  Oral epithelial dysplasia (OED) is a pre-malignant histopathological
diagnosis given to lesions of the oral cavity. Predicting OED grade or whether
a case will transition to malignancy is critical for early detection and
appropriate treatment. OED typically begins in the lower third of the
epithelium before progressing upwards with grade severity, thus we have
suggested that segmenting intra-epithelial layers, in addition to individual
nuclei, may enable researchers to evaluate important layer-specific
morphological features for grade/malignancy prediction. We present HoVer-Net+,
a deep learning framework to simultaneously segment (and classify) nuclei and
(intra-)epithelial layers in H&E stained slides from OED cases. The proposed
architecture consists of an encoder branch and four decoder branches for
simultaneous instance segmentation of nuclei and semantic segmentation of the
epithelial layers. We show that the proposed model achieves the
state-of-the-art (SOTA) performance in both tasks, with no additional costs
when compared to previous SOTA methods for each task. To the best of our
knowledge, ours is the first method for simultaneous nuclear instance
segmentation and semantic tissue segmentation, with potential for use in
computational pathology for other similar simultaneous tasks and for future
studies into malignancy prediction.
