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