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For: Hu J, Qing Z, Liu R, Zhang X, Lv P, Wang M, Wang Y, He K, Gao Y, Zhang B. Deep Learning-Based Classification and Voxel-Based Visualization of Frontotemporal Dementia and Alzheimer's Disease. Front Neurosci 2020;14:626154. [PMID: 33551735 DOI: 10.3389/fnins.2020.626154] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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4 Tufail AB, Anwar N, Othman MTB, Ullah I, Khan RA, Ma YK, Adhikari D, Rehman AU, Shafiq M, Hamam H. Early-Stage Alzheimer's Disease Categorization Using PET Neuroimaging Modality and Convolutional Neural Networks in the 2D and 3D Domains. Sensors (Basel) 2022;22:4609. [PMID: 35746389 DOI: 10.3390/s22124609] [Reference Citation Analysis]
5 McKenna MC, Murad A, Huynh W, Lope J, Bede P. The changing landscape of neuroimaging in frontotemporal lobar degeneration: from group-level observations to single-subject data interpretation. Expert Rev Neurother 2022. [PMID: 35227146 DOI: 10.1080/14737175.2022.2048648] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
6 Mckenna MC, Tahedl M, Murad A, Lope J, Hardiman O, Hutchinson S, Bede P. White matter microstructure alterations in frontotemporal dementia: Phenotype‐associated signatures and single‐subject interpretation. Brain and Behavior. [DOI: 10.1002/brb3.2500] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Kang W, Lin L, Zhang B, Shen X, Wu S; Alzheimer's Disease Neuroimaging Initiative. Multi-model and multi-slice ensemble learning architecture based on 2D convolutional neural networks for Alzheimer's disease diagnosis. Comput Biol Med 2021;136:104678. [PMID: 34329864 DOI: 10.1016/j.compbiomed.2021.104678] [Reference Citation Analysis]