Early detection of dysplasia of the cervix is critical for cervical cancer
treatment. However, automatic cervical dysplasia diagnosis via visual
inspection, which is more appropriate in low-resource settings, remains a
challenging problem. Though promising results have been obtained by recent deep
learning models, their performance is significantly hindered by the limited
scale of the available cervix datasets. Distinct from previous methods that
learn from a single dataset, we propose to leverage cross-domain cervical
images that were collected in different but related clinical studies to improve
the model's performance on the targeted cervix dataset. To robustly learn the
transferable information across datasets, we propose a novel prototype-based
knowledge filtering method to estimate the transferability of cross-domain
samples. We further optimize the shared feature space by aligning the
cross-domain image representations simultaneously on domain level with early
alignment and class level with supervised contrastive learning, which endows
model training and knowledge transfer with stronger robustness. The empirical
results on three real-world benchmark cervical image datasets show that our
proposed method outperforms the state-of-the-art cervical dysplasia visual
inspection by an absolute improvement of 4.7% in top-1 accuracy, 7.0% in
precision, 1.4% in recall, 4.6% in F1 score, and 0.05 in ROC-AUC.

通过利用不同但相关临床研究中收集的跨领域子宫颈图像，我们提出了一种基于原型的知识过滤方法，用于改善定向子宫颈数据集上模型的性能，实验证据显示我们的方法在顶级准确率、精确度、召回率、F1 分数和 ROC-AUC 上分别比最先进的子宫颈畸形视觉检查法提高了 4.7%、7.0%、1.4%、4.6% 和 0.05。