Update README.md

README.md

@@ -8,10 +8,28 @@ license: cc-by-nc-4.0
 
 The project source code: [GitHub Repository](https://github.com/theodpzz/ps3c).
 
+### Available resources
+
+Train weights for **Step 1** and **Step 2**, as well as the **per-class final predicted probabilities**, are provided in this repository.
+
 ### PS3C
 
 This project was developed as part of the **PS3C Challenge** at **ISBI 2025**.  
 
 Kaggle Challenge: [Kaggle Link](https://www.kaggle.com/competitions/pap-smear-cell-classification-challenge).
 
-APACC Dataset original paper: [Paper access](https://www.nature.com/articles/s41597-024-03596-3).
+APACC Dataset original paper: [Paper access](https://www.nature.com/articles/s41597-024-03596-3).
+
+### Citation
+
+If you use this model or related resources, we would appreciate the following citation:
+
+```BibTeX
+@inproceedings{dipiazza2025ps3c,
+  author    = {Di Piazza Theo and Loic Boussel},
+  title     = {An Ensemble-based Two-step Framework for Classification of Pap Smear Cell Images},
+  booktitle = {Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI)},
+  year      = {2025},
+  organization = {IEEE},
+}
+```