FSS-UBrain: Multi-region Few-Shot Brain Tumor MRI Segmentation

arXiv:2609.32273 · cs.CV · Submitted 2026-09-26 · Read on arXiv

cs.CV

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Transformer — 20× in this paper · explained in 2 episodes
• Adaptation — 12× in this paper · explained in 1 episode
• Core — 10× in this paper · explained in 2 episodes
• Regularization — 9× in this paper · explained in 3 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Aggregation — 8× in this paper · explained in 1 episode
• Alignment — 8× in this paper · explained in 1 episode
• Calibration — 6× in this paper · explained in 7 episodes
• Provenance — 6× in this paper · explained in 4 episodes
• Robustness — 6× in this paper · explained in 5 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Attention Mechanism — 3× in this paper · explained in 3 episodes
• Clustering — 3× in this paper · explained in 2 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Future Communications and Computer Science — 3× in this paper
• Ontology — 3× in this paper · explained in 2 episodes
• Center for Biomedical Image Computing and Analytics — 2× in this paper
• Feature Extraction — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• Transformer Architecture — 2× in this paper · explained in 2 episodes
• nnU-Net — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Cooperation — 1× in this paper · explained in 2 episodes
• Cross-Attention Mechanism — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Transferability — 1× in this paper · explained in 4 episodes

Sources

• Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
• TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
• Rethinking Atrous Convolution for Semantic Image Segmentation
• Attention U-Net: Learning Where to Look for the Pancreas
• One-Shot Learning for Semantic Segmentation
• The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI
• nnFormer: Interleaved Transformer for Volumetric Segmentation

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models