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Updated: Jul 16, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Systematic Evaluation of Vision Transformers for Automated Cervical Cancer Classification: Optimization, Statistical

Nisreen Albzour1, Sarah S Lam1

  • 1School of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA.

Cancers
|July 15, 2026
PubMed
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Vision Transformers (ViT) show promise for automated cervical cancer screening, achieving ~95% accuracy. This AI approach offers interpretable insights by focusing on clinically relevant cell features, improving upon traditional methods.

Area of Science:

  • Medical AI
  • Computer Vision
  • Oncology

Background:

  • Manual Pap smear analysis for cervical cancer screening faces challenges including inter-observer variability and limited expert availability.
  • Existing automated methods using convolutional neural networks (CNNs) struggle with long-range spatial dependencies and clinical interpretability.

Purpose of the Study:

  • To systematically optimize Vision Transformer (ViT) architectures for enhanced automated cervical cancer screening.
  • To improve the interpretability of AI models in cervical cancer diagnosis.

Main Methods:

  • Utilized the Herlev dataset (917 images) comprising normal and abnormal cervical cells.
  • Optimized a lightweight ViT architecture (ViT-Tiny) by evaluating augmentation strategies, class weighting, and hyperparameters.
Keywords:
Grad-CAMcervical cancerinterpretabilitymedical image classificationpap smearvision transformer

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  • Employed Gradient-weighted Class Activation Mapping (Grad-CAM) for model interpretability analysis.

    • Main Results:

    • Achieved a cross-validation accuracy of approximately 95% with optimal configuration.
    • Identified random horizontal flipping and specific class weighting (0.7 × 1.3) as most effective.
    • Grad-CAM analysis demonstrated model attention aligned with clinically relevant cytopathological features like nuclei and chromatin texture.

    Conclusions:

    • Vision Transformers offer accurate and interpretable decision support for cervical cancer screening.
    • The attention-based transparency of ViTs is relevant for medical AI applications.
    • Further validation on larger, multi-center datasets is required prior to clinical deployment.