Transformer-based Model for Oral Epithelial Dysplasia Segmentation
Adam J Shephard Hanya Mahmood Shan E Ahmed Raza Anna Luiza Damaceno Araujo Alan Roger Santos-Silva Marcio Ajudarte Lopes Pablo Agustin Vargas Kris McCombe Stephanie Craig Jacqueline James Jill Brooks Paul Nankivell Hisham Mehanna Syed Ali Khurram Nasir M Rajpoot
Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis
given to lesions of the oral cavity. OED grading is subject to large
inter/intra-rater variability, resulting in the under/over-treatment of
patients. We developed a new Transformer-based pipeline to improve detection
and segmentation of OED in haematoxylin and eosin (H&E) stained whole slide
images (WSIs). Our model was trained on OED cases (n = 260) and controls (n =
105) collected using three different scanners, and validated on test data from
three external centres in the United Kingdom and Brazil (n = 78). Our internal
experiments yield a mean F1-score of 0.81 for OED segmentation, which reduced
slightly to 0.71 on external testing, showing good generalisability, and
gaining state-of-the-art results. This is the first externally validated study
to use Transformers for segmentation in precancerous histology images. Our
publicly available model shows great promise to be the first step of a
fully-integrated pipeline, allowing earlier and more efficient OED diagnosis,
ultimately benefiting patient outcomes.