""" ================================================================================ 脚本名称 (Script Name): build_hcw_semantic_patch.py 原本用途 (Original Purpose): 隐私安全的 HCW 眼底诊断侧卡（Sidecar Patch）与 Caption 生成脚本。完全剥离患者住院号、就诊日期、自由病历文本等隐私字段，只保留合规图像 ID、ICD 编码与主诊断，生成轻量化 Parquet 标签集。 适用数据集 (Target Dataset): archives/data0410/ 中的 fundus_hcw / hcw_fundus 眼底大集。 作者与归属 (Author/Provenance): 李思成 (Lisicheng), 浙江大学多模态眼科团队 输入要求 (Input): 胡成伟清洗后的原始工作簿与 release manifest 输出结果 (Output): 去隐私后的结构化诊断 Parquet 侧卡（包含 image_name, diag_name, diag_icd 等） 依赖环境 (Dependencies): pandas, pyarrow, json ================================================================================ """ """Build a privacy-safe HCW fundus diagnosis sidecar and caption patch. Only image identifiers and clinical label fields are exported. Patient, visit, date and free-report columns in the source workbooks are never copied. """ from __future__ import annotations import argparse import json import re from collections import Counter from pathlib import Path from typing import Any import pandas as pd import pyarrow.parquet as pq COHORT = "hcw_fundus" BASE_PROMPT = "ophthalmic image, color fundus photography, standard field of view, retina" SAFE_SOURCE_COLUMNS = {"image_name", "diag_name", "diag_icd", "is_primary"} def _clean(value: Any) -> str: if value is None: return "" try: if pd.isna(value): return "" except (TypeError, ValueError): pass return re.sub(r"\s+", " ", str(value)).strip(" ,，;；") def _unique(values: list[str]) -> list[str]: result: list[str] = [] seen: set[str] = set() for value in values: value = _clean(value) key = value.casefold() if value and key not in seen: result.append(value) seen.add(key) return result def _load_release(path: Path, batch_size: int) -> pd.DataFrame: parts: list[pd.DataFrame] = [] parquet = pq.ParquetFile(path) columns = ["image_id", "file_path", "cohort", "diagnosis_group_clean"] missing = set(columns) - set(parquet.schema_arrow.names) if missing: raise ValueError(f"release manifest missing columns: {sorted(missing)}") for batch in parquet.iter_batches(columns=columns, batch_size=batch_size): frame = batch.to_pandas() frame = frame[frame["cohort"].eq(COHORT)] if not frame.empty: parts.append(frame) if not parts: raise ValueError(f"release manifest contains no {COHORT} rows") release = pd.concat(parts, ignore_index=True) if len(release) != release["image_id"].nunique(): raise ValueError("release HCW image_id is not unique") release["stem"] = release["file_path"].map(lambda value: Path(str(value)).stem) if release["stem"].duplicated().any(): raise ValueError("release HCW file stems are not unique") return release def _load_workbook(path: Path, source_name: str) -> pd.DataFrame: frame = pd.read_excel(path, keep_default_na=False) missing = SAFE_SOURCE_COLUMNS - set(frame.columns) if missing: raise ValueError(f"{path} missing columns: {sorted(missing)}") safe = frame[list(SAFE_SOURCE_COLUMNS)].copy() safe["stem"] = safe["image_name"].map(lambda value: Path(str(value)).stem) safe["diag_name"] = safe["diag_name"].map(_clean) safe["diag_icd"] = safe["diag_icd"].map(_clean) safe["is_primary"] = safe["is_primary"].astype(str).str.strip().eq("是") safe["source_table"] = source_name safe = safe[safe["stem"].ne("") & safe["diag_name"].ne("")] return safe.drop(columns=["image_name"]) def _dram_labels(name: str, icd: str) -> tuple[list[str], list[str], list[str]]: text = f"{name} {icd}".casefold() families: list[str] = [] subtypes: list[str] = [] findings: list[str] = [] if "糖尿病" in text or "糖尿病视网膜" in text or "h36" in text: families.append("diabetic retinopathy") if "增殖" in text: subtypes.append("proliferative diabetic retinopathy") elif "背景" in text or "斑点" in text: subtypes.append("non-proliferative diabetic retinopathy") if "分支静脉阻塞" in text: families.append("retinal vein occlusion") subtypes.append("branch retinal vein occlusion") elif "中心性静脉阻塞" in text or "中央静脉阻塞" in text: families.append("retinal vein occlusion") subtypes.append("central retinal vein occlusion") elif "静脉阻塞" in text: families.append("retinal vein occlusion") if "老年性黄斑变性" in text: families.append("age-related macular degeneration") if "渗出性" in text: subtypes.append("neovascular age-related macular degeneration") elif "萎缩性" in text: subtypes.append("non-neovascular age-related macular degeneration") if "黄斑水肿" in text or "视网膜增厚" in text: findings.append("macular edema") if "牵拉性视网膜脱离" in text: findings.append("tractional retinal detachment") if "玻璃体出血" in text: findings.append("vitreous hemorrhage") return _unique(families), _unique(subtypes), _unique(findings) GLAUCOMA_TERMS = re.compile( r"青光眼|闭角|开角|新生血管|继发|先天|青少年|发育|正常眼压|色素|" r"睫状体炎|眼压失控|恶性|无晶状体|晶状体脱位|血影细胞" ) def _glaucoma_labels(name: str, icd: str) -> tuple[list[str], list[str], list[str]]: text = f"{name} {icd}".casefold() glaucoma_evidence = bool( GLAUCOMA_TERMS.search(text) or re.search(r"\b(?:h40|h42|q15)", text, re.I) ) if not glaucoma_evidence: return [], [], [] families = ["glaucoma"] subtypes: list[str] = [] if "新生血管" in text: subtypes.append("neovascular glaucoma") if "闭角" in text: subtypes.append("angle-closure glaucoma") if "开角" in text: subtypes.append("open-angle glaucoma") if "继发" in text: subtypes.append("secondary glaucoma") if "先天" in text or "发育" in text or "青少年" in text or icd.upper().startswith("Q15"): subtypes.append("developmental glaucoma") if "正常眼压" in text: subtypes.append("normal-tension glaucoma") if "色素" in text: subtypes.append("pigmentary glaucoma") return families, _unique(subtypes), [] def _aggregate_labels(group: pd.DataFrame) -> dict[str, Any]: families: list[str] = [] subtypes: list[str] = [] findings: list[str] = [] for row in group.itertuples(index=False): if row.source_table == "dramdrvo_cleaned.xlsx": result = _dram_labels(row.diag_name, row.diag_icd) else: result = _glaucoma_labels(row.diag_name, row.diag_icd) families.extend(result[0]) subtypes.extend(result[1]) findings.extend(result[2]) primary = group[group["is_primary"]] primary_row = primary.iloc[0] if not primary.empty else group.iloc[0] return { "source_tables": _unique(group["source_table"].tolist()), "diagnosis_raw": _unique(group["diag_name"].tolist()), "diagnosis_icd": _unique(group["diag_icd"].tolist()), "primary_diagnosis_raw": _clean(primary_row["diag_name"]), "primary_diagnosis_icd": _clean(primary_row["diag_icd"]), "diagnosis_families": _unique(families), "diagnosis_subtypes": _unique(subtypes), "finding_tags": _unique(findings), } def _prompt_rows(row: pd.Series) -> list[dict[str, str]]: families = list(row["diagnosis_families"]) subtypes = list(row["diagnosis_subtypes"]) findings = list(row["finding_tags"]) medium_parts = [BASE_PROMPT, *(f"diagnosis: {value}" for value in families)] dense_parts = [ *medium_parts, *(f"subtype: {value}" for value in subtypes), *findings, ] prompts = { "short": BASE_PROMPT, "medium": ", ".join(_unique(medium_parts)), "dense": ", ".join(_unique(dense_parts)), } image_id = str(row["image_id"]) return [ { "caption_id": f"{image_id}_{level}", "image_id": image_id, "level": level, "prompt_text": prompt, "language": "en", "generator": "ophgen_semantic_patch_hcw_v1", "grounded_in": "v10.1+hcw_cleaned_diagnosis_tables+canonical_taxonomy_v1", } for level, prompt in prompts.items() ] def build(args: argparse.Namespace) -> None: args.out.mkdir(parents=True, exist_ok=True) release = _load_release(args.release_manifest, args.batch_size) workbooks = pd.concat( [ _load_workbook(args.dramdrvo, "dramdrvo_cleaned.xlsx"), _load_workbook(args.glaucoma, "glaucoma_cleaned.xlsx"), ], ignore_index=True, ) release_stems = set(release["stem"]) workbooks = workbooks[workbooks["stem"].isin(release_stems)].copy() workbook_stems = set(workbooks["stem"]) missing_stems = release_stems - workbook_stems if missing_stems: raise ValueError(f"{len(missing_stems)} release HCW images lack a cleaned diagnosis join") records: list[dict[str, Any]] = [] for stem, group in workbooks.groupby("stem", sort=False): records.append({"stem": stem, **_aggregate_labels(group)}) semantics = release.merge( pd.DataFrame(records), on="stem", how="left", validate="one_to_one" ) semantics["prompt_eligible"] = semantics["diagnosis_families"].map(bool) eligible = semantics[semantics["prompt_eligible"]].copy() if eligible.empty: raise ValueError("no HCW images have prompt-eligible canonical diagnoses") safe_columns = [ "image_id", "cohort", "source_tables", "diagnosis_raw", "diagnosis_icd", "primary_diagnosis_raw", "primary_diagnosis_icd", "diagnosis_families", "diagnosis_subtypes", "finding_tags", "diagnosis_group_clean", "prompt_eligible", ] semantics[safe_columns].to_parquet(args.out / "hcw_image_semantics.parquet", index=False) caption_rows: list[dict[str, str]] = [] for _, row in eligible.iterrows(): caption_rows.extend(_prompt_rows(row)) captions = pd.DataFrame(caption_rows) captions.to_parquet(args.out / "hcw_caption_patch.parquet", index=False) family_counts = Counter( family for families in eligible["diagnosis_families"] for family in families ) subtype_counts = Counter( subtype for subtypes in eligible["diagnosis_subtypes"] for subtype in subtypes ) summary_rows = [ {"label_type": "family", "label": label, "images": count} for label, count in family_counts.items() ] + [ {"label_type": "subtype", "label": label, "images": count} for label, count in subtype_counts.items() ] pd.DataFrame(summary_rows).sort_values( ["label_type", "images", "label"], ascending=[True, False, True] ).to_csv(args.out / "hcw_class_summary.csv", index=False) source_stems = { source: int(workbooks.loc[workbooks["source_table"].eq(source), "stem"].nunique()) for source in sorted(workbooks["source_table"].unique()) } level_counts = captions["level"].value_counts().to_dict() validation = { "source_read_only": True, "release_hcw_images": int(len(release)), "release_hcw_unique_stems": int(release["stem"].nunique()), "cleaned_workbook_joined_stems": source_stems, "cleaned_workbook_union_stems": int(workbooks["stem"].nunique()), "cleaned_workbook_overlap_stems": int( workbooks.groupby("stem")["source_table"].nunique().gt(1).sum() ), "prompt_eligible_images": int(len(eligible)), "prompt_ineligible_images": int(len(semantics) - len(eligible)), "current_diagnosis_nonempty": int( semantics["diagnosis_group_clean"].fillna("").astype(str).str.strip().ne("").sum() ), "patched_caption_rows": int(len(captions)), "patched_caption_level_counts": {key: int(value) for key, value in level_counts.items()}, "output_columns": safe_columns, "pii_columns_exported": False, "output_type": "privacy-safe sidecar and caption delta; source files were not modified", } (args.out / "validation.json").write_text( json.dumps(validation, ensure_ascii=False, indent=2) + "\n", encoding="utf-8" ) print(json.dumps(validation, ensure_ascii=False, indent=2)) def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser() parser.add_argument("--release-manifest", type=Path, required=True) parser.add_argument("--dramdrvo", type=Path, required=True) parser.add_argument("--glaucoma", type=Path, required=True) parser.add_argument("--out", type=Path, required=True) parser.add_argument("--batch-size", type=int, default=250_000) return parser.parse_args() if __name__ == "__main__": build(parse_args())