no code implementations • 14 Jun 2022 • Wenxuan Wang, Chen Chen, Jing Wang, Sen Zha, Yan Zhang, Jiangyun Li
For 3D medical image (e. g. CT and MRI) segmentation, the difficulty of segmenting each slice in a clinical case varies greatly.
1 code implementation • 9 Apr 2022 • Jiangyun Li, Sen Zha, Chen Chen, Meng Ding, Tianxiang Zhang, Hong Yu
First, commonly used upsampling methods in the decoder such as interpolation and deconvolution suffer from a local receptive field, unable to encode global contexts.
1 code implementation • 29 Mar 2022 • Jiangyun Li, Hong Yu, Chen Chen, Meng Ding, Sen Zha
In this model, we design a Supervised Attention Module (SAM) based on the attention mechanism, which can capture more accurate and stable long-range dependency in feature maps without introducing much computational cost.
1 code implementation • 30 Jan 2022 • Jiangyun Li, Wenxuan Wang, Chen Chen, Tianxiang Zhang, Sen Zha, Jing Wang, Hong Yu
Different from TransBTS, the proposed TransBTSV2 is not limited to brain tumor segmentation (BTS) but focuses on general medical image segmentation, providing a stronger and more efficient 3D baseline for volumetric segmentation of medical images.
2 code implementations • 7 Mar 2021 • Wenxuan Wang, Chen Chen, Meng Ding, Jiangyun Li, Hong Yu, Sen Zha
To capture the local 3D context information, the encoder first utilizes 3D CNN to extract the volumetric spatial feature maps.