DenseTrans: Multimodal Brain Tumor Segmentation Using Swin Transformer
Author:
Affiliation:
1. College of Information Science and Engineering, Xinjiang University, Ürümqi, China
2. Information Research and Development Center, 940th Hospital of the PLA Joint Logistic Support Force, Lanzhou, China
Funder
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10113635.pdf?arnumber=10113635
Reference44 articles.
1. TransUNet: Transformers make strong encoders for medical image segmentation;chen;arXiv 2102 04306,2021
2. Brain Tumor Segmentation Using Dual-Path Attention U-Net in 3D MRI Images
3. Extending nn-UNet for Brain Tumor Segmentation
4. Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-Net neural networks: A BraTS 2020 challenge solution;henry;Proc Int MICCAI Brainlesion Workshop,2020
5. Transformer-based multi-scale feature fusion network for remote sensing change detection
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4. Exploring Deep Learning-Based MRI Radiomics for Brain Tumor Prognosis and Diagnosis;2023 3rd Asian Conference on Innovation in Technology (ASIANCON);2023-08-25
5. TransDoubleU-Net: Dual Scale Swin Transformer With Dual Level Decoder for 3D Multimodal Brain Tumor Segmentation;IEEE Access;2023
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