  Whole slide image (WSI) refers to a type of high-resolution scanned tissue
image, which is extensively employed in computer-assisted diagnosis (CAD). The
extremely high resolution and limited availability of region-level annotations
make employing deep learning methods for WSI-based digital diagnosis
challenging. Recently integrating multiple instance learning (MIL) and
Transformer for WSI analysis shows very promising results. However, designing
effective Transformers for this weakly-supervised high-resolution image
analysis is an underexplored yet important problem. In this paper, we propose a
Multi-level MIL (MMIL) scheme by introducing a hierarchical structure to MIL,
which enables efficient handling of MIL tasks involving a large number of
instances. Based on MMIL, we instantiated MMIL-Transformer, an efficient
Transformer model with windowed exact self-attention for large-scale MIL tasks.
To validate its effectiveness, we conducted a set of experiments on WSI
classification tasks, where MMIL-Transformer demonstrate superior performance
compared to existing state-of-the-art methods, i.e., 96.80% test AUC and 97.67%
test accuracy on the CAMELYON16 dataset, 99.04% test AUC and 94.37% test
accuracy on the TCGA-NSCLC dataset, respectively. All code and pre-trained
models are available at: https://github.com/hustvl/MMIL-Transformer

Computer Vision and Pattern Recognition;Machine Learning