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<p>Accurate grading of cervical intraepithelial neoplasia (CIN1–3) from colposcopic images is clinically critical yet challenging due to subtle inter-grade morphology and substantial imaging variability. We propose an attention-guided mixture-of-experts (MoE) framework that ensembles five pretrained DenseNet-121 experts and employs an attention mechanism over pooled intermediate features to drive a gating network that adaptively weights expert outputs for each image. Operating on feature representations rather than raw pixels allows the gating network to perform input-specific expert selection and improves robustness to ambiguous cases. Using the Intel &amp; MobileODT cervical screening dataset with a strict patient-wise 70/10/20 split, we report mean performance over five runs with 95% confidence intervals. On the independent test set, the proposed MoE achieves 74.0% ± 1.6 accuracy and 72.1% ± 1.8 F1, with per-class AUCs of 0.88 (CIN1), 0.82 (CIN2), and 0.85 (CIN3). The method yields statistically significant improvements over single-network DenseNet-121 baselines and alternative MoE backbones (MobileNet, EfficientNet, ShuffleNet) (<i>p</i> &lt; 0.01). Ablation studies show that attention-guided gating contributes approximately 5–8% absolute accuracy gain over uniform weighting, and that five experts provide the optimal accuracy–efficiency balance. We further present attention visualizations and limited external validation to assess interpretability and generalizability. Although performance remains below that of recent transformer-ensemble models evaluated on smaller or less diverse test sets, the modular and interpretable MoE architecture offers a practical foundation for integrating segmentation or transformer-based experts to advance clinical utility. Code and trained models will be released to support reproducibility.</p>

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Cervical Intraepithelial Neoplasia (CIN1-3) Disease Grading Using a Mixture of Experts Approach

  • 期刊论文

  • 发表日期：

    2026年2月26日

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  • Mohammad Khaleel Sallam Ma’aitah,
  • Abdulkader Helwan,
  • Safa Ghannam,
  • Abdelrahman Radwan,
  • Khaled Almezhghwi

摘要

Accurate grading of cervical intraepithelial neoplasia (CIN1–3) from colposcopic images is clinically critical yet challenging due to subtle inter-grade morphology and substantial imaging variability. We propose an attention-guided mixture-of-experts (MoE) framework that ensembles five pretrained DenseNet-121 experts and employs an attention mechanism over pooled intermediate features to drive a gating network that adaptively weights expert outputs for each image. Operating on feature representations rather than raw pixels allows the gating network to perform input-specific expert selection and improves robustness to ambiguous cases. Using the Intel & MobileODT cervical screening dataset with a strict patient-wise 70/10/20 split, we report mean performance over five runs with 95% confidence intervals. On the independent test set, the proposed MoE achieves 74.0% ± 1.6 accuracy and 72.1% ± 1.8 F1, with per-class AUCs of 0.88 (CIN1), 0.82 (CIN2), and 0.85 (CIN3). The method yields statistically significant improvements over single-network DenseNet-121 baselines and alternative MoE backbones (MobileNet, EfficientNet, ShuffleNet) (p < 0.01). Ablation studies show that attention-guided gating contributes approximately 5–8% absolute accuracy gain over uniform weighting, and that five experts provide the optimal accuracy–efficiency balance. We further present attention visualizations and limited external validation to assess interpretability and generalizability. Although performance remains below that of recent transformer-ensemble models evaluated on smaller or less diverse test sets, the modular and interpretable MoE architecture offers a practical foundation for integrating segmentation or transformer-based experts to advance clinical utility. Code and trained models will be released to support reproducibility.