prikaz prve stranice dokumenta An Enhanced Histopathology Analysis: An AI- Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue
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Scientific paper - Original scientific paper
An Enhanced Histopathology Analysis: An AI- Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue
Cancers, 13 (2021), 8; 1-21. https://doi.org/10.3390/cancers13081784
Musulin, Jelena; Štifanić, Daniel; Zulijani, Ana; Ćabov, Tomislav; Dekanić, Andrea; Car, Zlatan

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Musulin, J., Štifanić, D., Zulijani, A., Ćabov, T., Dekanić, A. & Car, Z. (2021). An Enhanced Histopathology Analysis: An AI- Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue. Cancers, 13. (8), 1-21. doi: 10.3390/cancers13081784

Musulin, Jelena, et al. "An Enhanced Histopathology Analysis: An AI- Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue." Cancers, vol. 13, no. 8, 2021, pp. 1-21. https://doi.org/10.3390/cancers13081784

Musulin, Jelena, Daniel Štifanić, Ana Zulijani, Tomislav Ćabov, Andrea Dekanić and Zlatan Car. "An Enhanced Histopathology Analysis: An AI- Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue." Cancers 13, no. 8 (2021): 1-21. https://doi.org/10.3390/cancers13081784

Musulin, J., et al. (2021) 'An Enhanced Histopathology Analysis: An AI- Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue', Cancers, 13(8), pp. 1-21. doi: 10.3390/cancers13081784

Musulin J, Štifanić D, Zulijani A, Ćabov T, Dekanić A, Car Z. An Enhanced Histopathology Analysis: An AI- Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue. Cancers [Internet]. 2021 [cited 2024 December 21];13(8):1-21. doi: 10.3390/cancers13081784

J. Musulin, D. Štifanić, A. Zulijani, T. Ćabov, A. Dekanić and Z. Car, "An Enhanced Histopathology Analysis: An AI- Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue", Cancers, vol. 13, no. 8, pp. 1-21, 2021. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:271:015974. [Accessed: 21 December 2024]

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Title (english)An Enhanced Histopathology Analysis: An AI- Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue
AuthorJelena Musulin
AuthorDaniel Štifanić
AuthorAna Zulijani
AuthorTomislav Ćabov
AuthorAndrea Dekanić
MBZ: 336525
AuthorZlatan Car
Author's institutionUniversity of Rijeka
(Faculty of Dental Medicine)
Scientific / art field,
discipline and subdiscipline		BIOMEDICINE AND HEALTHCARE
Dental Medicine
Abstract (english)
Oral squamous cell carcinoma is most frequent histological neoplasm of head and neck cancers, and although it is localized in a region that is accessible to see and can be detected very early, this usually does not occur. The standard procedure for the diagnosis of oral cancer is based on histopathological examination, however, the main problem in this kind of procedure is tumor heterogeneity where a subjective component of the examination could directly impact patient- specific treatment intervention. For this reason, artificial intelligence (AI) algorithms are widely used as computational aid in the diagnosis for classification and segmentation of tumors, in order to reduce inter- and intra-observer variability. In this research, a two-stage AI- based system for automatic multiclass grading (the first stage) and segmentation of the epithelial and stromal tissue (the second stage) from oral histopathological images is proposed in order to assist the clinician in oral squamous cell carcinoma diagnosis. The integration of Xception and SWT resulted in the highest classification value of 0.963 (σ = 0.042) AUCmacro and 0.966 (σ = 0.027) AUCmicro while using DeepLabv3+ along with Xception_65 as backbone and data preprocessing, semantic segmentation prediction resulted in 0.878 (σ = 0.027) mIOU and 0.955 (σ = 0.014) F1 score. Obtained results reveal that the proposed AI- based system has great potential in the diagnosis of OSCC.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleCancers
Numberingvol. 13, no. 8, pp. 1-21
p-ISSN2072-6694
DOIhttps://doi.org/10.3390/cancers13081784
URN:NBNurn:nbn:hr:271:015974
Publication2021
Document URLhttp://bib.irb.hr/1123370
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Created on2023-01-23 07:55:01