locality in brain networks. â Predict clinical outcomes of pre-term babies http://brainnetcnn.cs.sfu.ca/. Predict the infant's future developmental score from the.
BrainNetCNN: http://brainnetcnn.cs.sfu.ca/ Artificial Convolutional Neural Networks for Connectomes Jeremy Kawahara†a, Colin J. Brown†a, Steven P. Millerb, Brian G. Bootha, Vann Chaub, Ruth E. Grunauc, Jill G. Zwickerc, Ghassan Hamarneha †joint first authors
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Medical Image Analysis Lab, Simon Fraser University, Burnaby, BC, Canada b Department of Paediatrics, The Hospital for Sick Children and the University of Toronto, Toronto, ON, Canada c Child and Family Research Institute and the University of British Columbia, Vancouver, BC, Canada
Preterm infant
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Key contributions: ● We design convolutional filters for connectomes ● Consider the topological locality in brain networks ● Predict clinical outcomes of pre-term babies
Feature Extraction
Goal Predict the infant's future developmental score from the connectome
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Background: Deep learning via convolutional artificial neural networks has had big successes for image-based prediction tasks