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.
@article{arxiv.2312.12619,
title = {Hierarchical Vision Transformers for Context-Aware Prostate Cancer Grading in Whole Slide Images},
author = {Clément Grisi and Geert Litjens and Jeroen van der Laak},
journal= {arXiv preprint arXiv:2312.12619},
year = {2023}
}
Comments
Accepted at Medical Imaging meets NeurIPS 2023 workshop