Self-calibrated convolution towards glioma segmentation
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 20 references that are in the map. First posted on arXiv 7 Feb 2024.
Builds on
8 of 20 references.
References · 8
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
- 2016Instance Normalization: The Missing Ingredient for Fast Stylizationcited by 1,187
- 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 507
- 20183D MRI brain tumor segmentation using autoencoder regularizationcited by 324
- 2017Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentationcited by 58
- 2021Optimized U-Net for Brain Tumor Segmentationcited by 40
- 2021Extending nn-UNet for brain tumor segmentationcited by 36
Built on by
Not cited yet.