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.

我们开发了一种基于Transformer的新管线，以改善血红素和伊红染色全切片图像中口腔上皮畸形（OED）的检测和分割。我们的模型在从英国和巴西三个外部中心收集的测试数据上验证后，表现良好，并取得了最先进的结果。这是第一项在癌前组织病理学图像中使用Transformer进行分割的外部验证研究，我们公开提供的模型显示出巨大的潜力，可成为完全集成管线的第一步，实现更早和更有效的OED诊断，最终使患者受益。

基于Transformer的口腔上皮异型增生分割模型