AI-Based Detection of Oral Squamous Cell Carcinoma with Raman Histology.
computational biology
head and neck neoplasms
machine learning
neural networks
pathology
Journal
Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829
Informations de publication
Date de publication:
06 Feb 2024
06 Feb 2024
Historique:
received:
15
01
2024
revised:
02
02
2024
accepted:
02
02
2024
medline:
24
2
2024
pubmed:
24
2
2024
entrez:
24
2
2024
Statut:
epublish
Résumé
Stimulated Raman Histology (SRH) employs the stimulated Raman scattering (SRS) of photons at biomolecules in tissue samples to generate histological images. Subsequent pathological analysis allows for an intraoperative evaluation without the need for sectioning and staining. The objective of this study was to investigate a deep learning-based classification of oral squamous cell carcinoma (OSCC) and the sub-classification of non-malignant tissue types, as well as to compare the performances of the classifier between SRS and SRH images. Raman shifts were measured at wavenumbers k
Identifiants
pubmed: 38398080
pii: cancers16040689
doi: 10.3390/cancers16040689
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : Federal Ministry of Education and Research
ID : 13GW0571D