A novel disease severity prediction scheme via big pair-wise ranking and learning techniques using image-based personal clinical data

• First attempt to perform dementia diagnosis via pair-wise ranking and learning.
• A novel single-pixel based method for partial volume correction in functional MRI.
• A new learning technique based on continuous rank correction coefficient.

Disease severity prediction is essential in clinical diagnosis nowadays, as correct understandings of the onset and progression of disease are priceless in treatment planning. In this study, dementia disease, one of the most severe non-communicable diseases worldwide, is focused. A novel dementia disease severity prediction scheme is proposed using new big ranking and learning techniques. To be specific, arterial spin labeling, an emerging functional-magnetic resonance imaging technique, is adopted to provide image-based clinical data. There are two steps composed of the whole scheme. First, a single-pixel based method is presented to correct the partial volume effect in arterial spin labeling images. The advantage of this method is that, problems of blurring and brain detail loss can be well tackled. Second, novel big pair-wise ranking and learning techniques is proposed to realize the dementia disease severity prediction task using arterial spin labeling images after partial volume effects correction. Extensive experiments using a big database composed of images acquired from 350 real demented patients are carried out with several conventional methods being compared. Comprehensive statistical analysis is performed and it suggests that the new scheme is promising in dementia disease severity prediction.