Semi-supervised Medical Image Segmentation based on Coarse-Fine Dual Training Streams

Commonly used semi-supervised medical segmentation networks usually use consistent learning under different data perturbations to regularise training, ignoring the multiscale information of the data itself. Therefore, this paper proposes a new network based on coarse and fine dual training streams(CF-UNet), which consists of a backbone network and auxiliary learning branches(ALB). Our approach has the following two novel designs: 1) We design a simple and effective coarse- fine dual training streams. Specifically, in the coarse training stream, we improve the robustness and generalisation of the model by establishing regularisation between different strong and weak perturbation views. In the fine training stream, we introduce an auxiliary learning branch to improve the prediction performance of the backbone network.2) In the ALB module, we design the channel spatial fusion attention module (CSMA) and multiscale large kernel convolutional attention (MS-LKA) to perform feature extraction and fusion from a variety of scales. We evaluate our proposed method on ACDC and DRIVE datasets and numerous experiments have shown that our CF-UNet outperforms state-of-the-art networks. Code is available at https://github.com/slz-bit/CF-UNet.