s2MRI-ADNet: an interpretable deep learning framework integrating Euclidean-graph representations of Alzheimer’s disease solely from structural MRI

Objective To establish a multi-dimensional representation solely on structural MRI (sMRI) for early diagnosis of AD. Methods A total of 3377 participants’ sMRI from four independent databases were retrospectively identified to construct an interpretable deep learning model that integrated multi-dime...

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Veröffentlicht in:	Magma (New York, N.Y.) Jg. 37; H. 5; S. 845 - 857
Hauptverfasser:	Song, Zhiwei, Li, Honglun, Zhang, Yiyu, Zhu, Chuanzhen, Jiang, Minbo, Song, Limei, Wang, Yi, Ouyang, Minhui, Hu, Fang, Zheng, Qiang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.10.2024
Schlagworte:	Basic Science - Reconstruction algorithms and artificial intelligence Biomedical Engineering and Bioengineering Computer Appl. in Life Sciences Health Informatics Imaging Medicine Medicine & Public Health Radiology Research Article Solid State Physics Deep learning Structural MRI Multi-dimensional representations Alzheimer’s disease Interpretability
ISSN:	1352-8661, 1352-8661
Online-Zugang:	Volltext
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