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name: medical-detection-toolkit description: "Route MedicalDetectionToolkit research tasks across experiment configuration, medical-image data preparation, detector architecture, prediction/evaluation, and legacy CUDA compatibility while preserving the repository's unmaintained and version-sensitive boundaries." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MedicalDetectionToolkit

Use this repo skill when a task names MedicalDetectionToolkit/MDT or asks about its legacy 2D/3D medical object-detection workflows, experiment configs, toy/ LIDC/PET-CT loaders, Retina U-Net/Mask R-CNN-style models, prediction consolidation, evaluation, or custom NMS/RoIAlign extensions.

First checks

1. Treat the exact source revision as part of the task. Read repo provenance before deciding whether a current checkout can use this graph.
2. Expect a legacy environment: the published metadata pins Python-3.6-era dependencies including torch==0.4.1, and the README says the project is no longer maintained. Do not silently replace it with nnDetection; that is a separate framework.
3. Separate portable configuration/data/analysis guidance from exact detector execution. The latter imports old torch.utils.ffi CUDA wrappers and must pass the compatibility route before any model run.
4. For a new data path, validate a bounded copied/synthetic case first; never download clinical data or run destructive packing/deletion as a smoke test.

Route by task

• Configure an experiment, choose dimensions/models, inspect CLI modes/folds, create an experiment directory, or make a bounded toy fixture: read configuration-and-experiments.
• Prepare arrays/manifests, preserve label semantics, adapt toy/LIDC/PET-CT loaders, or reason about patching/tiling/augmentation: read data-and-preprocessing.
• Choose or explain MRCNN, U-FRCNN, RetinaNet, Retina U-Net, Detection U-Net, FPN/backbone, anchors, heads, losses, or model result contracts: read models-and-architectures.
• Run/inspect prediction modes, saved outputs, WBC, 2D-to-3D merging, ROI or patient metrics, CSVs, or monitoring plots: read inference-and-evaluation.
• Diagnose NMS/RoIAlign import/build/ABI/device issues: read cuda-extensions and stop at its compatibility gate before claiming model execution.

Cross-workflow requests should follow this order: configuration → data contract → model choice → prediction/evaluation; insert CUDA compatibility at the model boundary when a custom operator is imported.

Installation and inspection guidance

For source checkout use, install into an isolated environment only after choosing a dependency variant. The repository's historical requirements are not a modern default; for metadata/API inspection, use python -m pip install --no-deps -e . and then verify metadata with python -c "from importlib.metadata import version; print(version('medicaldetectiontoolkit'))". This only proves package metadata; follow the leaf backend/data checks before running a workflow. Exact reproduction requires the old pinned dependencies and is a separate compatibility decision.

For a historical reproduction, use an isolated environment matching the checkout's documented Python/PyTorch/CUDA ABI only if those artifacts are actually available. For source/API inspection, use a modern isolated Python only to inspect portable modules and record compatibility failures; a current PyTorch CUDA tensor smoke does not validate the repository's custom extensions. The package distribution is medicaldetectiontoolkit (source metadata version 0.0.1), while the checkout exposes top-level modules such as models, utils, predictor, and evaluator.

Do not put source checkout paths, private environments, generated experiment outputs, precompiled .so/.o files, or external datasets into a Researcher workflow. Use the bundled validators and compatibility checker in the leaf sub-skills; each is read-only or bounded by default.

Shared failure policy

• If a loader fails, check package-version drift, axes, channel count, manifest names, and label semantics before changing the model.
• If an old detector import fails at torch.utils.ffi, classify it as a legacy ABI/toolchain block; do not patch imports or claim a CPU substitute.
• If WBC/evaluation fails, validate the result schema and keep raw prediction artifacts immutable before changing thresholds.
• If a path would overwrite data, copy it to a temporary workspace and stop for explicit review.

Read cross-cutting troubleshooting for the common install/import/data/runtime decision table. This root file is a router; API tables, schemas, long workflows, and failure matrices live in the linked leaf references.