Automated Grading of Oral Squamous Cell Carcinoma into Multiple Classes Using Deep Learning Methods (CROSBI ID 709552)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Musulin, Jelena ; Štifanić, Daniel ; Zulijani, Ana ; Baressi Šegota, Sandi ; Lorencin, Ivan ; Anđelić, Nikola ; Car, Zlatan
engleski
Automated Grading of Oral Squamous Cell Carcinoma into Multiple Classes Using Deep Learning Methods
The diagnosis of oral squamous cell carcinoma is based on a histopathological examination, which is still the most reliable way of identifying oral cancer despite its high subjectivity. However, due to the heterogeneous structure and textures of oral cancer, as well as the presence of any inflammatory tissue reaction, histopathological classification can be difficult. For that reason, an automatic classification of histopathology images with the help of artificial intelligence- assisted technologies can not only improve objective diagnostic results for the clinician but also provide extensive texture analysis to get a correct diagnosis. In this paper various deep learning methods are compared in order to get an AI-based model for multiclass grading of OSCC with the highest AUCmicro and AUCmacro values.
artificial intelligence, deep learning methods, histopathology images, oral cancer
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Podaci o prilogu
T.1A.2
2021.
objavljeno
Podaci o matičnoj publikaciji
The 21st IEEE International Conference on BioInformatics and BioEngineering
Filipović, Nenad
Kragujevac: Institute of Electrical and Electronics Engineers (IEEE)
978-86-81037-69-0
Podaci o skupu
21st IEEE International Conference on BioInformatics and BioEngineering (BIBE 2021)
predavanje
25.10.2021-27.10.2021
Kragujevac, Srbija