Linking paper page

README.md

@@ -1,48 +1,50 @@
----
-license: mit
----
-
-# Snuffy
-
-Snuffy is a state-of-the-art framework for whole-slide image (WSI) classification, introduced in the
-paper [Snuffy: Efficient Whole Slide Image Classifier](https://huggingface.co/papers/2408.08258) 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:
-
-1. **Self-Supervised Continual Pre-Training with PEFT**: Uses Parameter Efficient Fine Tuning (PEFT) with AdaptFormer
-   for effective training in the pathology domain.
-2. **Snuffy MIL-Pooling Architecture**: A novel pooling architecture designed for sparse transformers, tailored to the
-   complexity of cancer biology and tissue microenvironments.
-
-Snuffy addresses the challenge of balancing computational power and performance in WSI classification, offering two
-versions:
-
-- **Efficient Snuffy**: Pre-trained on natural images and fine-tuned with PEFT on WSIs.
-- **Exhaustive Snuffy**: Trained entirely from scratch on WSIs.
-
-Both versions use the Snuffy MIL-pooling architecture.
-
-## Usage
-
-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.
-
-Available models include:
-
-1. **Snuffy SimCLR from scratch** (Aggregator provided here)
-2. **Snuffy DINO from scratch** (All components provided here)
-3. **Snuffy DINO with Adapter** (All components provided here)
-4. **Snuffy MAE with Adapter** (All components provided here)
-
-## BibTeX entry and citation info
-
-```bibtex
-@misc{jafarinia2024snuffyefficientslideimage,
-      title={Snuffy: Efficient Whole Slide Image Classifier}, 
-      author={Hossein Jafarinia and Alireza Alipanah and Danial Hamdi and Saeed Razavi and Nahal Mirzaie and Mohammad Hossein Rohban},
-      year={2024},
-      eprint={2408.08258},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2408.08258}, 
-}
+---
+license: mit
+---
+
+# Snuffy
+
+Snuffy is a state-of-the-art framework for whole-slide image (WSI) classification, introduced in the
+paper [Snuffy: Efficient Whole Slide Image Classifier](https://huggingface.co/papers/2408.08258) 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:
+
+1. **Self-Supervised Continual Pre-Training with PEFT**: Uses Parameter Efficient Fine Tuning (PEFT) with AdaptFormer
+   for effective training in the pathology domain.
+2. **Snuffy MIL-Pooling Architecture**: A novel pooling architecture designed for sparse transformers, tailored to the
+   complexity of cancer biology and tissue microenvironments.
+
+Snuffy addresses the challenge of balancing computational power and performance in WSI classification, offering two
+versions:
+
+- **Efficient Snuffy**: Pre-trained on natural images and fine-tuned with PEFT on WSIs.
+- **Exhaustive Snuffy**: Trained entirely from scratch on WSIs.
+
+Both versions use the Snuffy MIL-pooling architecture.
+
+## Usage
+
+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.
+
+Available models include:
+
+1. **Snuffy SimCLR from scratch** (Aggregator provided here)
+2. **Snuffy DINO from scratch** (All components provided here)
+3. **Snuffy DINO with Adapter** (All components provided here)
+4. **Snuffy MAE with Adapter** (All components provided here)
+
+## BibTeX entry and citation info
+
+[Paper page](https://huggingface.co/papers/2408.08258)
+
+```bibtex
+@misc{jafarinia2024snuffyefficientslideimage,
+      title={Snuffy: Efficient Whole Slide Image Classifier}, 
+      author={Hossein Jafarinia and Alireza Alipanah and Danial Hamdi and Saeed Razavi and Nahal Mirzaie and Mohammad Hossein Rohban},
+      year={2024},
+      eprint={2408.08258},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2408.08258}, 
+}
 ```