pith:UAIHHKWF
U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation
U-Mamba pairs convolutional layers with state space models to capture long-range dependencies more effectively than prior CNN or Transformer networks for biomedical image segmentation.
arxiv:2401.04722 v1 · 2024-01-09 · eess.IV · cs.CV · cs.LG
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Claims
The results reveal that U-Mamba outperforms state-of-the-art CNN-based and Transformer-based segmentation networks across all tasks.
That the hybrid CNN-SSM block will reliably improve long-range dependency capture and generalization across diverse biomedical datasets without introducing training instability or requiring dataset-specific tuning beyond the claimed self-configuring mechanism.
U-Mamba is a hybrid CNN-SSM architecture that outperforms prior CNN and Transformer networks on biomedical image segmentation tasks by efficiently modeling long-range dependencies.
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| First computed | 2026-05-17T23:38:47.857423Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/UAIHHKWFHZSXETKFDKWESAPHR2 \
| jq -c '.canonical_record' \
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Canonical record JSON
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