11,625 research outputs found
ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression
We applied several regression and deep learning methods to predict fluid
intelligence scores from T1-weighted MRI scans as part of the ABCD
Neurocognitive Prediction Challenge (ABCD-NP-Challenge) 2019. We used voxel
intensities and probabilistic tissue-type labels derived from these as features
to train the models. The best predictive performance (lowest mean-squared
error) came from Kernel Ridge Regression (KRR; ), which produced a
mean-squared error of 69.7204 on the validation set and 92.1298 on the test
set. This placed our group in the fifth position on the validation leader board
and first place on the final (test) leader board.Comment: Winning entry in the ABCD Neurocognitive Prediction Challenge at
MICCAI 2019. 7 pages plus references, 3 figures, 1 tabl
Learning to rank from medical imaging data
Medical images can be used to predict a clinical score coding for the
severity of a disease, a pain level or the complexity of a cognitive task. In
all these cases, the predicted variable has a natural order. While a standard
classifier discards this information, we would like to take it into account in
order to improve prediction performance. A standard linear regression does
model such information, however the linearity assumption is likely not be
satisfied when predicting from pixel intensities in an image. In this paper we
address these modeling challenges with a supervised learning procedure where
the model aims to order or rank images. We use a linear model for its
robustness in high dimension and its possible interpretation. We show on
simulations and two fMRI datasets that this approach is able to predict the
correct ordering on pairs of images, yielding higher prediction accuracy than
standard regression and multiclass classification techniques
A supervised clustering approach for fMRI-based inference of brain states
We propose a method that combines signals from many brain regions observed in
functional Magnetic Resonance Imaging (fMRI) to predict the subject's behavior
during a scanning session. Such predictions suffer from the huge number of
brain regions sampled on the voxel grid of standard fMRI data sets: the curse
of dimensionality. Dimensionality reduction is thus needed, but it is often
performed using a univariate feature selection procedure, that handles neither
the spatial structure of the images, nor the multivariate nature of the signal.
By introducing a hierarchical clustering of the brain volume that incorporates
connectivity constraints, we reduce the span of the possible spatial
configurations to a single tree of nested regions tailored to the signal. We
then prune the tree in a supervised setting, hence the name supervised
clustering, in order to extract a parcellation (division of the volume) such
that parcel-based signal averages best predict the target information.
Dimensionality reduction is thus achieved by feature agglomeration, and the
constructed features now provide a multi-scale representation of the signal.
Comparisons with reference methods on both simulated and real data show that
our approach yields higher prediction accuracy than standard voxel-based
approaches. Moreover, the method infers an explicit weighting of the regions
involved in the regression or classification task
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