File size: 5,669 Bytes
79017c8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 | #!/usr/bin/env python3
"""
caption_v2.py — disease/lesion-centric caption generator (caption v2).
A PURE FUNCTION of a manifest row -> specificity-tiered caption rows.
Used to REGENERATE captions from existing *_images_v1.parquet WITHOUT
re-running extraction (same idea as regen_oct_captions.py), so it works
uniformly for the private, public-fundus and public-OCT cohorts.
Schema (EyeDiff-aligned): {modality}, {region}, {diagnosis[+severity]}, {lesions...}
Tiers (Brack et al. 2025 — vary SPECIFICITY, not wording):
short : {modality}, {diagnosis} (no region / severity / lesion)
medium : {modality}, {region}, {diagnosis+severity}
dense : {modality}, {region}, {diagnosis+severity}, {lesions}
Identical tiers are de-duplicated (a normal, lesion-free image collapses to one
short caption). The ~10% empty-caption dropout for classifier-free guidance is a
TRAINING-loader concern and is intentionally NOT stored as data rows.
Acquisition metadata (device, dataset name, quality score, slice index, bbox,
exact µm thickness, eye laterality) stays in the image parquet — it is
deliberately kept OUT of the prompt text.
Region rule: emitted for OCT / SLO (macula vs optic disc matters); omitted for
color fundus (inherently posterior-pole, EyeDiff-style "color fundus, <disease>").
"""
import disease_dict as dd
CAP_COLS = ["caption_id", "image_id", "level", "prompt_text",
"language", "generator", "grounded_in"]
def _get(row, k, default=None):
if hasattr(row, "get"):
return row.get(k, default)
return getattr(row, k, default)
def _as_list(x):
if x is None:
return []
if isinstance(x, (list, tuple)):
return list(x)
if hasattr(x, "tolist"):
try:
return list(x.tolist())
except Exception:
pass
return [x]
def caption_rows_for(row):
"""row: dict-like with modality, anatomy, diagnosis_group, severity,
lesion_tags, image_id. Returns a list of caption-row dicts (CAP_COLS)."""
image_id = _get(row, "image_id")
mod = _get(row, "modality")
anat = _get(row, "anatomy") or "macula"
sev = _get(row, "severity")
raw_dx = _as_list(_get(row, "diagnosis_group"))
raw_les = _as_list(_get(row, "lesion_tags"))
# Normalize old-mapping diagnosis tokens through the v2 dictionary
# (CNV -> nAMD + lesion CNV; DRUSEN -> AMD + lesion drusen; CSR -> CSC; ...)
codes, lesions = [], []
for t in raw_dx:
c, _s, l = dd.normalize_diagnosis(t)
codes += c
lesions += l
for lt in raw_les:
cc = dd.normalize_lesion(lt)
if cc:
lesions.append(cc)
codes = list(dict.fromkeys(codes)) # dedup, keep order
lesions = list(dict.fromkeys(lesions))
# CONFIRMED normal only when there is no diagnosis AND severity says 'none'.
is_normal = (len(raw_dx) == 0 and str(sev) == "none")
mod_s = dd.modality_phrase(mod)
region_s = dd.anatomy_phrase(anat) if mod in ("oct_bscan", "slo_gray") else None
dx_short = dd.compose_disease_segment(codes, None, [],
include_lesions=False, is_normal=is_normal)
dx_med = dd.compose_disease_segment(codes, sev, lesions,
include_lesions=False, is_normal=is_normal)
dx_dense = dd.compose_disease_segment(codes, sev, lesions,
include_lesions=True, is_normal=is_normal)
def join(parts):
return ", ".join([p for p in parts if p])
tiers = [
("short", join([mod_s] + dx_short)),
("medium", join([mod_s, region_s] + dx_med)),
("dense", join([mod_s, region_s] + dx_dense)),
]
out, seen = [], set()
for lvl, txt in tiers: # first (least specific) wins on dup
if not txt or txt in seen:
continue
seen.add(txt)
out.append({"caption_id": f"{image_id}_{lvl}", "image_id": image_id,
"level": lvl, "prompt_text": txt, "language": "en",
"generator": "caption_v2", "grounded_in": "manifest_fields"})
return out
# --------------------------------------------------------------------------- #
# preview: sample real manifest rows and print v2 captions (no writes) #
# --------------------------------------------------------------------------- #
if __name__ == "__main__":
import argparse
import pandas as pd
ap = argparse.ArgumentParser()
ap.add_argument("--manifest", required=True)
ap.add_argument("--n-per-group", type=int, default=2)
ap.add_argument("--group-col", default="cohort")
args = ap.parse_args()
df = pd.read_parquet(args.manifest)
print(f"loaded {len(df)} rows from {args.manifest}\n")
# show a spread: a few rows per (cohort) and ensure disease variety
shown = 0
for _, g in df.groupby(args.group_col):
# prefer rows with a diagnosis, fall back to any
nz = g[g["diagnosis_group"].apply(lambda x: x is not None and len(_as_list(x)) > 0)]
pick = pd.concat([nz.head(args.n_per_group), g.head(1)]).drop_duplicates("image_id")
for _, r in pick.head(args.n_per_group).iterrows():
dxs = _as_list(r.get("diagnosis_group"))
print(f"[{r.get('cohort')}] modality={r.get('modality')} anat={r.get('anatomy')} "
f"dx={dxs} sev={r.get('severity')} les={_as_list(r.get('lesion_tags'))}")
for c in caption_rows_for(r):
print(f" {c['level']:6s}| {c['prompt_text']}")
shown += 1
print()
print(f"shown {shown} sample images")
|