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arXiv · 2504.21340

Towards Improved Cervical Cancer Screening: Vision Transformer-Based Classification and Interpretability

Khoa Tuan Nguyen·Ho-min Park·Gaeun Oh·Joris Vankerschaver·Wesley De Neve
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Abstract

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.

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Khoa Tuan Nguyen, Ho-min Park, Gaeun Oh, Joris Vankerschaver, Wesley De Neve. 2025-04-30. Towards Improved Cervical Cancer Screening: Vision Transformer-Based Classification and Interpretability. https://arxiv.org/abs/2504.21340

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