Semi-supervised Cervical Segmentation on Ultrasound by A Dual Framework for Neural Networks

• URL: http://arxiv.org/abs/2503.17057v1
• Date: Fri, 21 Mar 2025 11:16:44 GMT
• Title: Semi-supervised Cervical Segmentation on Ultrasound by A Dual Framework for Neural Networks
• Authors: Fangyijie Wang, Kathleen M. Curran, Guénolé Silvestre,
• Abstract summary: This study introduces a novel semi-supervised learning (SSL) framework that integrates dual neural networks.<n>A self-supervised contrastive learning strategy is introduced, which employs a pair of deep representations to enhance feature learning capabilities.<n>Our framework demonstrates competitive performance in cervical segmentation tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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