Self-calibrated convolution towards glioma segmentation
<|reference_start|>Self-calibrated convolution towards glioma segmentation: Accurate brain tumor segmentation in the early stages of the disease is crucial for the treatment's effectiveness, avoiding exhaustive visual inspection of a qualified specialist on 3D MR brain images of multiple protocols (e.g., T1, T2, T2-FLAIR, T1-Gd). Several networks exist for Glioma segmentation, being nnU-Net one of the best. In this work, we evaluate self-calibrated convolutions in different parts of the nnU-Net network to demonstrate that self-calibrated modules in skip connections can significantly improve the enhanced-tumor and tumor-core segmentation accuracy while preserving the wholetumor segmentation accuracy.<|reference_end|>
@article{salvagnini2024self-calibrated,
    title={Self-calibrated convolution towards glioma segmentation},
    author={Felipe C. R. Salvagnini and Gerson O. Barbosa and Alexandre X. Falcao
  and Cid A. N. Santos},
    journal={arXiv preprint arXiv:2402.05218},
    year={2024},
    doi={10.1109/SIPAIM56729.2023.10373517},
    archivePrefix={arXiv},
    eprint={2402.05218},
    primaryClass={eess.IV cs.CV cs.LG}
}