Optimization of U-shaped pure transformer medical image segmentation network

Abstract

In recent years, neural networks have made pioneering achievements in the field of medical imaging. In particular, deep neural networks based on U-shaped structures are widely used in different medical image segmentation tasks. In order to improve the early diagnosis and clinical decision-making system of lung diseases, it has become a key step to use the neural network for lung segmentation to assist in positioning and observing the shape. There is still the problem of low precision. For the sake of achieving better segmentation accuracy, an optimized pure Transformer U-shaped segmentation is proposed in this article. The optimization segmentation network adopts the method of adding skip connections and performing special splicing processing, which reduces the information loss in the encoding process and increases the information in the decoding process, so as to achieve the purpose of improving the segmentation accuracy. The final experiment shows that our improved network achieves 97.86% accuracy in segmentation of the “Chest Xray Masks and Labels” dataset, which is better than the full convolutional network or the combination of Transformer and convolution.

Binary semantic segmentation for detection of prostate adenocarcinoma using an ensemble with attention and residual U-Net architectures

“…To address spatial information loss during down-sampling in Swin-Unet, Dan et al (2023) introduced enhancements by incorporating multi-scale skip connections and special splicing modules. This optimization aims to minimize information loss in the encoder while aggregating information in the decoder.…”