A Hierarchical Transformer Encoder to Improve Entire Neoplasm
  Segmentation on Whole Slide Image of Hepatocellular Carcinoma
Zhuxian Guo Qitong Wang Henning Müller Themis Palpanas Nicolas Loménie Camille Kurtz
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.