Alzheimer’s disease (AD) is one of the most common progressive neurodegenerative diseases, and the number of AD patients has increased year after year with the global aging trend. The onset of AD has a long preclinical stage. If doctors can make an initial diagnosis in the mild cognitive impairment (MCI) stage, it is possible to identify and screen those at a high-risk of developing full-blown AD, and thus the number of new AD patients can be reduced. However, there are problems with the medical datasets including AD data, such as insufficient number of samples and different data distributions. Transfer learning, which can effectively solve the problem of distribution discrepancy between training and test data and an insufficient number of target samples, has attracted increasing attention over recent years. In this paper, we propose a multi-source ensemble transfer learning (METL) approach by introducing ensemble learning and our tri-transfer model that uses Tri-Training, which ensures the transferability of source data by the tri-transfer model and high performance through ensemble learning. The experimental results on the benchmark and AD datasets demonstrate that our proposed approach has effective transferability, robustness, and feasibility, and is superior to existing algorithms. Based on METL, we propose an auxiliary diagnosis system for the initial diagnosis of AD, which helps doctors identify patients in the MCI stage as quickly as possible and with high accuracy so that measures can be taken to prevent or delay the occurrence of AD.

中文翻译：

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通过集成方法进行多源迁移学习用于阿尔茨海默病的初步诊断

阿尔茨海默病（AD）是最常见的进行性神经退行性疾病之一，随着全球老龄化趋势，AD患者人数逐年增加。AD的发病有一个很长的临床前阶段。如果医生能够在轻度认知障碍（MCI）阶段做出初步诊断，就可以识别和筛选出发展为完全性 AD 的高危人群，从而减少新发 AD 患者的数量。然而，包括AD数据在内的医学数据集存在样本数量不足和数据分布不同等问题。迁移学习可以有效解决训练和测试数据分布不均、目标样本数量不足等问题，近年来受到越来越多的关注。在本文中，我们通过引入集成学习和我们使用 Tri-Training 的三迁移模型提出了一种多源集成迁移学习 (METL) 方法，该模型通过三迁移模型确保源数据的可迁移性和通过集成学习的高性能。在基准和 AD 数据集上的实验结果表明，我们提出的方法具有有效的可迁移性、鲁棒性和可行性，并且优于现有算法。基于METL，我们提出了AD初始诊断的辅助诊断系统，帮助医生尽快、高精度地识别出处于MCI阶段的患者，从而采取措施预防或延缓AD的发生。