用高斯混合模型捕捉宫颈超声图像差异，提升早产预测准确率
From Point Estimates to Distributions: GMM Pooling for MIL in Preterm Birth Prediction
- 将患者多张超声图视为一个数据包，用高斯混合模型建模特征分布
- 早产预测的PR-AUC从0.44提升至0.56，淋巴结转移任务达到0.91 F1
- 适合医学影像中存在多幅图像但标签单一的任务，如疾病风险预测
早产预测可实现精准监测与及时干预，但现有基于超声的模型通常仅选取每位患者的一张经阴道超声（TVUS）图像，尽管临床检查会获取多张宫颈图像。本文将早产预测建模为多实例学习（MIL）问题，将每位患者表示为一张变量大小的TVUS图像包，且具有单一标签。为突破传统MIL聚合方法将图像包压缩为单一数值的局限，提出高斯混合模型（GMM）池化，通过建模图像特征分布，将包内所有图像汇总为固定长度的表示，从而捕捉患者内部图像间的变异性。在私有临床队列和公开淋巴结转移基准上进行评估。在早产预测任务中，GMM池化使PR-AUC从0.44提升至0.56；在淋巴结转移基准上，分类任务取得0.91 F1-score和0.89 ROC-AUC，回归任务达到0.18 MAE。代码已公开于https://github.com/HussainAlasmawi/GMM_Pooling。
原文摘要 · Abstract (English)
Preterm birth (PTB) prediction can enable targeted surveillance and timely intervention, yet most ultrasound-based models use a single selected transvaginal ultrasound (TVUS) frame per patient despite routine exams acquiring multiple cervical images. We formulate PTB prediction as a multiple instance learning (MIL) problem, representing each patient as a variable-sized bag of TVUS images with a single outcome label. To move beyond standard MIL aggregators that collapse a bag into a point estimate, we propose a Gaussian Mixture Model (GMM) pooling, which summarizes all images in a bag into a fixed-length representation by modeling their feature distribution. This design captures intra-patient variability. We evaluate the method on a private clinical cohort and on a public lymph node metastasis benchmark. For PTB prediction, GMM pooling improves over the instance-based model PR-AUC from 0.44 to 0.56. On the lymph node benchmark, it achieves state-of-the-art performance with 0.91 F1-score and 0.89 ROC-AUC for classification and 0.18 MAE for regression. The code is publicly available at https://github.com/HussainAlasmawi/GMM_Pooling.
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