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Titel:

Reducing variations in multi-center Alzheimer's disease classification with convolutional adversarial autoencoder.

Dokumenttyp:
Journal Article; Research Support, Non-U.S. Gov't
Autor(en):
Cobbinah, Bernard M; Sorg, Christian; Yang, Qinli; Ternblom, Arvid; Zheng, Changgang; Han, Wei; Che, Liwei; Shao, Junming
Abstract:
Based on brain magnetic resonance imaging (MRI), multiple variations ranging from MRI scanners to center-specific parameter settings, imaging protocols, and brain region-of-interest (ROI) definitions pose a big challenge for multi-center Alzheimer's disease characterization and classification. Existing approaches to reduce such variations require intricate multi-step, often manual preprocessing pipelines, including skull stripping, segmentation, registration, cortical reconstruction, and ROI out...     »
Zeitschriftentitel:
Med Image Anal
Jahr:
2022
Band / Volume:
82
Volltext / DOI:
doi:10.1016/j.media.2022.102585
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/36057187
Print-ISSN:
1361-8415
TUM Einrichtung:
Professur für Neuroradiologie (Prof. Zimmer)
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