Learning the integral connectional template of the brain
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24 May 2020
This is the first work on estimating a connectional brain template of a population of multi-view brain networks.
netNorm can be used to integrate a population of multi-view network datasets with heterogeneous distributions, given that they have the same size.
Source code in Matlab: https://github.com/basiralab/netNorm
Source code in Python: https://github.com/basiralab/netNorm-PY
23 June 2020
The first geometric deep learning model for multiview brain network integration and connectional brain template estimation is now accepted for publication in MICCAI (class A1) conference Springer proceedings.
Publication: M.B. Gurbuz and I. Rekik. Deep Graph Normalizer: A Geometric Deep Learning Approach for Estimating Connectional Brain Templates. MICCAI (2020), Lima, Peru (acceptance rate ~30%). —in press
23 June 2020
Our work on supervised brain multigraph diffusion and fusion for estimating population-driven connectional brain templates is now accepted for publication in MICCAI (class A1) conference Springer proceedings.
Publication: I. Mhiri, M.A. Mahjoub, and I. Rekik. Supervised Multi-topology Network Cross-diffusion for Population-Driven Brain Network Atlas Estimation. MICCAI (2020), Lima, Peru (acceptance rate ~30%).
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Acknowledgements: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101003403.
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