Medical media analytics via ranking and big learning: A multi-modality image-based disease severity prediction study

Abstract

Medical media analytics receives vast popularity nowadays because of its effectiveness in improving the performance of diverse health-care applications. In this study, the essential disease severity prediction problem in medical media analytics is investigated and a computer-aided diagnosis (CAD) strategy based on ranking and learning techniques is presented to tackle the disease severity prediction task. To be specific, two types of magnetic resonance images (MRI), including T1-weighted images as anatomic MRI and arterial spin labeling (ASL) images as functional MRI, are incorporated as multi-modality images to provide image-based information for dementia disease severity prediction in this study. There are two main steps composed of the whole CAD strategy. First, the problem of partial volume effects (PVE) mainly caused by signal cross-contamination due to pixel heterogeneity and limited spatial resolution of ASL is focused. Conventional regression-based PVE correction methods are discussed and their inherent problems of blurring and brain details loss in correction results, which prevents the actual brain atrophy being revealed, are studied. A pixel-based PVE correction method, which only counts on single pixel information and formulates the PVE correction problem as a constrained optimization problem solved via the split-Bregman algorithm, is presented to solve the problem. Second, ranking and learning techniques are incorporated based on multi-modality images after performing PVE correction for dementia disease severity prediction. Technically, a conventional discrete position-based ranking evaluation measure is approximated and its surrogated continuous form is optimized via gradient ascend for ranking functions learning. A large database composed of multi-modality images acquired from 320 real patients is utilized for experimental evaluation. Extensive experiments and comprehensive statistical analysis are carried out to demonstrate the superiority of the introduced CAD strategy with comparison to several existing ones. Promising results are reported from the statistical perspective.