has been cited by the following article(s):
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[1]
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A Review of U-Net Based Deep Learning Frameworks for MRI-Based Brain Tumor Segmentation
Diagnostics,
2026
DOI:10.3390/diagnostics16040506
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[2]
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From imaging to omics: deep learning is bridging MRI and liquid biopsy in bone tumor diagnosis
Journal of Bone Oncology,
2026
DOI:10.1016/j.jbo.2026.100753
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[3]
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A Morphology-Aware Preprocessing Pipeline for Improved Glioblastoma Segmentation in Brain MRI Using Hybrid Segformer
2026 IEEE 2nd International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN),
2026
DOI:10.1109/QPAIN69676.2026.11546603
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[4]
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Mamba Meets U-Net: A Comprehensive Study for Brain Tumor Segmentation
2026 1st International Conference on Artificial Intelligence & Machine Learning in Communication and Power Systems (AIMLCPS),
2026
DOI:10.1109/AIMLCPS68702.2026.11542439
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[5]
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Enhancing 3D MRI-Based Necrotic Core Segmentation in Glioblastoma Using Activation Functions in Deep Learning
Informatics,
2026
DOI:10.3390/informatics13070118
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[6]
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Biomedical Engineering Systems and Technologies
Communications in Computer and Information Science,
2026
DOI:10.1007/978-3-032-34459-5_15
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[7]
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From U-Net to Swin-Unet Transformers: The Next-Generation Advances in Brain Tumor Segmentation with Deep Learning
Journal of Biomedical Science and Engineering,
2025
DOI:10.4236/jbise.2025.188024
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[8]
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Current trends in glioma tumor segmentation: A survey of deep learning modules
Physica Medica,
2025
DOI:10.1016/j.ejmp.2025.104988
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[9]
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Automated Brain Tumor MRI Segmentation Using ARU-Net with Residual-Attention Modules
Diagnostics,
2025
DOI:10.3390/diagnostics15182326
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[10]
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Segmentation of glioma sub-regions based on EnnUnet in tumor treating fields
Biomedical Physics & Engineering Express,
2025
DOI:10.1088/2057-1976/ae13b5
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