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Shanlin Sun
Researcher at University of California, Irvine
Publications - 19
Citations - 156
Shanlin Sun is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 3, co-authored 8 publications receiving 22 citations.
Papers
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Journal ArticleDOI
A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy.
Xuming Chen,Shanlin Sun,Narisu Bai,Hao Tang,Qianqian Liu,Shengyu Yao,Kun Han,Chupeng Zhang,Zhipeng Lu,Qian Huang,Guoqi Zhao,Yi Xu,Tingfeng Chen,Xiaohui Xie,Yong Liu +14 more
TL;DR: WBNet as discussed by the authors is a deep learning-based automatic segmentation (AS) algorithm that can accurately and efficiently delineate all major OARs in the entire body directly on CT scans.
Posted Content
AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation.
TL;DR: In this article, Axial Fusion Transformer UNet (AFTer-UNet) is proposed, which takes both advantages of convolutional layers' capability of extracting detailed features and transformers' strength on long sequence modeling.
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Recurrent Mask Refinement for Few-Shot Medical Image Segmentation
TL;DR: In this paper, a context relation encoder (CRE) and a recurrent mask refinement module are proposed to capture local relation features between foreground and background regions and refine the segmentation mask iteratively.
Proceedings ArticleDOI
Spatial Context-Aware Self-Attention Model For Multi-Organ Segmentation
Hao Tang,Xingwei Liu,Kun Han,Xiaohui Xie,Xuming Chen,Huang Qian,Yong Liu,Shanlin Sun,Narisu Bai +8 more
TL;DR: In this article, a self-attention mechanism is used to control which 3D features should be used to guide 2D segmentation, and the segmentation is realized through high-resolution 2D convolutions, but guided by spatial contextual information extracted from a low-resolution 3D model.
Posted Content
Spatial Context-Aware Self-Attention Model For Multi-Organ Segmentation
Hao Tang,Xingwei Liu,Kun Han,Shanlin Sun,Narisu Bai,Xuming Chen,Huang Qian,Yong Liu,Xiaohui Xie +8 more
TL;DR: A new framework for combining 3D and 2D models is proposed, in which the segmentation is realized through high-resolution 2D convolutions, but guided by spatial contextual information extracted from a low-resolution 3D model.