How Far Can INRs Go? Cross-Domain Parameter-efficient INR-Based Semantic Segmentation for Brain MRI

arXiv:2609.31573 · cs.CV · Submitted 2026-09-25 · Read on arXiv

cs.CV

Submitted: 2026-09-25

Updated: 2026-09-25

Terminology

• Transformer — 9× in this paper · explained in 2 episodes
• nnU-Net — 7× in this paper · explained in 2 episodes
• Meta-learning — 6× in this paper · explained in 2 episodes
• Adaptation — 5× in this paper · explained in 1 episode
• Data Augmentation — 5× in this paper · explained in 7 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Domain Shift — 4× in this paper · explained in 4 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Parameter Efficiency — 1× in this paper · explained in 3 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Two-stage training — 1× in this paper · explained in 2 episodes

Sources

• Medical Image Segmentation Review: The success of U-Net
• TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
• CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations
• COIN: COmpression with Implicit Neural representations
• From data to functa: Your data point is a function and you can treat it like one
• Where Do We Stand with Implicit Neural Representations? A Technical and Performance Survey
• Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
• nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation
• Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation
• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
• Equivariant Architectures for Learning in Deep Weight Spaces
• SSDNeRF: Semantic Soft Decomposition of Neural Radiance Fields
• U-Net: Convolutional Networks for Biomedical Image Segmentation
• Implicit Neural Representations with Periodic Activation Functions
• NISF: Neural Implicit Segmentation Functions
• I-MedSAM: Implicit Medical Image Segmentation with Segment Anything
• WSI-INR: Implicit Neural Representations for Lesion Segmentation in Whole-Slide Images
• OSCAR: Occupancy-based Shape Completion via Acoustic Neural Implicit Representations
• pixelNeRF: Neural Radiance Fields from One or Few Images
• Q2A: Querying Implicit Fully Continuous Feature Pyramid to Align Features for Medical Image Segmentation

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