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Cited by 5
MRI Brain Tumour Segmentation Using Multiscale Attention U-Net

2025 4th International Joint Conference on Information and Communication Engineering (JCICE)
Mingzhe Zhou
https://doi.org/10.1109/JCICE66205.2025.11182045
Conference  (2025), p. 1
A Novel 3D U-Net–Vision Transformer Hybrid with Multi-Scale Fusion for Precision Multimodal Brain Tumor Segmentation in 3D MRI
Fathia Ghribi, Fayçal Hamdaoui
https://doi.org/10.3390/electronics14183604
Journal  Electronics Volume 14, Issue 18 (2025), p. 3604
DKA-U-Net: Dynamic-Kernel Attention U-Net for Brain Tumor Segmentation From MRI
Junjie Zhu, Heng Liu
https://doi.org/10.1109/ACCESS.2026.3693063
Journal  IEEE Access Volume 14 (2026), p. 73935
GAME-Net: an ensemble deep learning framework integrating Generative Autoencoders and attention mechanisms for automated brain tumor segmentation in MRI
Ihtisham Ul Haq, Abid Iqbal, Muhammad Anas, Fahad Masood, Ali S. Alzahrani, Mohammed Al-Naeem
https://doi.org/10.3389/fncom.2025.1702902
Journal  Frontiers in Computational Neuroscience Volume 19 (2025)
Hybrid MICO-LAC Segmentation with Panoptic Tumor Instance Analysis for Dense Breast Mammograms
Razia Jamil, Min Dong, Orken Mamyrbayev, Ainur Akhmediyarova
https://doi.org/10.3390/jimaging12030095
Journal  Journal of Imaging Volume 12, Issue 3 (2026), p. 95


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