Snuffy is a state-of-the-art framework for whole-slide image (WSI) classification, introduced in the
paper
Snuffy: Efficient Whole Slide Image Classifier by Hossein Jafarinia et al. from Sharif University of
Technology. Tested on the TCGA Lung Cancer and CAMELYON16 datasets, it consists of two main components:
Snuffy addresses the challenge of balancing computational power and performance in WSI classification, offering two
versions:
Both versions use the Snuffy MIL-pooling architecture.
The code and documentation for Snuffy is available at:
https://github.com/jafarinia/snuffy
This repository includes weights for the embedder, embeddings, and aggregator models as described in the paper.
1@misc{jafarinia2024snuffyefficientslideimage,
2 title={Snuffy: Efficient Whole Slide Image Classifier},
3 author={Hossein Jafarinia and Alireza Alipanah and Danial Hamdi and Saeed Razavi and Nahal Mirzaie and Mohammad Hossein Rohban},
4 year={2024},
5 eprint={2408.08258},
6 archivePrefix={arXiv},
7 primaryClass={cs.CV},
8 url={https://arxiv.org/abs/2408.08258},
9}
