Furen Xiao
77adc2b3af
Introduce the third segmentation pipeline using MONAI (1.6.x) to allow direct comparison with Pipelines A and B. This includes the implementation of the iterative pseudo-labeling workflow, training scripts, and inference protocols. - Add `scripts_monai/` directory containing the MONAI pipeline scripts. - Update documentation in `README.md` and `AGENTS.md` to include MONAI package requirements and pipeline details. - Configure `.gitignore` to exclude MONAI-specific run directories. - Update data directory descriptions to include MONAI pseudo-labels.
64 lines
No EOL
2.7 KiB
Python
64 lines
No EOL
2.7 KiB
Python
"""Build the training manifest for one pseudo-labeling round.
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base = labeled patient-level train split (w=1.0) — the same train/val usage as
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Pipeline A: the val split is never trained on (it is the internal-validation
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set). Accepted pseudo-label rows (pos + neg) are appended with w=<pseudo-weight>
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(negative rows point at the zero mask written by stage 03), de-duplicated by
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key (first occurrence wins). Every row must point at existing pimg/plabel
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niftis.
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Usage:
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python scripts_monai/01_monai_build_rows.py \
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--train data/manifests/split_train.jsonl \
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--accepted data/pseudo_monai/round1/accepted.jsonl \
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--pseudo-weight 0.3 --out data/pseudo_monai/round1_rows.jsonl
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"""
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import argparse
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import os
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import sys
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from src.common import load_jsonl, save_jsonl
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--train", required=True, help="labeled train split jsonl ({key,pimg,plabel})")
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ap.add_argument("--accepted", action="append", default=None,
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help="accepted pseudo jsonl (repeatable); pos + neg rows")
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ap.add_argument("--pseudo-weight", type=float, default=0.3)
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ap.add_argument("--no-neg", action="store_true", help="drop negative pseudo rows")
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ap.add_argument("--out", required=True)
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args = ap.parse_args()
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rows, seen = [], set()
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for r in load_jsonl(args.train):
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rows.append({"key": r["key"], "subject": r.get("subject"), "pimg": r["pimg"],
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"plabel": r["plabel"], "w": 1.0, "role": "labeled"})
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seen.add(r["key"])
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n_pos, n_neg = 0, 0
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for f in (args.accepted or []):
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for r in load_jsonl(f):
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if not r.get("label") or r["key"] in seen:
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continue
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if r["role"] == "neg" and args.no_neg:
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continue
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seen.add(r["key"])
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rows.append({"key": r["key"], "subject": r.get("subject"), "pimg": r["pimg"],
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"plabel": r["label"], "w": args.pseudo_weight, "role": r["role"]})
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if r["role"] == "pos":
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n_pos += 1
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else:
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n_neg += 1
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missing = [r["key"] for r in rows
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if not (os.path.exists(r["pimg"]) and os.path.exists(r["plabel"]))]
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if missing:
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raise FileNotFoundError(f"{len(missing)} rows missing pimg/plabel, e.g. {missing[:3]}")
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save_jsonl(rows, args.out)
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n_labeled = len(rows) - n_pos - n_neg
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print(f"[monai:rows] {args.out}: total={len(rows)} labeled={n_labeled} pos={n_pos} neg={n_neg}",
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flush=True)
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if __name__ == "__main__":
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main() |