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Alzheimer's disease (AD) cannot be reversed, but early diagnosis willsignificantly benefit patients' medical treatment and care. In recent works, ADdiagnosis has the primary assumption that all categories are known a prior -- aclosed-set classification problem, which contrasts with the open-setrecognition problem. This assumption hinders the application of the model innatural clinical settings. Although many open-set recognition technologies havebeen proposed in other fields, they are challenging to use for AD diagnosisdirectly since 1) AD is a degenerative disease of the nervous system withsimilar symptoms at each stage, and it is difficult to distinguish from itspre-state, and 2) diversified strategies for AD diagnosis are challenging tomodel uniformly. In this work, inspired by the concerns of clinicians duringdiagnosis, we propose an open-set recognition model, OpenAPMax, based on theanomaly pattern to address AD diagnosis in real-world settings. OpenAPMax firstobtains the abnormal pattern of each patient relative to each known categorythrough statistics or a literature search, clusters the patients' abnormalpattern, and finally, uses extreme value theory (EVT) to model the distancebetween each patient's abnormal pattern and the center of their category andmodify the classification probability. We evaluate the performance of theproposed method with recent open-set recognition, where we obtainstate-of-the-art results.

OpenAPMax: Abnormal Patterns-based Model for Real-World Alzheimer's Disease Diagnosis