PLU-Net: Extraction of multi-scale feature fusion
This work addresses medical image segmentation challenges, particularly for boundary accuracy, but appears incremental as it builds upon U-Net with hybrid enhancements.
The authors tackled the problem of poor boundary and detail segmentation in medical images by proposing PLU-Net, which integrates novel modules like PS and LS blocks into U-Net, resulting in improved performance with fewer parameters and FLOPs on three benchmark datasets.
Deep learning algorithms have achieved remarkable results in medical image segmentation in recent years. These networks are unable to handle with image boundaries and details with enormous parameters, resulting in poor segmentation results. To address the issue, we develop atrous spatial pyramid pooling (ASPP) and combine it with the Squeeze-and-Excitation block (SE block), as well as present the PS module, which employs a broader and multi-scale receptive field at the network's bottom to obtain more detailed semantic information. We also propose the Local Guided block (LG block) and also its combination with the SE block to form the LS block, which can obtain more abundant local features in the feature map, so that more edge information can be retained in each down sampling process, thereby improving the performance of boundary segmentation. We propose PLU-Net and integrate our PS module and LS block into U-Net. We put our PLU-Net to the test on three benchmark datasets, and the results show that by fewer parameters and FLOPs, it outperforms on medical semantic segmentation tasks.