Hierarchical Vision Transformers for Context-Aware Prostate Cancer
  Grading in Whole Slide Images
Clément Grisi Geert Litjens Jeroen van der Laak
Vision Transformers (ViTs) have ushered in a new era in computer vision,
showcasing unparalleled performance in many challenging tasks. However, their
practical deployment in computational pathology has largely been constrained by
the sheer size of whole slide images (WSIs), which result in lengthy input
sequences. Transformers faced a similar limitation when applied to long
documents, and Hierarchical Transformers were introduced to circumvent it.
Given the analogous challenge with WSIs and their inherent hierarchical
structure, Hierarchical Vision Transformers (H-ViTs) emerge as a promising
solution in computational pathology. This work delves into the capabilities of
H-ViTs, evaluating their efficiency for prostate cancer grading in WSIs. Our
results show that they achieve competitive performance against existing
state-of-the-art solutions.