Overview

WINNER OF THE CHALLENGE is Sabina Tangaro and her team,  including R. Bellotti, A. Lombardi, N. Amoroso and  A. Tateo, with their contribution ‘Semi-unsupervised Prediction for Mild TBI Based on Both Graph and K-nn Methods‘.

RUNNER-UPs are Yunliang Cai & Songbai Ji (‘Combining Deep Learning Networks with Permutation Tests to Predict Traumatic Brain Injury Outcome’) as well as  Po-Yu Kao and his team, E. Rojas , J. Chen , A. Zhang and B.S. Manjunath (‘Unsupervised 3-D Feature Learning for Mild Traumatic Brain Injury’).

Welcome to the Mild Traumatic Brain Injury Outcome Prediction (mTOP) challenge.
Its aim is to create a common ground to compare methods to find predictive MRI features, which help to characterise and distinguish mild traumatic brain injury patients from each other and healthy subjects.

Together with the BRATS and ISLES challenges, mTOP will be part of the Brainles workshop (17th October), which will be held at the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2016.

Here you can find detailed information about motivation, rules and data for the challenge.

 

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