Recently, deep learning approaches have been used to successfully show a high level of accuracy for such tasks. In addition, the diagnostic abilities and expertise of any pathologist is dependent on their direct experience with common as well as rarer variant morphologies. We show that a random forest model using these features can differentiate between low-risk and high-risk OED lesions.Īn optical microscopic examination of thinly cut stained tissue on glass slides prepared from a FFPE tissue blocks is the gold standard for tissue diagnostics. Accurate nuclear segmentation allows us to perform quantitative statistical and morphometric feature analyses of the segmented nuclei within regions of interest (ROIs) of multi-gigapixel whole-slide images (WSIs).
DEEPFOCUS THEME CROP IMAGES FULL
Our initial experimental results show that the NAS-derived architecture achieves 93.5% F1-score for the full epithelium segmentation and 94.5% for nuclear segmentation outperforming other state-of-the-art models. In this paper, we explore a customised neural architecture search (NAS) based method for optimisation of an efficient architecture for segmentation of the full epithelium and individual nuclei in pathology whole slide images (WSIs). Architectural, cytological and histological features of OED can be modelled through the segmentation of full epithelium, individual nuclei and stroma (connective tissues) to provide significant diagnostic features.
Oral epithelial dysplasia (OED) is a pre-cancerous histopathological diagnosis given to a range of oral lesions.
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