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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.
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Thursday 7th July
Poster Session 2.2 - onsite 11:00 - 12:00, virtual 15:20 - 16:20 (UTC+2)
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