A Fully Automated Multi-Scale Pipeline for Oral Epithelial Dysplasia Grading and Outcome Prediction
Adam Shephard, Neda Azarmehr, Raja Muhammad Saad Bashir, Shan E Ahmed Raza, Hanya Mahmood, Syed Ali Khurram, Nasir Rajpoot
Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity, characterised by changes to the nuclear morphometry and the epithelial layers. In this work, we have finetuned HoVer-Net+ for the simultaneous segmentation of nuclei and the epithelial layers in heamatoxylin and eosin (H&E) stained whole slide images (WSIs). We then employed a multi-scale attention-based multiple instance learning architecture for the prediction of OED status, grade, recurrence and malignant transformation. The impressive results have demonstrated the potential of such methods.
Thursday 7th July
Poster Session 2.2 - onsite 11:00 - 12:00, virtual 15:20 - 16:20 (UTC+2)