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Increasing effort in brain image analysis has been dedicated to earlydiagnosis of Alzheimer's disease (AD) based on neuroimaging data. Most existingstudies have been focusing on binary classification problems, e.g.,distinguishing AD patients from normal control (NC) elderly or mild cognitiveimpairment (MCI) individuals from NC elderly. However, identifying individualswith AD and MCI, especially MCI individuals who will convert to AD (progressiveMCI, pMCI), in a single setting, is needed to achieve the goal of earlydiagnosis of AD. In this paper, we propose a deep ordinal ranking model fordistinguishing NC, stable MCI (sMCI), pMCI, and AD at an individual subjectlevel, taking into account the inherent ordinal severity of brain degenerationcaused by normal aging, MCI, and AD, rather than formulating the classificationas a multi-category classification problem. The proposed deep ordinal rankingmodel focuses on the hippocampal morphology of individuals and learnsinformative and discriminative features automatically. Experiment results basedon a large cohort of individuals from the Alzheimer's Disease NeuroimagingInitiative (ADNI) indicate that the proposed method can achieve betterperformance than traditional multi-category classification techniques usingshape and radiomics features from structural magnetic resonance imaging (MRI)data.

Deep Ordinal Ranking for Multi-Category Diagnosis of Alzheimer's Disease using Hippocampal MRI data