@inproceedings{d580cce9fbb148208c6a92e72ec33378,
title = "The tenth annual MLSP competition: Schizophrenia classification challenge",
abstract = "For the 24th Machine Learning for Signal Processing competition, participants were asked to automatically diagnose schizophrenia using multimodal features derived from MRI scans. The objective of the classification task was to achieve the best possible schizophrenia diagnosis prediction based only on the multimodal features derived from brain MRI scans. A total of 2087 entries from 291 participants with active Kaggle.com accounts were made. Each participant developed a classifier, with optional feature selection, that combined functional and structural magnetic resonance imaging features. Here we review details about the competition setup, the winning strategies, and provide basic analyses of the submitted entries. We conclude with a discussion of the advances made to the neuroimaging and machine learning fields.",
keywords = "Competition, FNC, MRI, SBM, Schizophrenia",
author = "Silva, {Rogers F.} and Eduardo Castro and Gupta, {Cota Navin} and Mustafa Cetin and Mohammad Arbabshirani and Potluru, {Vamsi K.} and Plis, {Sergey M.} and Calhoun, {Vince D.}",
year = "2014",
month = nov,
day = "14",
doi = "10.1109/MLSP.2014.6958889",
language = "English (US)",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
publisher = "IEEE Computer Society",
editor = "Tulay Adali and Jan Larsen and Mamadou Mboup and Eric Moreau",
booktitle = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
note = "2014 24th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2014 ; Conference date: 21-09-2014 Through 24-09-2014",
}