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Title:

Hippocampal representations for deep learning on Alzheimer's disease.

Document type:
Journal Article; Research Support, Non-U.S. Gov't
Author(s):
Sarasua, Ignacio; Pölsterl, Sebastian; Wachinger, Christian
Abstract:
Deep learning offers a powerful approach for analyzing hippocampal changes in Alzheimer's disease (AD) without relying on handcrafted features. Nevertheless, an input format needs to be selected to pass the image information to the neural network, which has wide ramifications for the analysis, but has not been evaluated yet. We compare five hippocampal representations (and their respective tailored network architectures) that span from raw images to geometric representations like meshes and poin...     »
Journal title abbreviation:
Sci Rep
Year:
2022
Journal volume:
12
Journal issue:
1
Fulltext / DOI:
doi:10.1038/s41598-022-12533-6
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/35597814
Print-ISSN:
2045-2322
TUM Institution:
Institut für Diagnostische und Interventionelle Radiologie
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