UJAT-Net: A U-Net Combined Joint-Attention and Transformer for Breast Tubule Segmentation in H&E Stained Images

作者：Liu Li-wang, Zhao Long Huang, Kao-Yan Lu, Zuxuan Wang, Yao‐Ming Liang, Shi-Yu Lin, Yanhong Ji · 发表于：IEEE Access · 年份：2024 · DOI：10.1109/access.2024.3369678 · 被引用次数：7 · 研究领域：AI in cancer detection、Radiomics and Machine Learning in Medical Imaging、Advanced Image Fusion Techniques

The formation of breast tubules is an important evaluation index in the pathological grading of breast cancer. However, the tubules of breast present a wide variety of morphologies and a significant demonstrated significant advantages in the automatic analysis of histopathology images. We propose a Joint Attention and Transformer U-Net network to accurately segment breast tubules, named UJAT-Net. UJAT-Net uses the Joint Attention Block (named JA BLOCK) as the encoder of the network to enhance the extraction effect of the network for different layer features. And the Channel Cross fusion with Transformer (named CCT) module is used as the skip connection structure of the network. Furthermore, we employ a Transpose Cross Attention (named TCA) module as the decoder of the network to fuse the features of the skip connection layer and the decoder. Experimental results on our own invasive breast cancer tubule (Tubule of Breast Cancer, TBC) dataset and the benchmark dataset (Glas) of the GlaS challenge contest at MICCAI’2015 demonstrate that our method achieves competitive performance.