干旱区生态环境知识资源中心(Arid): Systematic comparison of 3D Deep learning and classical machine learning explanations for Alzheimer's Disease detection

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DOI	10.1016/j.compbiomed.2024.108029
	Systematic comparison of 3D Deep learning and classical machine learning explanations for Alzheimer's Disease detection
	Bloch, Louise; Friedrich, Christoph M.
通讯作者	Friedrich, CM
来源期刊	COMPUTERS IN BIOLOGY AND MEDICINE
ISSN	0010-4825
EISSN	1879-0534
出版年	2024
卷号	170
英文摘要	Black -box deep learning (DL) models trained for the early detection of Alzheimer's Disease (AD) often lack systematic model interpretation. This work computes the activated brain regions during DL and compares those with classical Machine Learning (ML) explanations. The architectures used for DL were 3D DenseNets, EfficientNets, and Squeeze -and -Excitation (SE) networks. The classical models include Random Forests (RFs), Support Vector Machines (SVMs), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting (LightGBM), Decision Trees (DTs), and Logistic Regression (LR). For explanations, SHapley Additive exPlanations (SHAP) values, Local Interpretable Model -agnostic Explanations (LIME), Gradient -weighted Class Activation Mapping (GradCAM), GradCAM++ and permutation -based feature importance were implemented. During interpretation, correlated features were consolidated into aspects. All models were trained on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The validation includes internal and external validation on the Australian Imaging and Lifestyle flagship study of Ageing (AIBL) and the Open Access Series of Imaging Studies (OASIS). DL and ML models reached similar classification performances. Regarding the brain regions, both types focus on different regions. The ML models focus on the inferior and middle temporal gyri, and the hippocampus, and amygdala regions previously associated with AD. The DL models focus on a wider range of regions including the optical chiasm, the entorhinal cortices, the left and right vessels, and the 4th ventricle which were partially associated with AD. One explanation for the differences is the input features (textures vs. volumes). Both types show reasonable similarity to a ground truth Voxel-Based Morphometry (VBM) analysis. Slightly higher similarities were measured for ML models.
英文关键词	Alzheimer's Disease Interpretable Machine Learning SHAP LIME GradCAM 3D CNN
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:001179326900001
WOS关键词	MILD COGNITIVE IMPAIRMENT ; ATROPHY ; HIPPOCAMPAL ; CORTEX ; BRAIN ; GYRUS ; MRI
WOS类目	Biology ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Mathematical & Computational Biology
WOS研究方向	Life Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403258
推荐引用方式 GB/T 7714	Bloch, Louise,Friedrich, Christoph M.. Systematic comparison of 3D Deep learning and classical machine learning explanations for Alzheimer's Disease detection[J],2024,170.
APA	Bloch, Louise,&Friedrich, Christoph M..(2024).Systematic comparison of 3D Deep learning and classical machine learning explanations for Alzheimer's Disease detection.COMPUTERS IN BIOLOGY AND MEDICINE,170.
MLA	Bloch, Louise,et al."Systematic comparison of 3D Deep learning and classical machine learning explanations for Alzheimer's Disease detection".COMPUTERS IN BIOLOGY AND MEDICINE 170(2024).