A semi-supervised segmentation network fusing pseudo-label with multi-level feature consistency correction for hard exudates

Timely detection of hard exudates in fundus images can effectively avoid the severity of the disease, but the labelling of small and numerous lesion areas requires a lot of labour costs. This paper proposes a semi-supervised segmentation network, which integrates pseudo-labels and multi-level features consistency correction. It achieves accurate segmentation of hard exudates by making full use of a small amount of labelled data and a large amount of unlabelled data. The network effectively extracts features from the unlabelled data through knowledge transfer of the teacher-student model, and incorporates a Transformer network for auxiliary training to promote the quality of transfer. In addition, three unsupervised losses are introduced to improve the performance: the perturbation loss improves the robustness of the model to noise by adding different noises to the same input; the multi-level feature consistency correction loss ensures the consistency of features of the student model at different scales; and the pseudo-labelling cross-supervision loss utilizes the generated pseudo-labels for supervision between CNN and Transformer. By comparing the segmentation results with different proportion of the labelled data, it has better segmentation performance compared to other methods. The proposed methods can totally increase dice by 16.56% and mean intersection over union (MIoU) by 25.11%. This paper proposes a semi-supervised segmentation network, PMCC-Net, which integrates pseudo-labels and multi-level features consistency correction. Three unsupervised losses: perturbation loss, multi-level feature consistency correction loss and pseudo-labelling cross-supervision loss are introduced to improve the training performance. image