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Proceedings Paper

Automated grade classification of oral epithelial dysplasia using morphometric analysis of histology images
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Paper Abstract

Oral dysplasia is a pre-malignant stage of oral epithelial carcinomas, e.g., oral squamous cell carcinoma, where significant changes in tissue layers and cells can be observed under the microscope. However, malignancy can be reverted or cured using proper medication or surgery if the grade of malignancy is assessed properly. The assessment of correct grade is therefore critical in patient management as it can change the treatment decisions and prognosis for the dysplastic lesion. This assessment is highly challenging due to considerable inter- and intraobserver variability in pathologists’ agreement, which highlights the need for an automated grading system that can predict more accurate and reliable grade. Recent advancements have made it possible for digital pathology (DP) and artificial intelligence (AI) to join forces from the digitization of tissue slides into images and using those images to train and predict more accurate grades using complex AI models. In this regard, we propose a novel morphometric approach exploiting the architectural features in dysplastic lesions i.e., irregular epithelial stratification where we measure the widths of different layers of the epithelium from the boundary layer i.e., keratin projecting inwards to the epithelium and basal layers to the rest of the tissue section from a clinically significant viewpoint.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 1132011 (16 March 2020); doi: 10.1117/12.2549705
Show Author Affiliations
R. M. Saad Bashir, The Univ. of Warwick (United Kingdom)
Hanya Mahmood, The Univ. of Sheffield (United Kingdom)
Muhammad Shaban, The Univ. of Warwick (United Kingdom)
Shan E. Ahmed Raza, The Univ. of Warwick (United Kingdom)
M. Moazam Fraz, National Univ. of Sciences and Technology (Pakistan)
Syed Ali Khurram, The Univ. of Sheffield (United Kingdom)
Nasir M. Rajpoot, The Univ. of Warwick (United Kingdom)


Published in SPIE Proceedings Vol. 11320:
Medical Imaging 2020: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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