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Title:

Unsupervised machine learning to analyze corneal tissue surfaces

Document type:
Zeitschriftenaufsatz
Author(s):
Rickert, Carolin A.; Henkel, Fabio; Lieleg, Oliver
Abstract:
Identifying/classifying damage features on soft materials, such as tissues, is much more challenging than on classical, hard materials—but nevertheless important, especially in the field of bio-tribology. For instance, cartilage samples from osteoarthritic patients exhibit surface damage even at early stages of tissue degeneration, and corneal tissues can be damaged by contact lenses when the ocular lubrication system fails. Here, we employ unsupervised machine learning (ML) methods to assess th...     »
Keywords:
Transformer, Profilometry, Tribology, Machine learning, Interface defects, Biomedical equipment, Tissue diagnostics, Tissues, Covariance and correlation
Dewey Decimal Classification:
500 Naturwissenschaften
Journal title:
APL Machine Learning
Year:
2023
Journal volume:
1
Year / month:
2023-11
Journal issue:
4
Pages contribution:
046107
Covered by:
Scopus
Reviewed:
ja
Language:
en
Fulltext / DOI:
doi:10.1063/5.0159502
WWW:
https://pubs.aip.org/aip/aml/article/1/4/046107/2921043/Unsupervised-machine-learning-to-analyze-corneal?searchresult=1
Publisher:
AIP Publishing
E-ISSN:
2770-9019
Status:
Verlagsversion / published
Date of publication:
14.11.2023
Semester:
WS 23-24
TUM Institution:
Fachgebiet für Biomechanik, MW
Ingested:
08.05.2024
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