Adaptive Gate-Aware Mamba Networks for Magnetic Resonance Fingerprinting

arXiv:2507.03369 · eess.IV, cs.LG, eess.SP · Submitted 2025-07-04 · Read on arXiv

eess.IV, cs.LG, eess.SP

Submitted: 2025-07-04

Updated: 2025-07-04

Terminology

• Fingerprinting — 31× in this paper · explained in 2 episodes
• Magnetic Resonance Fingerprinting — 23× in this paper
• Transformer — 10× in this paper · explained in 2 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Accuracy — 8× in this paper · explained in 1 episode
• Consistency — 7× in this paper · explained in 3 episodes
• Gate-Aware Spatial-Temporal — 6× in this paper
• Residual State-Space Block — 6× in this paper
• Deep Spatial Feature Extractor — 5× in this paper
• Initial Fingerprint Encoder — 5× in this paper
• Ablation study — 4× in this paper · explained in 1 episode
• Robustness — 4× in this paper · explained in 5 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Feature Extraction — 3× in this paper · explained in 2 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Attention Mechanism — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Generalizability — 2× in this paper · explained in 6 episodes
• Redundancy — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Residual State-Space Block Groups — 2× in this paper
• Singular Value Decomposition (SVD) — 2× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Contextual Awareness — 1× in this paper · explained in 2 episodes
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Parameter Efficiency — 1× in this paper · explained in 3 episodes
• Principal Component Analysis (PCA) — 1× in this paper · explained in 4 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Signal-to-noise ratio (SNR) — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• State Space Models (SSMs) — 1× in this paper · explained in 4 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• Mamba: Linear-Time Sequence Modeling with Selective State Spaces
• MambaIRv2: Attentive State Space Restoration
• Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation
• MedMamba: Vision Mamba for Medical Image Classification

Related papers

• Revisiting Integration of Image and Metadata for DICOM Series Classification: Cross-Attention and Dictionary Learning
• VesselSDF: Distance Field Priors for Vascular Network Reconstruction
• cSVR: Convolutional Slice-to-Volume Reconstruction
• NAIMA: Semantics Aware RGB Guided Depth Super-Resolution
• AneumoBench: A Source-Linked Benchmark for Synthetic-Geometry Transfer in Aneurysm CFD
• RETO: A Rotary-Enhanced Transformer Operator for High-Fidelity Prediction of Automotive Aerodynamics