Semantic segmentation is an essential task in developing medical image diagnosis systems. However, building an annotated medical dataset is expensive. Thus, semi-supervised methods are significant in this circumstance. In semi-supervised learning, the quality of labels plays a crucial role in model performance. In this work, we present a new pseudo labeling strategy that enhances the quality of pseudo labels used for training student networks. We follow the multi-stage semi-supervised training approach, which trains a teacher model on a labeled dataset and then uses the trained teacher to render pseudo labels for student training. By doing so, the pseudo labels will be updated and more precise as training progress. The key difference between previous and our methods is that we update the teacher model during the student training process. So the quality of pseudo labels is improved during the student training process. We also propose a simple but effective strategy to enhance the quality of pseudo labels using a momentum model -- a slow copy version of the original model during training. By applying the momentum model combined with re-rendering pseudo labels during student training, we achieved an average of 84.1% Dice Score on five datasets (i.e., Kvarsir, CVC-ClinicDB, ETIS-LaribPolypDB, CVC-ColonDB, and CVC-300) with only 20% of the dataset used as labeled data. Our results surpass common practice by 3% and even approach fully-supervised results on some datasets. Our source code and pre-trained models are available at https://github.com/sun-asterisk-research/online learning ssl
翻译:语义分解是开发医学图像诊断系统的一项基本任务。 但是, 建立一个附加说明的医疗数据集是昂贵的。 因此, 在此情况下, 半监督的方法很重要。 在半监督的学习中, 标签的质量在模型性能中起着关键的作用。 在这项工作中, 我们提出了一个新的假标签战略, 提高用于培训学生网络的假标签的质量。 我们遵循了多阶段半监督的培训方法, 在标签式的数据集上培训教师模型, 然后使用受过训练的教师来为学生培训提供假标签。 这样, 假标签将随着培训进展而更新, 更加精确。 在半监督的学习中, 标签的质量在模型模型模型的模型中扮演着关键角色。 在学生培训过程中, 我们提出了一个提高假标签质量的假标签质量的简单而有效的战略, 使用动力模型 -- -- 仅是原始模型的缓慢版本。 通过在学生培训期间应用我们的势头源代码, 与重塑假标签的模型相结合。 在常规的 C- C- Vlic- hold 数据中, 我们实现了84.1 的C- dal- dolds。