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<p>Cervical precancer screening is essential for reducing disease-related mortality. In colposcopic practice, clinicians jointly assess dynamic acetic-acid image sequences, iodine-stained images, and structured clinical information when distinguishing High-grade squamous intraepithelial lesion or higher (HSIL+) from low-grade squamous intraepithelial lesion or lower (LSIL−). However, most existing artificial intelligence models fail to integrate all three modalities effectively. To bridge this gap, we propose MMTC-Net, a lightweight multimodal temporal network for HSIL+ recognition in cervical precancer screening. MMTC-Net includes a temporal normalization linear module, which models frame-to-frame progression in acetic-acid sequences using a reference-normalized linear mapping, thereby preserving temporal cues with low computational overhead. MMTC-Net also uses a two-stage cross-modal attention mechanism that first aligns the acetic-acid and iodine-stained image modalities and then fuses the resulting image representation with structured clinical data. Evaluated by fivefold cross-validation on a real-world cohort of 1347 patients, MMTC-Net achieved a mean accuracy of 94.24%, sensitivity of 90.60%, specificity of 97.87%, and area under the receiver operating characteristic curve of 0.9765, significantly outperforming comparator models (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(p&lt;0.05\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>p</mi> <mo>&lt;</mo> <mn>0.05</mn> </mrow> </math></EquationSource> </InlineEquation>). Ablation studies further confirmed the contributions of temporal modeling of acetic-acid image sequences and multimodal fusion of cervicograms and clinical data. By explicitly modeling the information sources used in routine colposcopic assessment, these results suggest that MMTC-Net can support reproducible AI-assisted decision-making, particularly in resource-constrained screening settings where experienced colposcopists are limited.</p>

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MMTC-Net: Multimodal Temporal Cervical Network for HSIL+ Recognition in Precancer Screening

  • 期刊论文

  • 发表日期：

    2026年5月27日

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  • Ling Yan,
  • Qingyu Wang,
  • Yi Guo,
  • Peng Ren,
  • Li Ding,
  • Zhang Wang,
  • Jingjing Yang,
  • Xingfa Shen

摘要

Cervical precancer screening is essential for reducing disease-related mortality. In colposcopic practice, clinicians jointly assess dynamic acetic-acid image sequences, iodine-stained images, and structured clinical information when distinguishing High-grade squamous intraepithelial lesion or higher (HSIL+) from low-grade squamous intraepithelial lesion or lower (LSIL−). However, most existing artificial intelligence models fail to integrate all three modalities effectively. To bridge this gap, we propose MMTC-Net, a lightweight multimodal temporal network for HSIL+ recognition in cervical precancer screening. MMTC-Net includes a temporal normalization linear module, which models frame-to-frame progression in acetic-acid sequences using a reference-normalized linear mapping, thereby preserving temporal cues with low computational overhead. MMTC-Net also uses a two-stage cross-modal attention mechanism that first aligns the acetic-acid and iodine-stained image modalities and then fuses the resulting image representation with structured clinical data. Evaluated by fivefold cross-validation on a real-world cohort of 1347 patients, MMTC-Net achieved a mean accuracy of 94.24%, sensitivity of 90.60%, specificity of 97.87%, and area under the receiver operating characteristic curve of 0.9765, significantly outperforming comparator models ( \(p<0.05\) p < 0.05 ). Ablation studies further confirmed the contributions of temporal modeling of acetic-acid image sequences and multimodal fusion of cervicograms and clinical data. By explicitly modeling the information sources used in routine colposcopic assessment, these results suggest that MMTC-Net can support reproducible AI-assisted decision-making, particularly in resource-constrained screening settings where experienced colposcopists are limited.