We propose a novel approach to cervical cell image classification for cervical cancer screening using the EVA-02 transformer model. We developed a four-step pipeline: fine-tuning EVA-02, feature extraction, selecting important features through multiple machine learning models, and training a new artificial neural network with optional loss weighting for improved generalization. With this design, our best model achieved an F1-score of 0.85227, outperforming the baseline EVA-02 model (0.84878). We also utilized Kernel SHAP analysis and identified key features correlating with cell morphology and staining characteristics, providing interpretable insights into the decision-making process of the fine-tuned model. Our code is available at https://github.com/Khoa-NT/isbi2025_ps3c.
@article{arxiv.2504.21340,
title = {Towards Improved Cervical Cancer Screening: Vision Transformer-Based Classification and Interpretability},
author = {Khoa Tuan Nguyen and Ho-min Park and Gaeun Oh and Joris Vankerschaver and Wesley De Neve},
journal= {arXiv preprint arXiv:2504.21340},
year = {2025}
}
Comments
Accepted at ISBI 2025 "Challenge 2: Pap Smear Cell Classification Challenge"