Hierarchical Vision Transformers for Context-Aware Prostate Cancer Grading in Whole Slide Images
This addresses computational pathology needs for efficient cancer grading, but it is incremental as it adapts an existing hierarchical approach from text to medical images.
The paper tackled the challenge of applying Vision Transformers to large whole slide images for prostate cancer grading by proposing Hierarchical Vision Transformers, achieving competitive performance against state-of-the-art methods.
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.