[1]公丕强,闫作剑,李雪,等.深度学习的阿尔兹海默症影像分类方法[J].中国医学物理学杂志,2025,42(11):1420-1433.[doi:DOI:10.3969/j.issn.1005-202X.2025.11.004]
　GONG Piqiang,YAN Zuojian,et al.Deep learning approaches for image-based classification of Alzheimers disease[J].Chinese Journal of Medical Physics,2025,42(11):1420-1433.[doi:DOI:10.3969/j.issn.1005-202X.2025.11.004]
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深度学习的阿尔兹海默症影像分类方法()

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
42
期数:
2025年第11期
页码:
1420-1433
栏目:
医学影像物理
出版日期:
2025-11-28

文章信息/Info

Title:
Deep learning approaches for image-based classification of Alzheimers disease
文章编号:
1005-202X（2025）11-1420-14
作者:
公丕强1; 2; 闫作剑2; 李雪1; 2; 林冬梅3; 陈扶明2
1.甘肃中医药大学医学信息工程学院， 甘肃 兰州 730000； 2.中国人民解放军联勤保障部队第940医院医学工程科， 甘肃 兰州 730050； 3.兰州理工大学电气工程与信息学院， 甘肃 兰州 730050
Author(s):
GONG Piqiang1;  2;  YAN Zuojian2;  LI Xue1;  2;  LIN Dongmei3;  CHEN Fuming2
1. School of Medical Information Engineering, Gansu University of Chinese Medicine, Lanzhou 730000, China 2. Department of Engineering, the 940th Hospital of Joint Logistics Support Force of Chinese Peoples Liberation Army, Lanzhou 730050, China 3. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
关键词:
阿尔兹海默症; 深度学习; 磁共振成像; 神经影像; 综述
Keywords:
Alzheimers disease deep learning magnetic resonance imaging neuroimaging review
分类号:
R318；R749.1
DOI:
DOI:10.3969/j.issn.1005-202X.2025.11.004
文献标志码:
A
摘要:
阿尔兹海默症（AD）是一种进行性且不可逆的神经退行性疾病，随着脑细胞逐渐退化，认知能力逐步下降，最终可能导致死亡。在AD的诊断过程中，早期识别与干预是至关重要的。近年来，深度学习进一步推动AD影像分类方法的发展，并促进深度模型在临床早期诊断AD中的应用。为临床实现精确的早期诊断进而对AD患者进行分类，研究者结合深度学习和MRI成像，提供更加精确的模型。本文对国内外相关文献进行分析和总结，介绍AD常用的公开数据集和评价标准，分析MRI成像在AD分类中的应用及其与深度学习方法的融合，重点对CNN、迁移学习、注意力机制和多模态等技术在AD分类中的应用进行整理分析，讨论深度学习在AD分类应用中的优缺点及发展趋势，旨在为深度学习在AD领域的应用研究开辟新的思路。
Abstract:
Alzheimers disease (AD) is a progressive, irreversible neurodegenerative disorder characterized by gradual brain cell degeneration, leading to progressive decline in cognitive function and ultimately death. Early identification and intervention are critical to AD diagnosis. In recent years, deep learning has further advanced image-based AD classification methods and facilitated the application of deep models in the early AD diagnosis. To achieve accurate early diagnosis and subsequent classification of AD, researchers have integrated deep learning with magnetic resonance imaging to develop more precise models. By analyzing and synthesizing relevant domestic and international literature, this review introduces commonly used public datasets and evaluation criteria for AD, analyzes the application of magnetic resonance imaging in AD classification and its integration with deep learning methods, and highlights the roles of techniques such as convolutional neural networks, transfer learning, attention mechanisms, and multimodal approaches in AD classification. It also discusses the advantages, limitations, and developmental trends of deep learning in AD classification, aiming to provide new insights for the application of deep learning in AD research.

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备注/Memo

备注/Memo:
【收稿日期】2025-07-11 【基金项目】国家自然科学基金（61901515）；甘肃省自然科学基金（22JR5RA002） 【作者简介】公丕强，硕士研究生，研究方向：医学图像处理，E-mail: 627556123@qq.com 【通信作者】陈扶明，正高级工程师，研究方向：生物医学信号检测与处理，E-mail: cfm5762@126.com
更新日期/Last Update: 2025-12-01