MH2AFormer: An Efficient Multiscale Hierarchical Hybrid Attention With a Transformer for Bladder Wall and Tumor Segmentation-Reference-Cited by-同舟云学术

MH2AFormer: An Efficient Multiscale Hierarchical Hybrid Attention With a Transformer for Bladder Wall and Tumor Segmentation

Author:

Li Xiang¹^ORCID,Wang Jian²^ORCID,Wei Haifeng³^ORCID,Cong Jinyu⁴^ORCID,Sun Hongfu⁵^ORCID,Wang Pingping⁴^ORCID,Wei Benzheng⁴^ORCID

Affiliation:

1. First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China

2. Science College, Shandong Jiaotong University, Jinan, China

3. Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China

4. Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, China

5. Shandong Research Academy of Traditional Chinese Medicine, Jinan, China

Funder

National Natural Science Foundation of China

Natural Science of Foundation Shandong Province

Introduction and Cultivation Program for Young Creative Talents in Colleges and Universities of Shandong Province

science and technology of traditional Chinese medicine project of Shandong Province

Qilu Health and Health Leading Talents Training Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

Reference48 articles.

4. A new adaptive Markov random field model in a coupled level set framework for bladder wall segmentation in MR images;Han,2013