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name: data-loading-preprocessing description: "Prepare, inspect, and smoke-test MedicalZooPytorch dataset layouts, medical-image preprocessing, subvolume generation, normalization, resampling, coordinate transforms, and 3D augmentation." metadata: disco-role: operating disable-model-invocation: true license: MIT

Data Loading and Preprocessing

Use this sub-skill for the repository's data-preparation path only:

• dataset folder layouts and manifest formats
• loader arguments and dispatcher entry points
• subvolume generation and cached patch files
• normalization, resampling, cropping, and coordinate transforms
• 3D augmentation operators and synthetic smoke checks

Do not use this route for model selection, training, checkpointing, inference, or loss design. Hand those off to sibling sub-skills.

Start here

• references/data-layout.md — dataset folders, filenames, manifests, and generated cache paths
• references/workflows.md — preprocessing recipes, loader argument map, and smoke-script usage
• references/troubleshooting.md — common missing-data, shape, and transform failures
• scripts/smoke_preprocessing.py — synthetic NIfTI preprocessing smoke
• scripts/smoke_augmentations.py — synthetic 3D augmentation smoke
• scripts/smoke_dataloaders.py — synthetic loader and manifest smoke

Key entry points

• lib.medloaders.generate_datasets(...)
• lib.medloaders.select_full_volume_for_infer(...)
• lib.medloaders.medical_image_process.load_medical_image(...)
• lib.medloaders.medical_loader_utils.create_sub_volumes(...)
• lib.augment3D.RandomChoice(...)

Coverage

This route covers the loader and preprocessing families that prepare data for later training or inference:

• ISEG 2017 and 2019
• BraTS 2018, 2019, and 2020
• MRBRAINS 2018
• IXI T1/T2
• MICCAI 2019 Gleason pathology
• COVIDx and COVID CT manifest-based 2D loaders

Native-check note

The bundled scripts use tiny synthetic fixtures only. The repo's original data-loader checks remain blocked until the real datasets are placed in the documented folders.