NV-Segment-CT Finetune

Purpose

Used for smoke or dataset finetuning of NV-Segment-CT VISTA3D on CT NIfTI labels, including the upstream fixed-channel softmax workflow and optional MLflow tracking. Not for clinical validation.
Wraps the upstream MONAI bundle entrypoint; do not replace it with handwritten training or inference code.
Manifest inputs are dataset_dir, datalist, target_anatomy, label_mapping, smoke, sanity, auto_seg, softmax, skip_formal_eval, mlflow_tracking_uri, mlflow_experiment_name, and mlflow_run_name.
Manifest outputs are finetuned_ckpt and schema-checked result_json.

Run scripts/run_finetune.py; do not patch files under bundle/ or upstream checkouts during normal skill use.
For standalone Bash, include the fresh-environment setup line before the wrapper; benchmark venvs start empty.
Run the committed script in place from the repo root. Do not copy this skill to a runtime directory, and do not use rm or cleanup commands in generated invocations.
If a host exposes run_script, use run_script("scripts/run_finetune.py", args=[...]); otherwise run from the repo root.
For the shortest workflow check, use --smoke; for MSD Task06 Lung Tumor reproduction, use --sanity.
Choose between the standard and --softmax workflows using the criteria below. Do not combine --softmax with --auto-seg or --sanity.
Set --mlflow-experiment-name to enable MLflow for the training phase of either workflow. --mlflow-tracking-uri and --mlflow-run-name require an experiment name. Formal pre/post evaluation does not receive MLflow credentials.
Read references/task06-and-results.md only when you need Task06 reference details, output-field definitions, or manual bundle setup notes.