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In medical image analysis, semi-supervised learning is an effective method toextract knowledge from a small amount of labeled data and a large amount ofunlabeled data. This paper focuses on a popular pipeline known as selflearning, and points out a weakness named lazy learning that refers to thedifficulty for a model to learn from the pseudo labels generated by itself. Toalleviate this issue, we propose ATSO, an asynchronous version ofteacher-student optimization. ATSO partitions the unlabeled data into twosubsets and alternately uses one subset to fine-tune the model and updates thelabel on the other subset. We evaluate ATSO on two popular medical imagesegmentation datasets and show its superior performance in varioussemi-supervised settings. With slight modification, ATSO transfers well tonatural image segmentation for autonomous driving data.

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