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.

翻译：阿尔茨海默病（AD）无法逆转，但早期诊断将对患者的医疗和护理产生显著益处。近期研究中，AD诊断的主要假设是所有类别均事先已知——即封闭集分类问题，这与开放集识别问题形成对比。该假设阻碍了模型在自然临床环境中的应用。尽管其他领域已提出许多开放集识别技术，但这些技术难以直接用于AD诊断，原因是：1）AD是一种神经系统退行性疾病，各阶段症状相似，且难以与其前驱状态区分；2）AD诊断的多样化策略难以统一建模。本研究受临床医生诊断过程中关注点的启发，提出一种基于异常模式的开放集识别模型OpenAPMax，以解决真实场景中的AD诊断问题。OpenAPMax首先通过统计或文献检索获取每位患者相对于各已知类别的异常模式，对患者异常模式进行聚类，最后利用极值理论（EVT）对每位患者异常模式与类别中心之间的距离进行建模，并修正分类概率。我们通过与最新开放集识别方法的性能对比评估所提方法，取得了当前最优结果。