fogo Centre for Research into Ageing and The Brain. She is also a Lead academic of the Image Analysis and Image Processing Techniques courses offered by the MSc online Programmes of the Edinburgh Imaging Academy.
Dr Valdés Hernández work is focused on developing software tools and pipelines to process and analyse the images for various clinical studies, and works in the discovery of imaging biomarkers of small vessel disease, ageing and neurodegeneration. Her expertise includes software development, image and signal processing and analysis.
The University of Edinburgh affilated publication co-authors researh profiles
Lothian Birth Cohorts
For project inquiries, please get in touch with Dr Maria Valdes-Hernandez.