  Alzheimer's disease (AD) cannot be reversed, but early diagnosis will
significantly benefit patients' medical treatment and care. In recent works, AD
diagnosis has the primary assumption that all categories are known a prior -- a
closed-set classification problem, which contrasts with the open-set
recognition problem. This assumption hinders the application of the model in
natural clinical settings. Although many open-set recognition technologies have
been proposed in other fields, they are challenging to use for AD diagnosis
directly since 1) AD is a degenerative disease of the nervous system with
similar symptoms at each stage, and it is difficult to distinguish from its
pre-state, and 2) diversified strategies for AD diagnosis are challenging to
model uniformly. In this work, inspired by the concerns of clinicians during
diagnosis, we propose an open-set recognition model, OpenAPMax, based on the
anomaly pattern to address AD diagnosis in real-world settings. OpenAPMax first
obtains the abnormal pattern of each patient relative to each known category
through statistics or a literature search, clusters the patients' abnormal
pattern, and finally, uses extreme value theory (EVT) to model the distance
between each patient's abnormal pattern and the center of their category and
modify the classification probability. We evaluate the performance of the
proposed method with recent open-set recognition, where we obtain
state-of-the-art results.
