  Whole Slide Images (WSIs) present a challenging computer vision task due to
their gigapixel size and presence of numerous artefacts. Yet they are a
valuable resource for patient diagnosis and stratification, often representing
the gold standard for diagnostic tasks. Real-world clinical datasets tend to
come as sets of heterogeneous WSIs with labels present at the patient-level,
with poor to no annotations. Weakly supervised attention-based multiple
instance learning approaches have been developed in recent years to address
these challenges, but can fail to resolve both long and short-range
dependencies. Here we propose an end-to-end multi-stain self-attention graph
(MUSTANG) multiple instance learning pipeline, which is designed to solve a
weakly-supervised gigapixel multi-image classification task, where the label is
assigned at the patient-level, but no slide-level labels or region annotations
are available. The pipeline uses a self-attention based approach by restricting
the operations to a highly sparse k-Nearest Neighbour Graph of embedded WSI
patches based on the Euclidean distance. We show this approach achieves a
state-of-the-art F1-score/AUC of 0.89/0.92, outperforming the widely used CLAM
model. Our approach is highly modular and can easily be modified to suit
different clinical datasets, as it only requires a patient-level label without
annotations and accepts WSI sets of different sizes, as the graphs can be of
varying sizes and structures. The source code can be found at
https://github.com/AmayaGS/MUSTANG.
