Snuffy: Efficient Whole Slide Image Classifier
This addresses efficiency and performance issues in digital pathology for medical diagnosis, though it is incremental as it builds on existing MIL and transformer methods.
The paper tackles the computational challenges of Whole Slide Image classification by introducing Snuffy, a novel MIL-pooling method based on sparse transformers, which achieves superior accuracies on CAMELYON16 and TCGA Lung cancer datasets.
Whole Slide Image (WSI) classification with multiple instance learning (MIL) in digital pathology faces significant computational challenges. Current methods mostly rely on extensive self-supervised learning (SSL) for satisfactory performance, requiring long training periods and considerable computational resources. At the same time, no pre-training affects performance due to domain shifts from natural images to WSIs. We introduce Snuffy architecture, a novel MIL-pooling method based on sparse transformers that mitigates performance loss with limited pre-training and enables continual few-shot pre-training as a competitive option. Our sparsity pattern is tailored for pathology and is theoretically proven to be a universal approximator with the tightest probabilistic sharp bound on the number of layers for sparse transformers, to date. We demonstrate Snuffy's effectiveness on CAMELYON16 and TCGA Lung cancer datasets, achieving superior WSI and patch-level accuracies. The code is available on https://github.com/jafarinia/snuffy.