Add pipeline tag and link to paper

README.md

@@ -1,25 +1,27 @@
 ---
+license: mit
+pipeline_tag: image-feature-extraction
 tags:
-- image-feature-extraction
 - cell representation
 - histology
 - medical imaging
 - self-supervised learning
 - vision transformer
 - foundation model
-license: mit
 ---
 
 # Model card for LEMON
 
-`LEMON` is an open-source foundation model for single-cell histology images. The model is a Vision Transformer (ViT-s/8) trained using self-supervised learning on a dataset of 10 million histology cell images sampled from 10,000 slides from TCGA. 
+`LEMON` (Learning Embeddings from Morphology Of Nuclei) is an open-source foundation model for single-cell histology images, presented in the paper [LEMON: a foundation model for nuclear morphology in Computational Pathology](https://arxiv.org/abs/2603.25802). 
+
+The model is a Vision Transformer (ViT-s/8) trained using self-supervised learning on a dataset of 10 million histology cell images sampled from 10,000 slides from TCGA. 
 
 `LEMON` can be used to extract robust features from single-cell histology images for various downstream applications, such as gene expression prediction or cell type classification.
 
 
 ## How to use it to extract features
 
-The code below can be used to run inference. `LEMON` expects images of size 40x40 that were extracted at 0.25 microns per pixel (40X).
+The code below can be used to run inference. `LEMON` expects images of size 40x40 that were extracted at 0.25 microns per pixel (40X). Note that the code requires the `model.py` script provided in the repository.
 
 ```python
 import torch
@@ -53,10 +55,9 @@ assert features.shape == (1, 384)
 
 ## Citation
 
-If you found our work useful in your research, please consider citing our work at:
+If you found our work useful in your research, please consider citing our work:
 
-
-```
+```bibtex
 @misc{chadoutaud2026lemonfoundationmodelnuclear,
       title={LEMON: a foundation model for nuclear morphology in Computational Pathology}, 
       author={Loïc Chadoutaud and Alice Blondel and Hana Feki and Jacqueline Fontugne and Emmanuel Barillot and Thomas Walter},
@@ -66,4 +67,4 @@ If you found our work useful in your research, please consider citing our work a
       primaryClass={cs.CV},
       url={https://arxiv.org/abs/2603.25802}, 
 }
-```
+```