| """Build prompt QC samples for the de-identified new-data manifest.""" |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import html |
| import io |
| import math |
| import re |
| import tarfile |
| from pathlib import Path |
| from typing import Any |
|
|
| import pandas as pd |
| from PIL import Image, ImageOps |
|
|
|
|
| DEFAULT_MANIFEST = Path( |
| "/data/team/lisicheng/new_data_manifest/" |
| "new_data_manifest_dedup_split_labeled_with_wds_deid.parquet" |
| ) |
| DEFAULT_OUT = Path("/data/team/lisicheng/new_data_manifest/prompt_qc") |
|
|
| MODALITY_TEXT = { |
| "fundus_color": "color fundus photograph", |
| "oct_bscan": "OCT B-scan", |
| "octa": "optical coherence tomography angiography image", |
| "ffa": "fluorescein fundus angiography image", |
| "bscan_us": "ocular B-scan ultrasound image", |
| "ubm": "ultrasound biomicroscopy image", |
| "uwf": "ultra-widefield fundus photograph", |
| } |
|
|
| BANNED_PROMPT_TERMS = { |
| "tongren", |
| "xiangya", |
| "yinhai", |
| "chengdu", |
| "eryuan", |
| "handan", |
| "data0410", |
| "hcw", |
| "fq_unlabeled", |
| "mixed_unlabeled", |
| "octbuckets", |
| "train", |
| "test", |
| "internal_test", |
| "external_test", |
| "unlabeled", |
| } |
|
|
| DIAG_MAP = { |
| "amd": "age-related macular degeneration", |
| "年龄相关性黄斑变性": "age-related macular degeneration", |
| "老年性黄斑变性": "age-related macular degeneration", |
| "dr": "diabetic retinopathy", |
| "糖尿病视网膜病变": "diabetic retinopathy", |
| "糖网": "diabetic retinopathy", |
| "normal": "normal", |
| "正常": "normal", |
| "rvo": "retinal vein occlusion", |
| "视网膜静脉阻塞": "retinal vein occlusion", |
| "csc": "central serous chorioretinopathy", |
| "中心性浆液性脉络膜视网膜病变": "central serous chorioretinopathy", |
| "中浆": "central serous chorioretinopathy", |
| "erm": "epiretinal membrane", |
| "黄斑前膜": "epiretinal membrane", |
| "mh": "macular hole", |
| "黄斑裂孔": "macular hole", |
| "rao": "retinal artery occlusion", |
| "视网膜动脉阻塞": "retinal artery occlusion", |
| "rd": "retinal detachment", |
| "视网膜脱离": "retinal detachment", |
| "vkh": "Vogt-Koyanagi-Harada disease", |
| "葡萄膜炎": "uveitis", |
| "青光眼": "glaucoma", |
| "白内障": "cataract", |
| } |
|
|
| FINDING_RULES = [ |
| (re.compile(r"玻璃体混浊|中低回声光点|中低回声光斑"), "vitreous opacity"), |
| (re.compile(r"玻璃体积血"), "vitreous hemorrhage"), |
| (re.compile(r"玻璃体后脱离"), "posterior vitreous detachment"), |
| (re.compile(r"后巩膜葡萄肿"), "posterior staphyloma"), |
| (re.compile(r"视网膜脱离"), "retinal detachment"), |
| (re.compile(r"视网膜裂孔"), "retinal tear"), |
| (re.compile(r"眼内占位|占位性病变"), "intraocular mass"), |
| (re.compile(r"脉络膜黑色素瘤"), "choroidal melanoma"), |
| (re.compile(r"硅油眼"), "silicone oil eye"), |
| (re.compile(r"黄斑囊样水肿|黄斑水肿|囊样水肿"), "macular edema"), |
| (re.compile(r"黄斑前膜"), "epiretinal membrane"), |
| (re.compile(r"黄斑裂孔"), "macular hole"), |
| (re.compile(r"分支静脉阻塞"), "branch retinal vein occlusion"), |
| (re.compile(r"视网膜静脉阻塞|静脉阻塞"), "retinal vein occlusion"), |
| (re.compile(r"视网膜动脉阻塞|动脉阻塞"), "retinal artery occlusion"), |
| (re.compile(r"糖尿病视网膜病变|糖网"), "diabetic retinopathy"), |
| (re.compile(r"高血压视网膜病变"), "hypertensive retinopathy"), |
| (re.compile(r"新生血管|CNV", re.I), "neovascularization"), |
| (re.compile(r"荧光渗漏|渗漏"), "fluorescein leakage"), |
| (re.compile(r"无灌注"), "capillary nonperfusion"), |
| (re.compile(r"微动脉瘤"), "microaneurysms"), |
| (re.compile(r"硬性渗出|硬渗"), "hard exudates"), |
| (re.compile(r"棉絮斑"), "cotton-wool spots"), |
| (re.compile(r"出血"), "retinal hemorrhage"), |
| (re.compile(r"视盘.*高荧光|视盘.*渗漏"), "optic disc leakage"), |
| (re.compile(r"房角开放"), "open anterior chamber angle"), |
| (re.compile(r"房角偏窄|窄房角|房角狭窄"), "narrow anterior chamber angle"), |
| (re.compile(r"虹膜平坦"), "flat iris configuration"), |
| (re.compile(r"睫状体.*囊|囊样暗区|囊样无回声区"), "ciliary body cyst"), |
| (re.compile(r"晶体内回声增强"), "lens opacity"), |
| (re.compile(r"前房.*浅"), "shallow anterior chamber"), |
| ] |
|
|
| PII_PATTERNS = [ |
| re.compile(r"(姓名|患者姓名|病人姓名|name)\s*[:：]?\s*[\u4e00-\u9fffA-Za-z·]{1,8}", re.I), |
| re.compile(r"(ID号|\bID\b|编号|住院号|门诊号|检查号|patient\s*id)\s*[:：]?\s*[A-Za-z0-9_-]+", re.I), |
| re.compile(r"(检查日期|出生日期|DOB)\s*[:：]?\s*\d{4}[-/.年]\d{1,2}[-/.月]\d{1,2}(?:日)?", re.I), |
| re.compile(r"\b\d{4}[-/.年]\d{1,2}[-/.月]\d{1,2}(?:日)?(?:\s+\d{1,2}:\d{2}(?::\d{2})?)?\b"), |
| re.compile(r"\b\d{8,}\b"), |
| ] |
|
|
|
|
| def clean_text(text: Any) -> str: |
| if text is None: |
| return "" |
| if isinstance(text, float) and math.isnan(text): |
| return "" |
| out = str(text) |
| for pattern in PII_PATTERNS: |
| out = pattern.sub("", out) |
| out = re.sub(r"(医师签名|医生签名|签名|建议门诊|门诊时间).*", "", out, flags=re.I) |
| out = re.sub(r"(待删|删除)", "", out) |
| out = re.sub(r"\s+", " ", out) |
| out = re.sub(r"[，。；;,\s]+$", "", out) |
| return out.strip(" ，。；;,") |
|
|
|
|
| def build_prompts(row: dict[str, Any]) -> dict[str, str]: |
| modality = str(row.get("modality") or "") |
| base = base_prompt(row, modality) |
| descriptors = _clinical_descriptors(row) |
| demographics = _demographics(row) |
|
|
| short_parts = [base] |
| medium_parts = [base] + demographics + descriptors[:3] |
| dense_parts = [base] + demographics + descriptors[:8] |
|
|
| return { |
| "short": _join(short_parts), |
| "medium": _join(medium_parts), |
| "dense": _join(dense_parts), |
| } |
|
|
|
|
| def base_prompt(row: dict[str, Any], modality: str) -> str: |
| if str(row.get("cohort") or "") == "tongren_external_fundus": |
| return "anterior segment photograph" |
| return MODALITY_TEXT.get(modality, f"{modality.replace('_', ' ')} ophthalmic image") |
|
|
|
|
| def main() -> None: |
| args = parse_args() |
| args.out.mkdir(parents=True, exist_ok=True) |
| (args.out / "imgs").mkdir(exist_ok=True) |
|
|
| df = pd.read_parquet(args.manifest) |
| df = df[df.get("split", "train") == "train"].copy() if "split" in df.columns else df.copy() |
| for col in ["center", "modality"]: |
| if col not in df.columns: |
| raise KeyError(f"manifest missing {col!r}") |
|
|
| samples = sample_by_center_modality(df, args.per_group, args.seed) |
| records = [] |
| caption_rows = [] |
| for idx, row in enumerate(samples.to_dict("records")): |
| prompts = build_prompts(row) |
| for level, prompt in prompts.items(): |
| caption_rows.append( |
| {"image_id": row["image_id"], "level": level, "prompt_text": prompt} |
| ) |
| image_rel = render_thumb(row, args.out / "imgs", idx, args.thumb) |
| record = { |
| "idx": idx, |
| "image_rel": image_rel, |
| "prompts": prompts, |
| **{k: row.get(k) for k in [ |
| "image_id", |
| "center", |
| "modality", |
| "cohort", |
| "diagnosis_raw", |
| "group_raw", |
| "diagnosis_group", |
| "caption", |
| "train_path", |
| "pii_masked", |
| "ocr_age", |
| "ocr_sex", |
| "ocr_eye", |
| ]}, |
| } |
| records.append(record) |
|
|
| caps = pd.DataFrame(caption_rows) |
| caps.to_parquet(args.out / "prompt_qc_captions.parquet", index=False) |
| caps.to_csv(args.out / "prompt_qc_captions.csv", index=False) |
| write_html(records, args.out / "prompt_qc.html") |
| write_design(args.out / "prompt_design.md") |
| print(f"DONE -> {args.out / 'prompt_qc.html'} ({len(records)} samples)") |
|
|
|
|
| def sample_by_center_modality(df: pd.DataFrame, n: int, seed: int) -> pd.DataFrame: |
| groups = [] |
| for (_, _), group in df.groupby(["center", "modality"], dropna=False): |
| groups.append(group.sample(n=min(n, len(group)), random_state=seed)) |
| out = pd.concat(groups, ignore_index=True) |
| return out.sort_values(["modality", "center", "cohort", "image_id"]).reset_index(drop=True) |
|
|
|
|
| def render_thumb(row: dict[str, Any], img_dir: Path, idx: int, size: int) -> str: |
| path = str(row.get("train_path") or row.get("file_path")) |
| name = f"{idx:04d}_{safe_name(row.get('modality'))}_{safe_name(row.get('center'))}.jpg" |
| out = img_dir / name |
| try: |
| if "::" in path: |
| im = open_wds_member(path).convert("RGB") |
| else: |
| im = Image.open(path).convert("RGB") |
| im.thumbnail((size, size), Image.Resampling.LANCZOS) |
| canvas = Image.new("RGB", (size, size), (0, 0, 0)) |
| canvas.paste(im, ((size - im.width) // 2, (size - im.height) // 2)) |
| im = canvas |
| im.save(out, quality=90) |
| except Exception: |
| im = Image.new("RGB", (size, size), (80, 20, 20)) |
| im = ImageOps.expand(im, border=2, fill=(255, 0, 0)) |
| im.save(out, quality=90) |
| return f"imgs/{name}" |
|
|
|
|
| def open_wds_member(path: str) -> Image.Image: |
| tar_path, member_name = path.split("::", 1) |
| with tarfile.open(tar_path, "r:*") as tf: |
| member = tf.getmember(member_name) |
| fp = tf.extractfile(member) |
| if fp is None: |
| raise FileNotFoundError(path) |
| data = fp.read() |
| return Image.open(io.BytesIO(data)) |
|
|
|
|
| def write_html(records: list[dict[str, Any]], out: Path) -> None: |
| css = """ |
| body{font-family:Arial,sans-serif;margin:20px;background:#f7f7f7;color:#222} |
| .grid{display:grid;grid-template-columns:repeat(auto-fill,minmax(460px,1fr));gap:14px} |
| .card{background:white;border:1px solid #ddd;border-radius:6px;padding:12px} |
| img{width:180px;height:180px;object-fit:contain;background:#000;float:left;margin-right:12px} |
| .meta{font-size:12px;color:#555;line-height:1.35} |
| .prompt{font-size:13px;margin:6px 0}.level{font-weight:bold;color:#333} |
| .caption{clear:both;font-size:12px;color:#555;border-top:1px solid #eee;margin-top:10px;padding-top:8px} |
| h1{margin-bottom:4px}.note{color:#555;margin-bottom:18px} |
| """ |
| parts = [ |
| "<!doctype html><html><head><meta charset='utf-8'>", |
| f"<style>{css}</style></head><body>", |
| "<h1>New-data prompt QC</h1>", |
| "<div class='note'>Grouped by center × modality, sampled 5 rows per group. " |
| "Prompts intentionally exclude center/cohort names and raw identifiers.</div>", |
| "<div class='grid'>", |
| ] |
| for r in records: |
| parts.append("<div class='card'>") |
| parts.append(f"<img src='{html.escape(r['image_rel'])}'>") |
| parts.append("<div class='meta'>") |
| for k in ["image_id", "modality", "center", "cohort", "pii_masked", "ocr_age", "ocr_sex", "ocr_eye"]: |
| parts.append(f"<b>{k}</b>: {html.escape(str(r.get(k)))}<br>") |
| parts.append(f"<b>train_path</b>: {html.escape(str(r.get('train_path'))[:180])}</div>") |
| for level in ["short", "medium", "dense"]: |
| parts.append( |
| f"<div class='prompt'><span class='level'>{level}</span>: " |
| f"{html.escape(r['prompts'][level])}</div>" |
| ) |
| parts.append("<div class='caption'>") |
| for k in ["diagnosis_raw", "group_raw", "diagnosis_group", "caption"]: |
| v = r.get(k) |
| if v is not None and not (isinstance(v, float) and math.isnan(v)): |
| parts.append(f"<b>{k}</b>: {html.escape(str(v)[:400])}<br>") |
| parts.append("</div></div>") |
| parts.append("</div></body></html>") |
| out.write_text("\n".join(parts), encoding="utf-8") |
|
|
|
|
| def write_design(out: Path) -> None: |
| text = """# Prompt design logic |
| |
| The training prompt space follows the previous private-data SD3 logic: each image has |
| multiple candidate prompts (`short`, `medium`, `dense`), and training samples one |
| prompt level at random per step. |
| |
| Prompt levels: |
| - `short`: modality phrase only, matching the old private-data minimal prompt style. |
| - `medium`: modality phrase plus age/sex/eye if available and up to three English labels. |
| - `dense`: modality phrase plus age/sex/eye if available and up to eight English labels. |
| |
| Modality phrases: |
| - `fundus_color`: color fundus photograph |
| - `tongren_external_fundus`: anterior segment photograph, overriding the |
| `fundus_color` modality phrase because these images are external/anterior-eye photos. |
| - `uwf`: ultra-widefield fundus photograph |
| - `ffa`: fluorescein fundus angiography image |
| - `octa`: optical coherence tomography angiography image |
| - `bscan_us`: ocular B-scan ultrasound image |
| - `ubm`: ultrasound biomicroscopy image |
| |
| Safety rules: |
| - Never include center, hospital, cohort, dataset name, file name, ID, exact date, DOB, or raw OCR text. |
| - Store only non-identifying OCR-derived attributes: age, sex, eye laterality. |
| - Rows without labels still receive a modality-only prompt. |
| - Long free-text reports are never copied into prompts; they are reduced to English finding labels. |
| """ |
| out.write_text(text, encoding="utf-8") |
|
|
|
|
| def _clinical_descriptors(row: dict[str, Any]) -> list[str]: |
| desc: list[str] = [] |
| for key in ["diagnosis_raw", "diagnosis_group", "group_raw"]: |
| val = clean_text(row.get(key)) |
| val = translate_diag(val) |
| candidates = [val] if _is_prompt_ready_label(val) else extract_finding_tags(val) |
| _extend_unique(desc, candidates) |
| cap = clean_text(row.get("caption")) |
| _extend_unique(desc, extract_finding_tags(cap)) |
| return desc |
|
|
|
|
| def extract_finding_tags(text: str) -> list[str]: |
| if not text: |
| return [] |
| tags = [label for pattern, label in FINDING_RULES if pattern.search(text)] |
| return _unique(tags) |
|
|
|
|
| def _extend_unique(out: list[str], values: list[str]) -> None: |
| seen = {v.lower() for v in out} |
| for value in values: |
| if _allowed_descriptor(value) and value.lower() not in seen: |
| out.append(value) |
| seen.add(value.lower()) |
|
|
|
|
| def _unique(values: list[str]) -> list[str]: |
| out = [] |
| seen = set() |
| for value in values: |
| key = value.lower() |
| if key not in seen: |
| out.append(value) |
| seen.add(key) |
| return out |
|
|
|
|
| def _is_prompt_ready_label(text: str) -> bool: |
| if not _allowed_descriptor(text): |
| return False |
| if re.search(r"[\u4e00-\u9fff]", text): |
| return False |
| return _is_concise_label(text) |
|
|
|
|
| def _allowed_descriptor(text: str) -> bool: |
| if not text: |
| return False |
| lowered = text.lower().strip() |
| if lowered in BANNED_PROMPT_TERMS: |
| return False |
| if "_" in text or re.match(r"^group\d+\b", lowered): |
| return False |
| return not any(term in lowered for term in BANNED_PROMPT_TERMS) |
|
|
|
|
| def _is_concise_label(text: str) -> bool: |
| if len(text) > 80: |
| return False |
| return not re.search(r"[。；;]|提示|所见|检查", text) |
|
|
|
|
| def _clip_text(text: str, max_chars: int) -> str: |
| if len(text) <= max_chars: |
| return text |
| return text[: max_chars - 3].rstrip(" ，。；;,") + "..." |
|
|
|
|
| def _demographics(row: dict[str, Any]) -> list[str]: |
| out: list[str] = [] |
| age = row.get("ocr_age") |
| if age is not None and not (isinstance(age, float) and math.isnan(age)): |
| bucket = age_bucket(age) |
| if bucket: |
| out.append(bucket) |
| sex = str(row.get("ocr_sex") or "").upper() |
| if sex == "M": |
| out.append("male") |
| elif sex == "F": |
| out.append("female") |
| eye = str(row.get("ocr_eye") or "").upper() |
| if eye == "OD": |
| out.append("right eye") |
| elif eye == "OS": |
| out.append("left eye") |
| elif eye == "OU": |
| out.append("both eyes") |
| if len(out) >= 2 and out[1] in {"male", "female"}: |
| out = [f"{out[1]} patient {out[0]}"] + out[2:] |
| return out |
|
|
|
|
| def age_bucket(age: Any) -> str: |
| try: |
| value = int(age) |
| except (TypeError, ValueError): |
| return "" |
| if value < 0 or value > 120: |
| return "" |
| if value < 2: |
| return "infant" |
| if value < 13: |
| return "child" |
| if value < 20: |
| return "teenage patient" |
| decade = min((value // 10) * 10, 90) |
| return f"in their {decade}s" |
|
|
|
|
| def _join(parts: list[str]) -> str: |
| clean = [p for p in (clean_text(part) for part in parts) if p] |
| return ", ".join(clean) |
|
|
|
|
| def translate_diag(text: str) -> str: |
| if not text: |
| return "" |
| key = text.strip().lower() |
| return DIAG_MAP.get(key, text.strip()) |
|
|
|
|
| def safe_name(value: Any) -> str: |
| return re.sub(r"[^A-Za-z0-9_.-]+", "_", str(value or "na"))[:80] |
|
|
|
|
| def parse_args() -> argparse.Namespace: |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--manifest", type=Path, default=DEFAULT_MANIFEST) |
| parser.add_argument("--out", type=Path, default=DEFAULT_OUT) |
| parser.add_argument("--per-group", type=int, default=5) |
| parser.add_argument("--seed", type=int, default=20260701) |
| parser.add_argument("--thumb", type=int, default=320) |
| return parser.parse_args() |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
