  In digital histopathology, entire neoplasm segmentation on Whole Slide Image
(WSI) of Hepatocellular Carcinoma (HCC) plays an important role, especially as
a preprocessing filter to automatically exclude healthy tissue, in histological
molecular correlations mining and other downstream histopathological tasks. The
segmentation task remains challenging due to HCC's inherent high-heterogeneity
and the lack of dependency learning in large field of view. In this article, we
propose a novel deep learning architecture with a hierarchical Transformer
encoder, HiTrans, to learn the global dependencies within expanded
4096$\times$4096 WSI patches. HiTrans is designed to encode and decode the
patches with larger reception fields and the learned global dependencies,
compared to the state-of-the-art Fully Convolutional Neural networks (FCNN).
Empirical evaluations verified that HiTrans leads to better segmentation
performance by taking into account regional and global dependency information.
