VGG16-MCA UNet: Whole-Tumor Segmentation in 2D FLAIR MRI with Decoder-Side Channel Attention

arXiv:2609.23919 · eess.IV, cs.CV · Submitted 2026-09-20 · Read on arXiv

eess.IV, cs.CV

Submitted: 2026-09-20

Updated: 2026-09-20

Comments: 22 pages, 7 figures, 4 tables. Code and data splits: https://github.com/ShubhamGajjar/vgg16-mca-unet

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 11× in this paper · explained in 1 episode
• Attention Mechanism — 9× in this paper · explained in 2 episodes
• Generalization — 7× in this paper · explained in 13 episodes
• Aggregation — 6× in this paper · explained in 1 episode
• Benchmark — 6× in this paper · explained in 2 episodes
• Multi-Channel Attention — 5× in this paper
• Transfer Learning — 5× in this paper · explained in 10 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Data Augmentation — 3× in this paper · explained in 7 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• nnU-Net — 3× in this paper · explained in 2 episodes
• Attention Gate — 2× in this paper
• Consistency — 2× in this paper · explained in 3 episodes
• Convolutional Block Attention Module — 2× in this paper
• Core — 2× in this paper · explained in 2 episodes
• Gradient Flow — 2× in this paper · explained in 2 episodes
• Segmentation Masks — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
• Ensemble Learning — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Federated Learning — 1× in this paper · explained in 14 episodes
• IEEE International Symposium on Biomedical Imaging — 1× in this paper
• Pruning — 1× in this paper · explained in 4 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• U-net architecture — 1× in this paper · explained in 3 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Attention U-Net: Learning Where to Look for the Pancreas
• nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation

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