---

name: medsegdiff description: "Guide MedSegDiff medical-image segmentation workflows for dataset preparation, diffusion-model training, checkpoint sampling, ensemble aggregation, and evaluation." disable-model-invocation: true metadata: disco-role: operating license: MIT

MedSegDiff

MedSegDiff is a repository-level PyTorch diffusion framework for medical-image segmentation. Use this skill when a task mentions MedSegDiff, its ISIC or BRATS examples, diffusion-based segmentation, the guided_diffusion modules, or the repository's segmentation train/sample/evaluation workflows.

First route

• Prepare or validate data: read sub-skills/data-preparation/SKILL.md. It covers ISIC CSV/image/mask trees, BRATS NIfTI modalities and slices, and custom 2D/3D layouts.
• Configure or plan training: read sub-skills/training/SKILL.md. It covers the training CLI contract, model/diffusion factories, schedules, checkpoints, mixed precision, and multi-GPU behavior.
• Sample or score predictions: read sub-skills/inference-evaluation/SKILL.md. It covers checkpoint compatibility, DDIM/DPM-Solver options, ensembles, STAPLE aggregation, output naming, IoU/Dice, and per-class metrics.

For a task spanning multiple routes, prepare and validate the data first, then train or provide a compatible checkpoint, then sample and evaluate. Preserve the dataset branch, effective input channels, image size, model version, diffusion schedule, and checkpoint settings across every stage.

Installation and runtime gate

The upstream repository has no pyproject.toml, setup.py, console entry point, or formal package metadata. Its documented dependency source is requirement.txt. In a fresh isolated environment, install the public runtime dependencies and make the checkout importable before using its Python modules:

python -m pip install -r requirement.txt
PYTHONDONTWRITEBYTECODE=1 python -c "import guided_diffusion.script_util as s; print(s.model_and_diffusion_defaults())"

Use a CUDA-enabled PyTorch build for actual training or sampling. CPU checks are appropriate for data loaders, metric helpers, and small factory/API smokes, but CPU is not a truthful substitute for the full training/sampling path: the runtime uses CUDA device placement and, during sampling, CUDA timing events and synchronization.

Before a real run, use the bundled safe inspectors and environment reference:

• scripts/check_environment.py checks Python, imports, and optional CUDA availability without downloading data or starting training.
• references/api-reference.md records the verified module and factory contracts.
• references/troubleshooting.md handles cross-cutting install, import, backend, checkpoint, and path failures.
• references/repo-provenance.md tells you when the source baseline is stale and a refresh is warranted.

Keep medical data, checkpoints, predictions, and generated results outside the skill directory. Do not assume the repository's example data is complete; use the data-preparation validator and fetch data only through an approved, user-controlled source.

Shared invariants

• The model factory's default image_size is 64, while the documented example recipes commonly use 256; only 64, 128, 256, and 512 have automatic channel multipliers in the inspected source.
• Boolean options use an explicit value such as True or False, not a bare store_true switch.
• data_name=ISIC and data_name=BRATS select dedicated branches; other values select the custom branch, whose 2D/3D detection has source-specific behavior.
• Keep batch_size=1 for the unpatched sampler because several output-ID paths use the first item of a batch.
• Treat version, effective input channels, architecture flags, diffusion steps/schedule, and checkpoint state as one compatibility tuple.

This skill is guidance for operating the public repository; it does not claim medical validity, reproduce paper metrics without the required data/checkpoint, or replace clinical review.