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Titel:

Unsupervised machine learning to analyze corneal tissue surfaces

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
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...     »
Stichworte:
Transformer, Profilometry, Tribology, Machine learning, Interface defects, Biomedical equipment, Tissue diagnostics, Tissues, Covariance and correlation
Dewey Dezimalklassifikation:
500 Naturwissenschaften
Zeitschriftentitel:
APL Machine Learning
Jahr:
2023
Band / Volume:
1
Jahr / Monat:
2023-11
Heft / Issue:
4
Seitenangaben Beitrag:
046107
Nachgewiesen in:
Scopus
Reviewed:
ja
Sprache:
en
Volltext / 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
Verlag / Institution:
AIP Publishing
E-ISSN:
2770-9019
Status:
Verlagsversion / published
Publikationsdatum:
14.11.2023
Semester:
WS 23-24
TUM Einrichtung:
Fachgebiet für Biomechanik, MW
Eingabe:
08.05.2024
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