EMIFS: Efficient Multi-scale Information Fusion Self-supervision for Medical Image Segmentation

Medical image segmentation plays an important role in clinical decision making and auxiliary diagnosis. Today, however, it still faces three major challenges. 1. In the task of medical image segmentation, due to the different types of lesions and the large difference in the size of the lesion area, the segmentation accuracy is seriously reduced. 2. In order to pursue the segmentation performance, the model is difficult to be applied to the actual medical environment due to the excessive parameters. 3. Relying too much on manually labeled images to assist training. In order to meet these challenges, we propose a lightweight segmentation network, which is dedicated to extracting local and global information and fusing multi-level and multi-source features to maximize the segmentation accuracy for different shape lesions, especially for the case of fuzzy boundary and small segmentation target. The method of generating intermediate mask self-monitoring is used to generate additional labeled images to assist training. Finally, by using efficient down sampling and up sampling operations, the parameter quantity is only 1.37M while effectively extracting information. On the BUSI and ISIC2018 datasets, mIoU and DSC scores reached 75.57%, 83.57% and 83.85%, 90.38% respectively, indicating that we have reached the best balance between parameters and performance. The code is available at https://github.com/Jay217219/EMIFS.