Multi-View Privileged Information-Based Representation Learning for Liver Cancer Diagnosis

Abstract

Privileged information (PI) provides additional knowledge to improve performance. Though some efforts are carried out by learning using privileged information (LUPI), they mainly focus on classifier-level LUPI and single-view PI tasks. Therefore, it is a challenge for feature representation learning by transferring multi-view PI to improve the main view. In this paper, we propose a novel feature-level LUPI for multi-view PI tasks, called the multi-view privileged information-based representation learning (MPIRL) algorithm, in which multi-view PI and main view are required at the training phase, but only the main view is available at the testing phase. MPIRL consists of a feature-level LUPI module and a classification module. The feature-level LUPI module of MPIRL designs a multi-branch structure to transfer the multi-view privileged information to the main view, so that diversity and discriminative representation can be generated. For the classification module, multi-view deep SVM (MDSVM) is developed, which combines a multi-channel deep neural network with SVM into a unified framework. MDSVM further learns the fusion representation and classification simultaneously to improve the generalization performance. The experimental results on the dual-view PI tasks and multi-view PI tasks of the real-world multi-view liver cancer dataset show that the proposed MPIRL achieves superior performance with an accuracy of 86.92%, sensitivity of 89.58%, and specificity of 84.25%.