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arXiv · 2006.13461

ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Medical Image Segmentation

Xinyue Huo·Lingxi Xie·Jianzhong He·Zijie Yang·Qi Tian
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Abstract

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.

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Xinyue Huo, Lingxi Xie, Jianzhong He, Zijie Yang, Qi Tian. 2020-06-24. ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Medical Image Segmentation. https://arxiv.org/abs/2006.13461

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