# bowang-lab/U-Mamba

U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

Repository: https://github.com/bowang-lab/U-Mamba
Canonical: https://ross.abutalabs.com/products/u-mamba
Homepage: https://arxiv.org/abs/2401.04722
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2024-04-04T22:14:54+00:00

## Health v2 (maintenance only)
Score: 26/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 70
- inputs: {"age_days": 992, "days_push": 881, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1019, forks 108 (observed 2026-08-28T04:03:15.075519+00:00)

## What it is
U-Mamba is a hybrid CNN-state-space-model (Mamba) network for biomedical image segmentation, built on top of the nnU-Net framework. It combines convolutional local feature extraction with Mamba's long-range dependency modeling and self-configures to datasets for 2D and 3D segmentation tasks.

## Use cases
- segment organs in CT and MRI scans
- segment cells in microscopy images
- segment surgical instruments in endoscopy video
- train a segmentation model on my own medical dataset
- try Mamba-based architectures for medical image segmentation
- get state-of-the-art biomedical segmentation without manual tuning

## When to choose
- you need long-range dependency modeling in 2D/3D medical image segmentation
- you already use nnU-Net and want a stronger network architecture
- you work with CT, MRI, endoscopy, or microscopy segmentation tasks

## When to avoid
- you need a lightweight CPU-only solution - it requires CUDA GPUs and Mamba SSM kernels
- your task is general natural-image segmentation rather than biomedical
- you need Windows or macOS support - it targets Ubuntu with CUDA 11.8

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, image-processing, computer-vision
- domain: deep-learning, computer-vision, healthcare, bioinformatics
- platform: python
- tags: image-segmentation, mamba, state-space-models, nnunet, biomedical-imaging, medical-imaging, research-code, linux, gpu

## Member repositories
- bowang-lab/U-Mamba (main) score 26

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:15.075519+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:09:21.764099+00:00, confidence not recorded.
  - readme: https://github.com/bowang-lab/U-Mamba (fetched 2026-08-28T04:03:15.075519+00:00, sha 2a34618a5720)
  - homepage: https://arxiv.org/abs/2401.04722 (fetched 2026-08-29T13:09:39.774789+00:00, sha 6cadcbc5c3a1)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T13:09:39.784053+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T13:09:39.787610+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T13:09:39.789615+00:00, sha a1f16f915a9a)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T13:09:39.785983+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.