Multi-Task Curriculum Learning For Semi-Supervised Medical Image Segmentation

The lack of annotated data is a common problem in medical image segmentation tasks. In this paper, we present a novel multi-task semi-supervised segmentation algorithm with a curriculum-style learning strategy. The proposed method includes a segmentation task and an auxiliary regression task. Concretely, the auxiliary regression task aims to learn image-level properties such as the size and centroid position of target region to regularize the segmentation network, enforcing the pixel-level segmentation result match the distributions of these regressions. In addition, these regressions are treated as pseudo labels for the learning of unlabeled data. For the purpose of decreasing noise from the deviation of inferred labels, we adopt the inequality constraint for the learning of unlabeled data, which would generate a tolerance interval where the prediction within it would not be published to reduce the impact of prediction deviation of regression network. Experimental results on both 2017 ACDC dataset and PROMISE12 dataset demonstrate the effectiveness of our method.