Image-based dementia disease diagnosis via deep low-resource pair-wise learning

Abstract

Medical Images data is widely acknowledged for its large volumes, high complexities and limited labels information, which makes learning-based tasks challenging to obtain promising performance based on such images data. Also, Alzheimer’s disease, one of the five most severe non-communicable diseases all around the world, receives much attention in both academic research and clinical diagnosis for the time being. It is generally acknowledged that magnetic resonance imaging, a popular imaging tool because of its free of radiation exposure in terms of patients safety issues, has been vastly employed in Alzheimer’s disease diagnosis in recent years. However, most contemporary clinical diagnosis efforts still heavily rely on clinicians’ expertise to realize the disease diagnosis task. In this study, inspired by the recent rapid development of deep learning and low-resource learning techniques, a novel image-based disease diagnosis scheme is proposed to automatically fulfil the diagnosis of Alzheimer’s disease based on arterial spin labeling magnetic resonance images. This new end-to-end low-resource learning scheme is conducted following four steps. First, raw arterial spin labeling images need to be acquired and essential pre-processing steps should be carried out on raw images. Second, the popular convolutional neural network technique is adopted to automatically extract latent trainable features from processed arterial spin labelling images, instead of traditional hand-crafted features. Third, a novel low-resource pair-wise learning method inspired by the semi-supervised learning theory is carried out based on latent trainable features for automatically determining disease similarity functions. Forth, learned outcomes are then fed into a multi-nominal logistic regression model and a regression process with the highest posterior probability is capable to reveal the corresponding disease diagnosis outcome. Extensive experiments based on a database composed of magnetic resonance images acquired from 350 real demented patients are carried out with the newly poroposed scheme being compared towards several other well-known diagnosis tools. All diagnosis results have undergone rigorous and comprehensive statistical analysis composed of analysis of variance and multiple comparison tests. The superiority of the new scheme has been demonstrated therein.