Joint feature selection and classification using a Bayesian neural network with automatic relevance determination priors: potential use in CAD of medical imaging

Abstract

Bayesian neural network (BNN) with automatic relevance determination (ARD) priors has the ability to assess the relevance of each input feature during network training. Our purpose is to investigate the potential use of BNN-with-ARD-priors for joint feature selection and classification in computer-aided diagnosis (CAD) of medical imaging. With ARD priors, each group of weights that connect an input feature to the hidden units is associated with a hyperparameter controlling the magnitudes of the weights. The hyperparameters and the weights are updated simultaneously during neural network training. A smaller hyperparameter will likely result in larger weight values and the corresponding feature will likely be more relevant to the output, and thus, to the classification task. For our study, a multivariate normal feature space is designed to include one feature with high classification performance in terms of both ideal observer and linear observer, two features with high ideal observer performance but low linear observer performance and 7 useless features. An exclusive-OR (XOR) feature space is designed to include 2 XOR features and 8 useless features. Our simulation results show that the ARD-BNN approach has the ability to select the optimal subset of features on the designed nonlinear feature spaces on which the linear approach fails. ARD-BNN has the ability to recognize features that have high ideal observer performance. Stepwise linear discriminant analysis (SWLDA) has the ability to select features that have high linear observer performance but fails to select features that have high ideal observer performance and low linear observer performance. The cross-validation results on clinical breast MRI data show that ARD-BNN yields statistically significant better performance than does the SWLDA-LDA approach. We believe that ARD-BNN is a promising method for pattern recognition in computer-aided diagnosis of medical imaging.