Comparative Analysis of Transformer Integration in U-net Networks for Enhanced Medical Image Segmentation

Abstract

Transformer is popular in Natural Language Processing (NLP) and is a cornerstone of large models. Transformer has been used by researchers to address the limitations of Convolutional Neural Networks (CNNs) in medical picture segmentation models. Through an extensive literature review and case studies, this paper comparatively analyzes the performance of different models in this field, summarizes different methods of integrating transformers into U-net, and points out existing gaps and challenges. Research has found that the Transformer model can significantly improve the accuracy and efficiency of medical image analysis. The paper discusses the advantages, disadvantages, innovations, performance, and complexity of various models in detail, and shows how to enhance performance by integrating the Transformer structure into the U-net network. In particular, the paper also analyzes the advantages of Transformers that are most suitable for integration into the encoder part and highlights the balance that needs to be made between improving performance and computational cost. The conclusion shows that although there is no perfect model, optimal performance and efficiency can be achieved by selecting different combinations of Transformer and U-net according to the actual situation. It can be seen from the networks’ performance that the mixed use of a U-shaped convolutional network and Transformer module has good development prospects and high research significance.