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---
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license: mit
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---
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# Snuffy
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Snuffy is a state-of-the-art framework for whole-slide image (WSI) classification, introduced in the
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paper [Snuffy: Efficient Whole Slide Image Classifier](https://huggingface.co/papers/2408.08258) by Hossein Jafarinia et al. from Sharif University of
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Technology. Tested on the TCGA Lung Cancer and CAMELYON16 datasets, it consists of two main components:
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1. **Self-Supervised Continual Pre-Training with PEFT**: Uses Parameter Efficient Fine Tuning (PEFT) with AdaptFormer
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for effective training in the pathology domain.
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2. **Snuffy MIL-Pooling Architecture**: A novel pooling architecture designed for sparse transformers, tailored to the
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complexity of cancer biology and tissue microenvironments.
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Snuffy addresses the challenge of balancing computational power and performance in WSI classification, offering two
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versions:
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- **Efficient Snuffy**: Pre-trained on natural images and fine-tuned with PEFT on WSIs.
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- **Exhaustive Snuffy**: Trained entirely from scratch on WSIs.
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Both versions use the Snuffy MIL-pooling architecture.
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## Usage
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The code and documentation for Snuffy is available at: https://github.com/jafarinia/snuffy
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This repository includes weights for the embedder, embeddings, and aggregator models as described in the paper.
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Available models include:
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1. **Snuffy SimCLR from scratch** (Aggregator provided here)
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2. **Snuffy DINO from scratch** (All components provided here)
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3. **Snuffy DINO with Adapter** (All components provided here)
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4. **Snuffy MAE with Adapter** (All components provided here)
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## BibTeX entry and citation info
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```bibtex
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@misc{jafarinia2024snuffyefficientslideimage,
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title={Snuffy: Efficient Whole Slide Image Classifier},
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author={Hossein Jafarinia and Alireza Alipanah and Danial Hamdi and Saeed Razavi and Nahal Mirzaie and Mohammad Hossein Rohban},
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year={2024},
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eprint={2408.08258},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2408.08258},
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}
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``` |
