1 research outputs found
GSplit LBI: Taming the Procedural Bias in Neuroimaging for Disease Prediction
In voxel-based neuroimage analysis, lesion features have been the main focus
in disease prediction due to their interpretability with respect to the related
diseases. However, we observe that there exists another type of features
introduced during the preprocessing steps and we call them "\textbf{Procedural
Bias}". Besides, such bias can be leveraged to improve classification accuracy.
Nevertheless, most existing models suffer from either under-fit without
considering procedural bias or poor interpretability without differentiating
such bias from lesion ones. In this paper, a novel dual-task algorithm namely
\emph{GSplit LBI} is proposed to resolve this problem. By introducing an
augmented variable enforced to be structural sparsity with a variable splitting
term, the estimators for prediction and selecting lesion features can be
optimized separately and mutually monitored by each other following an
iterative scheme. Empirical experiments have been evaluated on the Alzheimer's
Disease Neuroimaging Initiative\thinspace(ADNI) database. The advantage of
proposed model is verified by improved stability of selected lesion features
and better classification results.Comment: Conditional Accepted by Miccai,201