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

Source: [Towards Improved Cervical Cancer Screening: Vision Transformer-Based Classification and Interpretability](<https://www.sota2.com/research/paper/68fa785d1e209131cea77ffb>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:57:16.023Z

Authors: Khoa Tuan Nguyen, Ho-min Park, Gaeun Oh, Joris Vankerschaver, Wesley De Neve

Published: 2025-04-30T05:59:56.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2504.21340>)

[PDF](<https://arxiv.org/pdf/2504.21340.pdf>)