Towards Improved Cervical Cancer Screening: Vision Transformer-Based Classification and Interpretability
Journal:
arXiv
Published Date:
Apr 30, 2025
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.
