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---
license: cc-by-nc-4.0
---
## An Ensemble-based Two-step Framework for Classification of Pap Smear Cell Images
### Code repository
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).
### 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},
}
``` |