RTNet: a residual t-shaped network for medical image segmentation
Author:
Funder
Key Scientific Research Project of Higher School of Henan Province
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11042-024-18544-x.pdf
Reference46 articles.
1. Kumari P, Saxena P (2023) Disease localization and its prediction from retinal fundus images using explicitly designed deep learning architecture. Multimed Tools Appl. https://doi.org/10.1007/s11042-023-16585-2
2. Chakraborty S, Mali K (2023) An overview of biomedical image analysis from the deep learning perspective. Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention 43–59. https://doi.org/10.4018/978-1-6684-7544-7.ch003
3. Wan Y, Shao M, Cheng Y, Ding W (2023) Fuzzy-based cross-image pixel contrastive learning for compact medical image segmentation. Multimed Tools Appl. https://doi.org/10.1007/s11042-023-16611-3
4. Xiao H, Li L, Liu Q, Zhu X, Zhang Q (2023) Transformers in medical image segmentation: A review. Biomed Signal Proc Control 84:104791. https://doi.org/10.1016/j.bspc.2023.104791
5. Shamshad F, Khan S, Zamir SW, Khan MH, Hayat M, Khan FS, Fu H (2023) Transformers in medical imaging: A survey. Med Image Anal 102802. https://doi.org/10.1016/j.media.2023.102802
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