用迁移学习提升阿尔茨海默病识别准确率，尤其适合数据少的场景。
Emergence of Transfer Learning towards Specific Identification of Alzheimer's Disease A Prospective Approach
- 利用预训练模型迁移知识，减少小样本下模型过拟合
- 结合可解释AI增强诊断结果可信度，提升临床实用性
- 适合刚入门神经退行性疾病研究的学者参考
全球数百万老年人正受阿尔茨海默病（AD）困扰，其特征为记忆障碍、认知功能下降及意识困难。深度学习（DL）与机器学习（ML）模型被广泛用于从高维神经影像数据中识别AD相关模式，但普遍存在全局优化需求和过拟合问题，导致测试集表现不佳。尽管深度卷积网络通过卷积核处理图像可缓解部分问题，但MRI图像中的突发变化或人为操作、预处理不足仍可能导致模型误判。迁移学习（TL）通过在大规模数据集上预训练模型，指导新神经影像数据集上的模型训练，在AD诊断中展现出显著优势。本文系统综述了TL在分类、识别及疾病转化预测中的应用，评估其在有限数据下提升诊断准确性的潜力，并首次将可解释AI引入基于TL的AD诊断体系。该综述可为从事迁移学习驱动神经退行性疾病检测的新研究者提供指导。
原文摘要 · Abstract (English)
Worldwide, millions of senior citizens are suffering from Alzheimer disease abbreviated as AD, a well- versed form of dementia. AD is featured by amnesia, intellectual disability, and difficulty with consciousness. DL and ML models are undoubtedly explored to identify AD related patterns on large dimensional neuroimaging data but they need global optimization and are suffering from overfitting issue that might yield dissatisfactory result in testing data set. DL overcomes the issue by convolution of input image with kernel but any sudden change in the MRI image or human manipulation, limited pre- processing of the images can mislead CNN in achieving highly accurate detection. Transfer Learning (TL) has proved itself in AD diagnosis by utilizing pre-trained models on large data sets to guide novice model in a new neuroimaging dataset. This review provides an inclusive glimpse of TL implication in classification, identification including the conversion of AD. Keeping in view, we have assessed the strengths and limitations of TL in improvising diagnostic accuracy even with limited data. The uniqueness of the present review is the incorporation of explainable AI in TL based AD diagnosis system. Finally, it can be claimed that the review will guide the new re-searchers in the area of TL induced neurodegenerative disease detection.
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