Yang Yun, Li Xinfa, Wang Pei, Xia Yuelong, Ye Qiongwei
1National Pilot School of SoftwareYunnan UniversityKunming650091China.
2School of Information Science and EngineeringYunnan UniversityKunming650091China.
IEEE J Transl Eng Health Med. 2020 Apr 23;8:1400310. doi: 10.1109/JTEHM.2020.2984601. eCollection 2020.
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.
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患者数量逐年增加。AD的发病有很长的临床前期。如果医生能在轻度认知障碍（MCI）阶段做出初步诊断，就有可能识别和筛查出那些有发展为全面AD高风险的人群，从而减少新的AD患者数量。然而，包括AD数据在内的医学数据集存在问题，如样本数量不足和数据分布不同。迁移学习能够有效解决训练数据和测试数据之间的分布差异以及目标样本数量不足的问题，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，该方法通过三迁移模型确保源数据的可迁移性，并通过集成学习实现高性能。在基准数据集和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，我们提出了一个用于AD初步诊断的辅助诊断系统，该系统有助于医生尽快且高精度地识别处于MCI阶段的患者，以便采取措施预防或延缓AD的发生。
