Relational learning helps in three-way classification of Alzheimer patients from structural magnetic resonance images of the brain
Magnetic resonance imaging (MRI) has emerged as an important tool to identify intermediate biomarkers of Alzheimers disease (AD) due to its ability to measure regional changes in the brain that are thought to reflect disease severity and progression. In this paper, we set out a novel pipeline that uses volumetric MRI data collected from different subjects as input and classifies them into one of three classes: AD, mild cognitive impairment (MCI) and cognitively normal (CN). Our pipeline consists of three stages(1) a segmentation layer where brain MRI data is divided into clinically relevant regions; (2) a classification layer that uses relational learning algorithms to make pairwise predictions between the three classes; and (3) a combination layer that combines the results of the different classes to obtain the final classification. One of the key features of our proposed approach is that it allows for domain experts knowledge to guide the learning in all the layers. We evaluate our pipeline on 397 patients acquired from the Alzheimers Disease Neuroimaging Initiative and demonstrate that it obtains state-of-the-art performance with minimal feature engineering.
statistical relational learning fmri prediction biomedical applications ensemble methods