Department of IT Convergence Engineering, Gachon University, Seongnam, 13120, South Korea.
School of Computing, Gachon University, Seongnam, 13120, South Korea.
Sci Rep. 2022 Aug 26;12(1):14626. doi: 10.1038/s41598-022-17843-3.
Polyp segmentation has accomplished massive triumph over the years in the field of supervised learning. However, obtaining a vast number of labeled datasets is commonly challenging in the medical domain. To solve this problem, we employ semi-supervised methods and suitably take advantage of unlabeled data to improve the performance of polyp image segmentation. First, we propose an encoder-decoder-based method well suited for the polyp with varying shape, size, and scales. Second, we utilize the teacher-student concept of training the model, where the teacher model is the student model's exponential average. Third, to leverage the unlabeled dataset, we enforce a consistency technique and force the teacher model to generate a similar output on the different perturbed versions of the given input. Finally, we propose a method that upgrades the traditional pseudo-label method by learning the model with continuous update of pseudo-label. We show the efficacy of our proposed method on different polyp datasets, and hence attaining better results in semi-supervised settings. Extensive experiments demonstrate that our proposed method can propagate the unlabeled dataset's essential information to improve performance.
多年来，在监督学习领域，息肉分割已经取得了巨大的成功。然而，在医学领域，获得大量标记数据集通常具有挑战性。为了解决这个问题，我们采用半监督方法，并适当地利用未标记数据来提高息肉图像分割的性能。首先，我们提出了一种基于编码器-解码器的方法，非常适合形状、大小和比例不同的息肉。其次，我们利用教师-学生的概念来训练模型，其中教师模型是学生模型的指数平均。第三，为了利用未标记数据集，我们强制实施一致性技术，迫使教师模型在给定输入的不同扰动版本上生成相似的输出。最后，我们提出了一种通过使用连续更新伪标签来学习模型的方法，改进了传统的伪标签方法。我们在不同的息肉数据集上展示了我们提出的方法的有效性，从而在半监督设置中获得了更好的结果。广泛的实验表明，我们提出的方法可以传播未标记数据集的重要信息，以提高性能。
