用不确定性与多样性指导采样，提升超声胎儿头分割的少量标注效果
Entropy-Guided Agreement-Diversity: A Semi-Supervised Active Learning Framework for Fetal Head Segmentation in Ultrasound
- 基于预测熵和一致性多样性双重筛选，主动选择最有价值样本
- 仅用5%~10%标注数据即达94.57%~96.32%的分割准确率
- 适合标注稀缺的医学图像分析，尤其适用于孕期不同阶段数据
胎儿超声（US）数据受限于隐私与监管，难以训练深度学习模型。现有半监督学习方法多依赖随机采样，易因标签数据同质导致过拟合。为此，本文提出两阶段主动学习采样器EGAD：先用预测熵筛选高不确定性样本，再结合余弦相似度与互信息计算的一致性-多样性得分进行优化。同时，采用特征降采样的一致性学习策略提升性能。在两个公开数据集上，使用5%和10%标注数据训练时，该方法平均Dice得分分别达到94.57%和96.32%，优于现有半监督模型，并在不同孕周数据中保持稳定表现。代码已开源。
原文摘要 · Abstract (English)
Fetal ultrasound (US) data is often limited due to privacy and regulatory restrictions, posing challenges for training deep learning (DL) models. While semi-supervised learning (SSL) is commonly used for fetal US image analysis, existing SSL methods typically rely on random limited selection, which can lead to suboptimal model performance by overfitting to homogeneous labeled data. To address this, we propose a two-stage Active Learning (AL) sampler, Entropy-Guided Agreement-Diversity (EGAD), for fetal head segmentation. Our method first selects the most uncertain samples using predictive entropy, and then refines the final selection using the agreement-diversity score combining cosine similarity and mutual information. Additionally, our SSL framework employs a consistency learning strategy with feature downsampling to further enhance segmentation performance. In experiments, SSL-EGAD achieves an average Dice score of 94.57\% and 96.32\% on two public datasets for fetal head segmentation, using 5\% and 10\% labeled data for training, respectively. Our method outperforms current SSL models and showcases consistent robustness across diverse pregnancy stage data. The code is available on \href{https://github.com/13204942/Semi-supervised-EGAD}{GitHub}.
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