Region Aware Transformer for Automatic Breast Ultrasound Tumor Segmentation
Xiner Zhu, Haoji Hu, Hualiang Wang, Jincao Yao, 李 伟 liwei, Di Ou, Dong Xu
Although Automatic Breast Ultrasound (ABUS) has become an important tool to detect breast cancer, computer-aided diagnosis requires accurate segmentation of tumors on ABUS. In this paper, we propose the Region Aware Transformer Network (RAT-Net) for tumor segmentation on ABUS images. RAT-Net incorporates region prior information of tumors into network design. The specially designed Region Aware Self-Attention Block(RASAB) and Region Aware Transformer Block (RATB) fuse the tumor region information into multi-scale features to obtain accurate segmentation. To the best of our knowledge,it is the first time that tumor region distributions are incorporated into network architectures for ABUS image segmentation. Experimental results on a dataset containing 84,480 ABUS images taken from 256 subjects show that RAT-Net outperforms other state-of-the-art methods.
Wednesday 6th July
Poster Session 1.1 - onsite 15:20 - 16:20, virtual 11:00 - 12:00 (UTC+2)