论文标题
ATSO：半监督医学图像分割的异步教师学生优化
ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Medical Image Segmentation
论文作者
论文摘要
在医学图像分析中，半监督学习是一种从少量标记的数据和大量未标记数据中提取知识的有效方法。本文着重于一种称为自我学习的流行管道，并指出了一个名为Lazy Learne的弱点，它指的是模型很难从自己生成的伪标签中学习的困难。为了减轻这个问题，我们提出了ATSO，这是教师学生优化的异步版本。 ATSO分区将未标记的数据分为两个子集，然后或使用一个子集对模型进行微调并在另一个子集上更新标签。我们在两个流行的医学图像分割数据集上评估ATSO，并在各种半监视设置中显示出其出色的性能。经过轻微的修改，ATSO可以很好地转移到自动驾驶数据的自然图像分割。
In medical image analysis, semi-supervised learning is an effective method to extract knowledge from a small amount of labeled data and a large amount of unlabeled data. This paper focuses on a popular pipeline known as self learning, and points out a weakness named lazy learning that refers to the difficulty for a model to learn from the pseudo labels generated by itself. To alleviate this issue, we propose ATSO, an asynchronous version of teacher-student optimization. ATSO partitions the unlabeled data into two subsets and alternately uses one subset to fine-tune the model and updates the label on the other subset. We evaluate ATSO on two popular medical image segmentation datasets and show its superior performance in various semi-supervised settings. With slight modification, ATSO transfers well to natural image segmentation for autonomous driving data.
