Beckschen/TransUNet

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

3234 stars · 583 forks observed · 2026-08-28

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

Official PyTorch implementation of TransUNet, a U-Net-style architecture that uses a Vision Transformer encoder for medical image segmentation, supporting both 2D and 3D data. It includes training and testing scripts, pretrained ViT checkpoints, and datasets like Synapse and ACDC.

Use cases

• segment medical images with a transformer-based U-Net
• train a segmentation model on the Synapse multi-organ dataset
• run 3D medical image segmentation on CT volumes
• benchmark against nn-UNet on BTCV or ACDC
• fine-tune pretrained ViT weights for segmentation
• reproduce results from the TransUNet paper

When to choose

• you need state-of-the-art transformer-based medical image segmentation in PyTorch
• you want a published, well-cited reference implementation with pretrained weights
• you work with 2D medical images or 3D volumes and want one codebase for both

When to avoid

• you need a production-ready, pip-installable segmentation library rather than research code
• you lack a GPU or want lightweight CPU inference
• you need general-purpose (non-medical) image segmentation out of the box

Facets

library · maturity stable

machine-learning deep-learning image-processing computer-vision deep-learning computer-vision healthcare python segmentation unet vision-transformer medical-image-segmentation research-code pytorch medical-imaging gpu linux

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markdown · JSON · MCP: product_card(name="Beckschen/TransUNet")