Brain Tumor Image Segmentation Network Based on Dual Attention Mechanism
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-4761-4_11
Reference19 articles.
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2. Cinar, N., Ozcan, A., Kaya, M.: A hybrid DenseNet121-UNet model for brain tumor segmentation from MR Images. Biomed. Signal Proces. Control 76, 103647 (2022). https://doi.org/10.1016/j.bspc.2022.103647
3. Wang, S., et al.: Label-free detection of the architectural feature of blood vessels in glioblastoma based on multiphoton microscopy. IEEE J. Sel. Topics Quantum Electron. 27(4), 1–7 (2021). https://doi.org/10.1109/JSTQE.2021.3058175
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5. Zhao, X., et al.: A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med. Image Anal. 43, 98–111 (2017). https://doi.org/10.1016/j.media.2017.10.002
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