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Vision Transformers (ViTs) have ushered in a new era in computer vision,showcasing unparalleled performance in many challenging tasks. However, theirpractical deployment in computational pathology has largely been constrained bythe sheer size of whole slide images (WSIs), which result in lengthy inputsequences. Transformers faced a similar limitation when applied to longdocuments, and Hierarchical Transformers were introduced to circumvent it.Given the analogous challenge with WSIs and their inherent hierarchicalstructure, Hierarchical Vision Transformers (H-ViTs) emerge as a promisingsolution in computational pathology. This work delves into the capabilities ofH-ViTs, evaluating their efficiency for prostate cancer grading in WSIs. Ourresults show that they achieve competitive performance against existingstate-of-the-art solutions.

Hierarchical Vision Transformers for Context-Aware Prostate Cancer Grading in Whole Slide Images