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Pregled bibliografske jedinice broj: 1154226

Automated Grading of Oral Squamous Cell Carcinoma into Multiple Classes Using Deep Learning Methods


Musulin, Jelena; Štifanić, Daniel; Zulijani, Ana; Baressi Šegota, Sandi; Lorencin, Ivan; Anđelić, Nikola; Car, Zlatan
Automated Grading of Oral Squamous Cell Carcinoma into Multiple Classes Using Deep Learning Methods // The 21st IEEE International Conference on BioInformatics and BioEngineering / Filipović, Nenad (ur.).
Kragujevac: IEEE, 2021. T.1A.2, 6 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


CROSBI ID: 1154226 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Automated Grading of Oral Squamous Cell Carcinoma into Multiple Classes Using Deep Learning Methods

Autori
Musulin, Jelena ; Štifanić, Daniel ; Zulijani, Ana ; Baressi Šegota, Sandi ; Lorencin, Ivan ; Anđelić, Nikola ; Car, Zlatan

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
The 21st IEEE International Conference on BioInformatics and BioEngineering / Filipović, Nenad - Kragujevac : IEEE, 2021

ISBN
978-86-81037-69-0

Skup
The 21st IEEE International Conference on BioInformatics and BioEngineering

Mjesto i datum
Kragujevac, Srbija, 25-27.10.2021

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
artificial intelligence, deep learning methods, histopathology images, oral cancer

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Dentalna medicina



POVEZANOST RADA


Projekti:
Ostalo-CEI - 305.6019-20 - Use of regressive artificial intelligence (AI) and machine learning (ML) methods in modelling of COVID-19 spread (COVIDAi) (Car, Zlatan, Ostalo - CEI Extraordinary Call for Proposals 2020) ( POIROT)
InoUstZnVO-CIII-HR-0108-10 - Concurrent Product and Technology Development - Teaching, Research and Implementation of Joint Programs Oriented in Production and Industrial Engineering (Car, Zlatan, InoUstZnVO - CEEPUS) ( POIROT)
EK-KF-KK.01.1.1.01.009 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (DATACROSS) (Lončarić, Sven; Petrović, Ivan; Šmuc, Tomislav; Jokić, Andrej, EK - KK.01.1.1.01) ( POIROT)

Ustanove:
Tehnički fakultet, Rijeka,
Klinički bolnički centar Rijeka

Poveznice na cjeloviti tekst rada:

Pristup cjelovitom tekstu rada

Citiraj ovu publikaciju:

Musulin, Jelena; Štifanić, Daniel; Zulijani, Ana; Baressi Šegota, Sandi; Lorencin, Ivan; Anđelić, Nikola; Car, Zlatan
Automated Grading of Oral Squamous Cell Carcinoma into Multiple Classes Using Deep Learning Methods // The 21st IEEE International Conference on BioInformatics and BioEngineering / Filipović, Nenad (ur.).
Kragujevac: IEEE, 2021. T.1A.2, 6 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Musulin, J., Štifanić, D., Zulijani, A., Baressi Šegota, S., Lorencin, I., Anđelić, N. & Car, Z. (2021) Automated Grading of Oral Squamous Cell Carcinoma into Multiple Classes Using Deep Learning Methods. U: Filipović, N. (ur.)The 21st IEEE International Conference on BioInformatics and BioEngineering.
@article{article, author = {Musulin, Jelena and \v{S}tifani\'{c}, Daniel and Zulijani, Ana and Baressi \v{S}egota, Sandi and Lorencin, Ivan and An\djeli\'{c}, Nikola and Car, Zlatan}, editor = {Filipovi\'{c}, N.}, year = {2021}, pages = {6}, chapter = {T.1A.2}, keywords = {artificial intelligence, deep learning methods, histopathology images, oral cancer}, isbn = {978-86-81037-69-0}, title = {Automated Grading of Oral Squamous Cell Carcinoma into Multiple Classes Using Deep Learning Methods}, keyword = {artificial intelligence, deep learning methods, histopathology images, oral cancer}, publisher = {IEEE}, publisherplace = {Kragujevac, Srbija}, chapternumber = {T.1A.2} }
@article{article, author = {Musulin, Jelena and \v{S}tifani\'{c}, Daniel and Zulijani, Ana and Baressi \v{S}egota, Sandi and Lorencin, Ivan and An\djeli\'{c}, Nikola and Car, Zlatan}, editor = {Filipovi\'{c}, N.}, year = {2021}, pages = {6}, chapter = {T.1A.2}, keywords = {artificial intelligence, deep learning methods, histopathology images, oral cancer}, isbn = {978-86-81037-69-0}, title = {Automated Grading of Oral Squamous Cell Carcinoma into Multiple Classes Using Deep Learning Methods}, keyword = {artificial intelligence, deep learning methods, histopathology images, oral cancer}, publisher = {IEEE}, publisherplace = {Kragujevac, Srbija}, chapternumber = {T.1A.2} }




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