PATHS: A Hierarchical Transformer for Efficient Whole Slide Image Analysis
This work improves computational pathology for diagnostic and prognostic tasks by reducing noise and computational cost, though it is incremental as it builds on existing bag-of-patches methods with a novel hierarchical approach.
The paper tackled the problem of analyzing whole slide images (WSIs) by addressing noise from uninformative patches, proposing PATHS, a hierarchical transformer that recursively filters patches to focus on relevant regions, achieving superior performance on slide-level prediction tasks across five TCGA datasets.
Computational analysis of whole slide images (WSIs) has seen significant research progress in recent years, with applications ranging across important diagnostic and prognostic tasks such as survival or cancer subtype prediction. Many state-of-the-art models process the entire slide - which may be as large as $150,000 \times 150,000$ pixels - as a bag of many patches, the size of which necessitates computationally cheap feature aggregation methods. However, a large proportion of these patches are uninformative, such as those containing only healthy or adipose tissue, adding significant noise and size to the bag. We propose Pathology Transformer with Hierarchical Selection (PATHS), a novel top-down method for hierarchical weakly supervised representation learning on slide-level tasks in computational pathology. PATHS is inspired by the cross-magnification manner in which a human pathologist examines a slide, recursively filtering patches at each magnification level to a small subset relevant to the diagnosis. Our method overcomes the complications of processing the entire slide, enabling quadratic self-attention and providing a simple interpretable measure of region importance. We apply PATHS to five datasets of The Cancer Genome Atlas (TCGA), and achieve superior performance on slide-level prediction tasks when compared to previous methods, despite processing only a small proportion of the slide.