用双网络协作提升超声宫颈分割精度，仅需少量标注数据。
Semi-supervised Cervical Segmentation on Ultrasound by A Dual Framework for Neural Networks
- 双神经网络互相生成伪标签并像素级互监督
- 引入对比学习增强未标注数据特征表示能力
- 适合标注数据稀缺的医学图像分割任务
超声图像中宫颈肌肉的精准分割对精准医疗至关重要，但标注数据稀缺制约了自动化方法的发展。本文提出一种新型半监督学习框架，采用双神经网络结构，通过相互生成伪标签并在像素级别交叉监督，提升分割性能。同时引入自监督对比学习策略，利用深层特征对增强未标注数据的表征学习能力。实验表明该框架在宫颈分割任务中表现优异，代码已公开于 https://github.com/13204942/SSL_Cervical_Segmentation。
原文摘要 · Abstract (English)
Accurate segmentation of ultrasound (US) images of the cervical muscles is crucial for precision healthcare. The demand for automatic computer-assisted methods is high. However, the scarcity of labeled data hinders the development of these methods. Advanced semi-supervised learning approaches have displayed promise in overcoming this challenge by utilizing labeled and unlabeled data. This study introduces a novel semi-supervised learning (SSL) framework that integrates dual neural networks. This SSL framework utilizes both networks to generate pseudo-labels and cross-supervise each other at the pixel level. Additionally, a self-supervised contrastive learning strategy is introduced, which employs a pair of deep representations to enhance feature learning capabilities, particularly on unlabeled data. Our framework demonstrates competitive performance in cervical segmentation tasks. Our codes are publicly available on https://github.com/13204942/SSL\_Cervical\_Segmentation.
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