NormNets

Learning the integral connectional template of the brain

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Check out our recent NormNets publications, videos, open source codes and more ~

24 May 2020

step 0

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

Publication: https://www.sciencedirect.com/science/article/pii/S1361841519301070

23 June 2020

Output 1

 

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

Output 2

 

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%). 

check out more BASIRA lab publications

Feel free to contact us at basiralab@gmail.com to apply for positions at BASIRA lab or establish synergetic collaborations!

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