Xiaoqi-Zhao-DLUT/MSNet-M2SNet

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1042 stars · 89 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official PyTorch implementations of MSNet and M2SNet, multi-scale subtraction networks for medical image segmentation such as polyp, lung infection, and breast ultrasound segmentation. Includes pretrained models and links to training/testing datasets.

Use cases

• segment polyps in endoscopy images
• segment medical images with a multi-scale network
• reproduce MICCAI polyp segmentation results
• segment COVID-19 lung infection CT images
• segment breast ultrasound images
• apply pretrained medical segmentation models

When to choose

• you need proven medical image segmentation models with pretrained weights
• you work on polyp or ultrasound segmentation research
• you want a strong baseline for multi-scale segmentation

When to avoid

• you need a production-ready clinical application
• you need a license permitting commercial use (none is provided)
• you need segmentation outside medical imaging domains

Facets

library · maturity active

machine-learning deep-learning image-processing computer-vision healthcare deep-learning computer-vision image-processing python medical-image-segmentation polyp-segmentation pytorch research-code miccai gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="Xiaoqi-Zhao-DLUT/MSNet-M2SNet")