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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.21340 |
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| _version_ | 1866909597657202688 |
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| author | Nguyen, Khoa Tuan Park, Ho-min Oh, Gaeun Vankerschaver, Joris De Neve, Wesley |
| author_facet | Nguyen, Khoa Tuan Park, Ho-min Oh, Gaeun Vankerschaver, Joris De Neve, Wesley |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_21340 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Improved Cervical Cancer Screening: Vision Transformer-Based Classification and Interpretability Nguyen, Khoa Tuan Park, Ho-min Oh, Gaeun Vankerschaver, Joris De Neve, Wesley Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Towards Improved Cervical Cancer Screening: Vision Transformer-Based Classification and Interpretability |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2504.21340 |