CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer With Modality-Correlated Cross-Attention for Brain Tumor Segmentation

Author:

Lin Jianwei1,Lin Jiatai1,Lu Cheng2,Chen Hao3ORCID,Lin Huan2ORCID,Zhao Bingchao2,Shi Zhenwei2ORCID,Qiu Bingjiang2,Pan Xipeng2,Xu Zeyan2,Huang Biao2ORCID,Liang Changhong2,Han Guoqiang1ORCID,Liu Zaiyi2ORCID,Han Chu2ORCID

Affiliation:

1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

2. Department of Radiology and the Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China

3. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Sai Kung, Hong Kong

Funder

Key-Area Research and Development Program of Guangdong Province

Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application

National Key Research and Development Program of China

National Science Fund for Distinguished Young Scholars

Regional Innovation and Development Joint Fund of National Natural Science Foundation of China

National Science Foundation for Young Scientists of China

National Natural Science Foundation of China

High-level Hospital Construction Project of Guangdong Provincial People's Hospital

China Postdoctoral Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Reference55 articles.

1. The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classification;baid;arXiv 2107 02314,2021

2. Multimodal learning with transformers: A survey;xu;arXiv 2206 06488,2022

3. A review on brain tumor segmentation of MRI images;anjali;Magn Reson Imag,2019

4. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

5. Coupling nnU-Nets with Expert Knowledge for Accurate Brain Tumor Segmentation from MRI

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