Tasks SotA Datasets Papers Methods Submit About

Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable Benchmarks All SotA Datasets Papers Methods

Community

Submit Results About

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Models/U-Mamba

U-Mamba

Reported on 6 benchmarks across 2 tasks · 1 paper · 6 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Medical3 results

Semantic SegmentationonThe ULS23 Challenge Test Set
ChallengeScore· 2024-01-09
0.735
SOTA
U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation arXiv:2401.04722
Semantic SegmentationonThe ULS23 Challenge Test Set
Long-Axis SMAPE· 2024-01-09
0.103
best: 0.112 (Baseline Model (Semi-Supervised))
SOTA
U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation arXiv:2401.04722
Semantic SegmentationonThe ULS23 Challenge Test Set
Short-Axis SMAPE· 2024-01-09
0.118
best: 0.12 (Baseline Model (Semi-Supervised))
SOTA
U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation arXiv:2401.04722

Audio3 results

10-shot image generationonThe ULS23 Challenge Test Set
ChallengeScore· 2024-01-09
0.735
SOTA
U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation arXiv:2401.04722
10-shot image generationonThe ULS23 Challenge Test Set
Long-Axis SMAPE· 2024-01-09
0.103
best: 0.112 (Baseline Model (Semi-Supervised))
SOTA
U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation arXiv:2401.04722
10-shot image generationonThe ULS23 Challenge Test Set
Short-Axis SMAPE· 2024-01-09
0.118
best: 0.12 (Baseline Model (Semi-Supervised))
SOTA
U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation arXiv:2401.04722