SwinD-Net: a lightweight segmentation network for laparoscopic liver segmentation-Reference-Cited by-同舟云学术

SwinD-Net: a lightweight segmentation network for laparoscopic liver segmentation

Published:2024-03-20 Issue:1 Volume:29 Page:
ISSN:2469-9322
Container-title:Computer Assisted Surgery
language:en
Short-container-title:Computer Assisted Surgery

Author:

Ouyang Shuiming¹²,He Baochun¹²,Luo Huoling¹,Jia Fucang¹²³

Affiliation:

1. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

2. Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China

3. Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, Shenzhen, China

Funder

National Natural Science Foundation of China

Research on the Evaluation Index System of Information Literacy Course Teaching Quality in Applied Undergraduate Universities

Guangdong Natural Science Foundation

the Shenzhen Key Basic Research Grant

the Zhuhai Science and Technology Program

the Shenzhen Basic Research Grant

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/24699322.2024.2329675

Reference32 articles.

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2. Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al. UNet++: a nested U-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support, LNCS. vol. 11045; 2018. pp. 3–11.

3. ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data

4. Oktay O Schlemper J Folgoc LL et al. Attention U-Net: learning where to look for the pancreas. arXiv preprint. arXiv:1804.03999. 2018.

5. Chen LC, Zhu Y, Papandreou G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation In Proceedings of the European Conference on Computer Vision (ECCV). 2018. pp. 801–818.

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