# From Point Estimates to Distributions: GMM Pooling for MIL in Preterm Birth Prediction

arXiv: https://arxiv.org/abs/2606.23005 · HTML: https://mlsanity.com/paper/2606.23005/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 22 Jun 2026.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [FetalCLIP: A Visual-Language Foundation Model for Fetal Ultrasound Image Analysis](https://mlsanity.com/paper/2502.14807/) (2025)
- [Estimation of preterm birth markers with U-Net segmentation network](https://mlsanity.com/paper/1908.09148/) (2019)
- [Spontaneous preterm birth prediction using convolutional neural networks](https://mlsanity.com/paper/2008.07000/) (2020)
- [Enhancing Fetal Plane Classification Accuracy with Data Augmentation Using Diffusion Models](https://mlsanity.com/paper/2501.15248/) (2025)
- [FUSC: Fetal Ultrasound Semantic Clustering of Second Trimester Scans Using Deep Self-supervised Learning](https://mlsanity.com/paper/2310.12600/) (2023)
- [TPA: Temporal Prompt Alignment for Fetal Congenital Heart Defect Classification](https://mlsanity.com/paper/2508.15298/) (2025)

## Abstract (arXiv)

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.