Dynamic Linear Transformer for 3D Biomedical Image Segmentation-Reference-Cited by-同舟云学术

Dynamic Linear Transformer for 3D Biomedical Image Segmentation

Published:2022 Issue: Volume: Page:171-180
ISSN:0302-9743
Container-title:Machine Learning in Medical Imaging
language:
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Author:

Zhang Zheyuan,Bagci Ulas

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-21014-3_18

Reference21 articles.

1. Antonelli, M., et al.: The medical segmentation decathlon. arXiv preprint arXiv:2106.05735 (2021)

2. Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. arXiv preprint arXiv:2105.05537 (2021)

3. Chen, J., et al.: Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)

4. Hatamizadeh, A., et al.: UNETR: transformers for 3D medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022)

5. Hong, S., et al.: Hypernet-ensemble learning of segmentation probability for medical image segmentation with ambiguous labels. arXiv preprint arXiv:2112.06693 (2021)

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