Boosting Alzheimer diagnosis accuracy with the help of incomplete privileged information

Early and accurate diagnosis of Alzheimer's disease is beneficial to both preserve daily functioning and test possible new treatments. However, current diagnosis depends on dozens of factors, including the family member, past medical problems, tests of memory, blood and urine tests, brain scans and even cerebrospinal fluid specimens. Among them, regular features (e.g., blood and urine tests, brain scans) are simple and accurate. Privileged features (e.g., tests of memory, cerebrospinal fluid specimens) are inconvenient and uncomfortable to acquire, and thus incomplete in most cases. In this work, we propose a two-stage learning framework to predict merely using the regular feature with the aid of incomplete privileged data in the training time. In particular, we first complement the missing data of privileged features by exploring the relationship with regular features and labels. The recovered privileged features, regular features, as well as labels, are combined as a new set of privileged features. Then privileged learning via matching logits is applied to boost the diagnosis accuracy. Our experiments and comparison studies with competing techniques on both synthetic data and real benchmarks have corroborated the effectiveness and superiority of the proposed framework for biomedical applications.