干旱区生态环境知识资源中心(Arid): Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation

Arid

DOI	10.1016/j.media.2020.101694
	Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation
	Wen, Junhao; Thibeau-Sutre, Elina; Diaz-Melo, Mauricio; Samper-Gonzalez, Jorge; Routier, Alexandre; Bottani, Simona; Dormont, Didier; Durrleman, Stanley; Burgos, Ninon; Colliot, Olivier
通讯作者	Colliot, O
来源期刊	MEDICAL IMAGE ANALYSIS
ISSN	1361-8415
EISSN	1361-8423
出版年	2020
卷号	63
英文摘要	Numerous machine learning (ML) approaches have been proposed for automatic classification of Alzheimer's disease (AD) from brain imaging data. In particular, over 30 papers have proposed to use convolutional neural networks (CNN) for AD classification from anatomical MRI. However, the classification performance is difficult to compare across studies due to variations in components such as participant selection, image preprocessing or validation procedure. Moreover, these studies are hardly reproducible because their frameworks are not publicly accessible and because implementation details are lacking. Lastly, some of these papers may report a biased performance due to inadequate or unclear validation or model selection procedures. In the present work, we aim to address these limitations through three main contributions. First, we performed a systematic literature review. We identified four main types of approaches: i) 2D slice-level, ii) 3D patch-level, iii) ROI-based and iv) 3D subject-level CNN. Moreover, we found that more than half of the surveyed papers may have suffered from data leakage and thus reported biased performance. Our second contribution is the extension of our open-source framework for classification of AD using CNN and T1-weighted MRI. The framework comprises previously developed tools to automatically convert ADNI, AIBL and OASIS data into the BIDS standard, and a modular set of image preprocessing procedures, classification architectures and evaluation procedures dedicated to deep learning. Finally, we used this framework to rigorously compare different CNN architectures. The data was split into training/validation/test sets at the very beginning and only the training/validation sets were used for model selection. To avoid any overfitting, the test sets were left untouched until the end of the peer-review process. Overall, the different 3D approaches (3D-subject, 3D-ROI, 3D-patch) achieved similar performances while that of the 2D slice approach was lower. Of note, the different CNN approaches did not perform better than a SVM with voxel-based features. The different approaches generalized well to similar populations but not to datasets with different inclusion criteria or demographical characteristics. All the code of the framework and the experiments is publicly available: general-purpose tools have been integrated into the Clinica software (www.clinica.run) and the paper-specific code is available at: https://github.com/aramis-lab/AD-DL. (C) 2020 Elsevier B.V. All rights reserved.
英文关键词	Convolutional neural network Reproducibility Alzheimer's disease classification Magnetic resonance imaging
类型	Article
语种	英语
开放获取类型	Green Submitted, Bronze
收录类别	SCI-E
WOS记录号	WOS:000537839100008
WOS关键词	MILD COGNITIVE IMPAIRMENT ; DIFFEOMORPHIC IMAGE REGISTRATION ; EARLY-DIAGNOSIS ; MRI ; EXTRACTION ; PREDICTION ; ENSEMBLE ; SYSTEM ; MCI
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS研究方向	Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/324640
作者单位	[Wen, Junhao; Thibeau-Sutre, Elina; Diaz-Melo, Mauricio; Samper-Gonzalez, Jorge; Routier, Alexandre; Bottani, Simona; Dormont, Didier; Durrleman, Stanley; Burgos, Ninon; Colliot, Olivier] ICM, F-75013 Paris, France; [Wen, Junhao; Thibeau-Sutre, Elina; Diaz-Melo, Mauricio; Samper-Gonzalez, Jorge; Routier, Alexandre; Bottani, Simona; Dormont, Didier; Durrleman, Stanley; Burgos, Ninon; Colliot, Olivier] SorbonneUniv, F-75013 Paris, France; [Wen, Junhao; Thibeau-Sutre, Elina; Diaz-Melo, Mauricio; Samper-Gonzalez, Jorge; Routier, Alexandre; Bottani, Simona; Dormont, Didier; Durrleman, Stanley; Burgos, Ninon; Colliot, Olivier] INSERM, U 1127, F-75013 Paris, France; [Wen, Junhao; Thibeau-Sutre, Elina; Diaz-Melo, Mauricio; Samper-Gonzalez, Jorge; Routier, Alexandre; Bottani, Simona; Dormont, Didier; Durrleman, Stanley; Burgos, Ninon; Colliot, Olivier] CNRS, UMR 7225, F-75013 Paris, France; [Wen, Junhao; Thibeau-Sutre, Elina; Diaz-Melo, Mauricio; Samper-Gonzalez, Jorge; Routier, Alexandre; Bottani, Simona; Dormont...
推荐引用方式 GB/T 7714	Wen, Junhao,Thibeau-Sutre, Elina,Diaz-Melo, Mauricio,et al. Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation[J],2020,63.
APA	Wen, Junhao.,Thibeau-Sutre, Elina.,Diaz-Melo, Mauricio.,Samper-Gonzalez, Jorge.,Routier, Alexandre.,...&Colliot, Olivier.(2020).Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation.MEDICAL IMAGE ANALYSIS,63.
MLA	Wen, Junhao,et al."Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation".MEDICAL IMAGE ANALYSIS 63(2020).