  In recent years, segmentation methods based on deep convolutional neural
networks (CNNs) have made state-of-the-art achievements for many medical
analysis tasks. However, most of these approaches improve performance by
optimizing the structure or adding new functional modules of the U-Net, which
ignoring the complementation and fusion of the coarse-grained and fine-grained
semantic information. To solve the above problems, we propose a medical image
segmentation framework called progressive learning network (PL-Net), which
includes internal progressive learning (IPL) and external progressive learning
(EPL). PL-Net has the following advantages: (1) IPL divides feature extraction
into two "steps", which can mix different size receptive fields and capture
semantic information from coarse to fine granularity without introducing
additional parameters; (2) EPL divides the training process into two "stages"
to optimize parameters, and realizes the fusion of coarse-grained information
in the previous stage and fine-grained information in the latter stage. We
evaluate our method in different medical image analysis tasks, and the results
show that the segmentation performance of PL-Net is better than the
state-of-the-art methods of U-Net and its variants.
