G-UNeXt: a lightweight MLP-based network for reducing semantic gap in medical image segmentation-Reference-Cited by-同舟云学术

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G-UNeXt: a lightweight MLP-based network for reducing semantic gap in medical image segmentation

Published:2023-09-06 Issue:6 Volume:29 Page:3431-3446
ISSN:0942-4962
Container-title:Multimedia Systems
language:en
Short-container-title:Multimedia Systems

Author:

Zhang Xin,Cao Xiaotian,Wang Jun,Wan Lei

Funder

Natural Science Foundation of Hebei Province

Startup Foundation for Advanced Talents of Hebei University

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Information Systems,Software

Link

https://link.springer.com/content/pdf/10.1007/s00530-023-01173-z.pdf

Reference57 articles.

1. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

2. He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

3. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241. Springer, Cham (2015)

4. Zhou, Z., Rahman Siddiquee, M.M., 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, pp. 3–11. Springer, Cham (2018)

5. Huang, H., Lin, L., Tong, R., et al.: Unet 3+: a full-scale connected unet for medical image segmentation. In: ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1055–1059. IEEE (2020)

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