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name: medical-zoo-pytorch description: "Operate MedicalZooPytorch 3D medical image segmentation, data loading, losses, training, inference, and COVID 2D classification workflows." metadata: disco-role: operating disable-model-invocation: true license: MIT

MedicalZooPytorch

Use this repo skill when a task involves MedicalZooPytorch, its lib.* modules, 3D medical image segmentation in PyTorch, medical-image dataset loaders, segmentation losses, training/inference loops, or the repository's small 2D COVID classification branch.

First checks

1. Confirm the MedicalZooPytorch code is available as importable Python modules. This repository exposes lib.* directly and may not provide package metadata, so a local checkout often needs its repository root on PYTHONPATH or another explicit import path.
2. Install a PyTorch build appropriate for the user's CPU/CUDA environment, then install the non-torch runtime dependencies used by selected workflows: nibabel, scipy, matplotlib, Pillow, tensorboard, torchsummary, torchsummaryX, and torchvision for the COVID branch.
3. Run the root smoke check before using deeper routes:

python scripts/smoke_repo_imports.py
python scripts/smoke_repo_imports.py --cuda

Use --package-root /path/to/MedicalZooPytorch if the modules are not already importable. Do not run full training or the original examples until dataset folders and checkpoint/output paths are confirmed.

Route by task

• 3D model selection, training, checkpoints, TensorBoard, inference, or visualization: open sub-skills/segmentation-workflows/SKILL.md.
• Dataset folder layout, loader arguments, image preprocessing, subvolume generation, normalization, resampling, coordinate transforms, or augmentation: open sub-skills/data-loading-preprocessing/SKILL.md.
• Loss selection, Dice/CE/weighted/contrastive/angular losses, one-hot expansion, shape contracts, or trainer criterion return types: open sub-skills/losses-and-metrics/SKILL.md.
• 2D COVID X-ray/CT classification, COVIDx/CovidCT manifests, COVID model caveats, or COVID metric tracking: open sub-skills/covid-2d-classification/SKILL.md.

Shared references

• references/workflows.md summarizes the end-to-end route map and how the sub-skills combine.
• references/troubleshooting.md covers cross-cutting install, data, CUDA, path, and package-import failures.
• references/repo-provenance.md records the source baseline and evidence paths used to build this skill.
• references/repo-routing-metadata.json is structured routing metadata for the managed repo-skills router.

Repository operating model

MedicalZooPytorch is source-tree-oriented rather than packaged like a modern PyPI project. Its public import surface is mostly:

• lib.medzoo: model classes plus create_model(args).
• lib.medloaders: dataset loader classes plus generate_datasets(args, path=...).
• lib.losses3D: factory and concrete loss classes.
• lib.train: trainer loops.
• lib.visual3D_temp: TensorBoard writer and visualization helpers.
• lib.utils: reproducibility, input preparation, checkpoint-era helpers, and COVID metrics.

The old example launchers are valuable evidence, but this skill distills them into bundled references and smoke scripts. Do not point a future user at source examples as required runtime documentation; use the sub-skill workflows instead.

Backend and data expectations

• CPU is enough for import checks, loss checks, preprocessing checks, and tiny model forward passes.
• CUDA is needed to truthfully exercise paths that move models/tensors with .cuda() or that mirror the historical GPU-oriented training/inference examples.
• Real dataset loader runs require the actual ISEG/BraTS/MRBRAINS/IXI/MICCAI/COVID files in the documented folder structure; the repository includes mostly placeholder readmes and some COVID manifest text files, not the full image data.
• Full training and inference are data-heavy and long-running. Use bundled smoke scripts first, then run full experiments only after data paths, output directories, model class, loss return contract, and device placement are confirmed.

Safe bundled scripts

• scripts/smoke_repo_imports.py checks cross-cutting imports and optional CUDA availability.
• Each sub-skill has smaller scripts for its owned workflow. Prefer those before running a complete training job.

Avoid this skill when

• The task is about another medical-imaging framework such as MONAI, nnU-Net, or TorchIO and does not name MedicalZooPytorch or its APIs.
• The user wants general medical-imaging theory rather than operating this package.
• The task is repository maintenance or code editing rather than package usage; then use normal repo-development context and tests instead of this runtime operating graph.