Datasets:

Kaphathy
/

Dataset

"""Verified cohort registry for the h100 cross-center new-data consolidation (2026-06-30).
Single source of truth, encoding the 11-agent read-only recon (see memory reference_h100_newdata).
HARD RULES baked in: originals on /nfs01 are READ-ONLY and NEVER modified/deleted. Archives
extract ONLY to EXTRACT_ROOT (work dir on /data). Already-extracted dirs are read in place.
Dedup/skip is done at MANIFEST level (drop rows), never by touching files.
Each cohort dict:
  key        unique cohort id (also the manifest `cohort` namespace)
  modality   fundus_color | octa | ffa | bscan_us | ubm | uwf
  center     hospital/source (for multi-center splits)
  src        path to the extracted dir OR the archive
  comp       none | zip | targz | rar5 | 7z   (archive type if not extracted)
  extracted  True  -> read `src` in place (0 disk);  False -> extract to EXTRACT_ROOT/key
  extract_glob  optional member filter for selective extraction (e.g. images only)
  exts       image extensions to enumerate
  label_kind folder | json | jsonl | xlsx | none
  label_path label file (abs), or None  (folder labels come from dir names)
  pii        none | low | t1   (t1 = must strip identifiers before any export; see register)
  dedup_group  cohorts sharing a group are cross-checked for duplicates
  role       train | external_test         (external_test = held out, never trained)
  note
"""
import os

EXTRACT_ROOT = "/data/team/lisicheng/new_data_extracted"   # all extraction lands here (/data, 2.9T free); /nfs01 is read-only
WORK = "/data/team/lisicheng"
NFS_BACKUP = "/nfs01/datasetsBackup"
NFS_FQ = "/nfs01/FQ_Datasets/data"
DATASETS = "/data/datasets"
UNRAR = "/data/team/lisicheng/miniconda3/envs/ocr/bin/unrar"   # not on PATH; full path

IMG_EXTS = (".png", ".jpg", ".jpeg", ".tif", ".tiff", ".bmp")


def read_path(c):
    """Where images are actually read from."""
    return c["src"] if c.get("extracted") else os.path.join(EXTRACT_ROOT, c["key"])


# ----------------------------------------------------------------------------- COHORTS
COHORTS = [
    # ===================== FUNDUS (color fundus photography) =====================
    dict(key="fundus90wer", modality="fundus_color", center="mixed_unlabeled",
         src=f"{DATASETS}/fundus90wER", comp="none", extracted=True, exts=(".png", ".jpg"),
         label_kind="none", label_path=None, pii="t1", dedup_group="fundus_pool", role="train",
         note="899,266 (verified). Chinese names in filenames -> t1 (filename PII only, not in pixels). md5 in cleaning_report usable as dedup key."),
    dict(key="fq_rawfundus", modality="fundus_color", center="fq_unlabeled",
         src=f"{NFS_FQ}/rawFundus", comp="none", extracted=True, exts=(".png",),
         label_kind="none", label_path=None, pii="low", dedup_group="fq_tongren_idname", role="train",
         note="208,138 (verified). <id>-<n>.png. SKIP rawFundus_predicted (same imgs, quality-resorted)."),
    dict(key="fq_testfundus", modality="fundus_color", center="fq_unlabeled",
         src=f"{NFS_FQ}/testFundus", comp="none", extracted=True, exts=(".png",),
         label_kind="none", label_path=None, pii="low", dedup_group="fq_tongren_idname", role="train",
         note="52,034 (verified). SKIP testFundus_predicted."),
    dict(key="fq_validfundus", modality="fundus_color", center="fq_unlabeled",
         src=f"{NFS_FQ}/validFundus", comp="none", extracted=True, exts=(".png",),
         label_kind="folder", label_path=None, pii="low", dedup_group="fq_tongren_idname", role="train",
         note="4,000 (verified). quality=gradable (whole-dir tag)."),
    dict(key="eryuan_t17_fundus", modality="fundus_color", center="eryuan",
         src=f"{NFS_FQ}/eryuanFundusT17RDCls", comp="none", extracted=True, exts=(".tif",),
         label_kind="folder", label_path=None, pii="low", dedup_group="eryuan_pool", role="train",
         note="31,621 (verified). 17 disease classes; has train/val/test -> reserve its test split as INTERNAL test. class = immediate parent dir."),
    dict(key="tongren_fundus", modality="fundus_color", center="tongren",
         src=f"{WORK}/Dataser/shards_tongren", comp="none", extracted=True, exts=(".png",),
         label_kind="json", label_path=None, pii="low", dedup_group="tongren_pool", role="train",
         note="~628,392 WebDataset shards (de-id product, sex/age in per-image json). USE THIS, skip 同仁/fundus.zip. WebDataset -> needs wds reader (see build_manifest stub)."),
    dict(key="tongren_external_fundus", modality="fundus_color", center="tongren",
         src=f"{NFS_BACKUP}/同仁/external.zip", comp="zip", extracted=False, exts=(".png",),
         label_kind="none", label_path=None, pii="low", dedup_group="tongren_pool", role="train",
         note="209,091 (verified). 99.5% SAME PATIENTS as tongren_fundus but 0 same-file -> OK as extra TRAIN, NEVER as eval (leakage)."),
    dict(key="tongren_95disease_fundus", modality="fundus_color", center="tongren",
         src=f"{NFS_BACKUP}/tr/tr1695L2/tongren16G95DFundus", comp="none", extracted=True, exts=(".png",),
         label_kind="folder", label_path=None, pii="low", dedup_group="tongren_pool", role="train",
         note="264,412 (verified). 16 groups / ~95 disease subclasses (folder). GOLDMINE for downstream. dedup vs tongren_pool by 编号. SKIP the tr .7z (extracted copy already here). class=parent, group=grandparent."),
    dict(key="eryuan_fundus", modality="fundus_color", center="eryuan",
         src=f"{NFS_BACKUP}/data0410/eryuan.zip", comp="zip", extracted=False, exts=(".tif", ".png"),
         label_kind="folder", label_path=None, pii="low", dedup_group="eryuan_pool", role="train",
         note="19,599 (verified): 4 classes AMD/DR/Normal/RVO (NOT 17 as md said). SKIP eryuan_split (subset). dedup vs data0410_master."),
    dict(key="dual_comorbidity_fundus", modality="fundus_color", center="data0410",
         src=f"{NFS_BACKUP}/data0410/Dual_comornidity.tar.gz", comp="targz", extracted=False, exts=(".png",),
         label_kind="folder", label_path=None, pii="low", dedup_group="data0410_pool", role="train",
         note="~12k unique (24,120 entries = 6-class scheme + 4-class scheme of SAME pool). Keep ONE scheme in manifest. hypertension comorbidity classes."),
    dict(key="hcw_fundus", modality="fundus_color", center="hcw",
         src=f"{NFS_BACKUP}/data0410/fundus_hcw.zip", comp="zip", extracted=False, exts=(".tif", ".png"),
         extract_glob="fundus/fundus_img/*", label_kind="xlsx", label_path=None, pii="low",
         dedup_group="data0410_pool", role="train",
         note="~15,503 imgs ONLY (extract_glob excludes 150G .pth + code!). dx-pairing xlsx keyed by image_name."),
    dict(key="data0410_master_fundus", modality="fundus_color", center="data0410",
         src=f"{NFS_BACKUP}/data0410/data.tar.gz", comp="targz", extracted=False, exts=(".png", ".jpg"),
         label_kind="xlsx", label_path=None, pii="t1", dedup_group="data0410_pool", role="train",
         note="104,505 (md-UNVERIFIED). master 二院 pool. reports_2020_2025_cleaned.xlsx has 姓名/PatientID/DOB (STRIP). ⚠ xlsx->image join key UNRESOLVED (datetime filenames vs patient key) -> label as none until join confirmed. dedup vs eryuan/hcw."),
    dict(key="eyfrd6_fundus", modality="fundus_color", center="eryuan",
         src=f"{DATASETS}/eyFRDCls", comp="none", extracted=True, exts=(".tif",),
         label_kind="folder", label_path=None, pii="low", dedup_group="fundus_pool", role="train",
         note="~645. 6 classes CSC/ERM/MH/RAO/RD/VKH (has train/val/test). tiny."),
    dict(key="handan_fundus", modality="fundus_color", center="handan",
         src=f"{NFS_BACKUP}/ffa/handan_ffa/only_image", comp="none", extracted=True, exts=(".png",),
         label_kind="json", label_path=f"{NFS_BACKUP}/ffa/handan_ffa/data.json", pii="t1",
         dedup_group="handan", role="external_test",
         note="6,432 fundus = filenames ending _1/_2 ONLY (must filter by suffix at enumerate). HELD OUT (邯郸 external). data.json 印象 dx; strip 姓名/年龄/检查日期/ID号."),

    # ===================== OCTA =====================
    dict(key="octbuckets_octa", modality="octa", center="octbuckets",
         src=f"{WORK}/Dataser/shards_oct", comp="none", extracted=True, exts=(".png",),
         label_kind="json", label_path=None, pii="none", dedup_group="octa", role="train",
         note="~34,519 WebDataset (de-id product; eye/scan_mode/frame). USE THIS, skip octBuckets.zip (raw, has name-in-filename). Is OCTA (Angio/en-face), NOT plain OCT."),

    # ===================== FFA (fluorescein angiography) -- NEW modality =====================
    dict(key="handan_ffa", modality="ffa", center="handan",
         src=f"{NFS_BACKUP}/ffa/handan_ffa/only_image", comp="none", extracted=True, exts=(".png",),
         label_kind="json", label_path=f"{NFS_BACKUP}/ffa/handan_ffa/data.json", pii="t1",
         dedup_group="handan", role="external_test",
         note="23,212 FFA = filenames _3 and higher suffix (filter by suffix). HELD OUT (邯郸 external). 3,226 study impressions; strip PII."),
    dict(key="chengdu_ffa", modality="ffa", center="chengdu",
         src=f"{NFS_BACKUP}/ffa/chengdu_ffa/jpg", comp="none", extracted=True, exts=(".png",),
         label_kind="jsonl", label_path=f"{NFS_BACKUP}/ffa/chengdu_ffa/visionoph4.jsonl", pii="low",
         dedup_group="chengdu_ffa", role="external_test",
         note="4,938 PNG (dir misnamed 'jpg', holds .png). HELD OUT (成都 FFA external). visionoph4.jsonl is a JSON ARRAY: scan_id/structured dx codes; normalize windows backslash paths."),
    dict(key="yinhai_ffa", modality="ffa", center="yinhai",
         src=f"{NFS_BACKUP}/ffa/yinhai_ffa/jpg", comp="none", extracted=True, exts=(".bmp",),
         label_kind="jsonl", label_path=f"{NFS_BACKUP}/ffa/yinhai_ffa/output2.jsonl", pii="t1",
         dedup_group="yinhai_ffa", role="train",
         note="1,058 BMP (dir misnamed 'jpg', holds .bmp). output2.jsonl 拟诊报告; strip 姓名+P-ID+签名."),
    dict(key="eryuan_ffa", modality="ffa", center="eryuan",
         src=f"{NFS_BACKUP}/ffa/Final_eryuan_FFA/FFA", comp="none", extracted=True, exts=(".tif",),
         label_kind="none", label_path=None, pii="low", dedup_group="eryuan_ffa", role="train",
         note="18,543 tif (verified). SKIP the redundant 72.5G Final_eryuan_FFA.zip. no labels."),

    # ===================== B-scan ocular ultrasound -- NEW modality =====================
    dict(key="yinhai_bscan", modality="bscan_us", center="yinhai",
         src=f"{NFS_BACKUP}/pretrainNewDS/BUltrasound/YinhaiBscan", comp="none", extracted=True, exts=(".png",),
         label_kind="json", label_path=None, pii="low", dedup_group="yinhai_bscan", role="train",
         note="71,289 (verified). images in image/ subdir. Use RAW json (超声所见 findings); cleanedv3 nulls findings. no name/dob."),
    dict(key="xiangya_bscan", modality="bscan_us", center="xiangya",
         src=f"{NFS_BACKUP}/pretrainNewDS/BUltrasound/XiangyaBscan", comp="none", extracted=True, exts=(".bmp",),
         label_kind="json", label_path=None, pii="t1", dedup_group="xiangya_bscan", role="train",
         note="33,740 (verified) bmp in image/. cleanedv3.json 4,939 records: findings + name/dob/examdate (STRIP)."),
    dict(key="chengdu_bscan", modality="bscan_us", center="chengdu",
         src=f"{NFS_BACKUP}/pretrainNewDS/BUltrasound/chengdu_Bscan", comp="none", extracted=True, exts=(".jpg",),
         label_kind="none", label_path=None, pii="none", dedup_group="chengdu_bscan", role="train",
         note="8,726 (verified) jpg flat. no labels."),

    # ===================== UBM (ultrasound biomicroscopy) -- NEW modality =====================
    dict(key="chengdu_shiyi_ubm", modality="ubm", center="chengdu",
         src=f"{NFS_BACKUP}/pretrainNewDS/ubm/ChengduShiyiUBM", comp="none", extracted=True, exts=(".png",),
         label_kind="json", label_path=None, pii="low", dedup_group="chengdu_ubm", role="train",
         note="236,028 (verified) png in image/. Use cleanedv3 json (clinical report; raw json has corrupted blobs/false-PII)."),
    dict(key="yinhai_ubm", modality="ubm", center="yinhai",
         src=f"{NFS_BACKUP}/pretrainNewDS/ubm/YinhaiUBM", comp="none", extracted=True, exts=(".png",),
         label_kind="json", label_path=None, pii="low", dedup_group="yinhai_ubm", role="train",
         note="83,750 (verified). cleanedv3 clinical text."),
    dict(key="eryuan_ubm", modality="ubm", center="eryuan",
         src=f"{NFS_BACKUP}/pretrainNewDS/ubm/Final_eryuan_ubm", comp="none", extracted=True, exts=(".tif",),
         label_kind="none", label_path=None, pii="low", dedup_group="eryuan_ubm", role="train",
         note="134,350 (verified) tif. truly no labels. SKIP the 2 redundant UBM .rar packs. ms-timestamp in filename (quasi-id)."),

    # ===================== UWF (ultra-widefield) -- NEW modality, single center =====================
    dict(key="yinhai_uwf", modality="uwf", center="yinhai",
         src=f"{NFS_BACKUP}/pretrainNewDS/uwf/yinhai_uwf", comp="rar5", extracted=False, exts=(".jpg",),
         label_kind="none", label_path=None, pii="low", dedup_group="yinhai_uwf", role="train",
         note="11,113 (md-UNVERIFIED). 3 RAR5 multipart sets (Secondary_1/_2/_3). extract first volume of each set with unrar. SINGLE CENTER -> no external val possible (paper limitation)."),
]

# Archives explicitly NOT ingested (dups / products-superseded / code). Documented for transparency; never deleted.
SKIP = {
    "rawFundus_predicted": "dup of fq_rawfundus (quality-resorted)",
    "testFundus_predicted": "dup of fq_testfundus",
    "eryuan_split.tar.gz": "subset of eryuan_fundus",
    "handan_ffa2": "strict subset of handan (0 unique files)",
    "Final_eryuan_FFA.zip": "redundant archive of eryuan_ffa extracted dir",
    "2-UBM-SW3200L-去噪声处理.rar / 3-UBM-SW3200L2-dataset.rar": "redundant with eryuan_ubm",
    "同仁/fundus.zip": "superseded by tongren_fundus shards (de-id)",
    "octBuckets.zip": "superseded by octbuckets_octa shards (de-id)",
    "tr/*.7z": "extracted copy tr1695L2 already present",
    "BioCliFM.tar.gz": "code, not data",
    "fundus_hcw .pth/code": "150G checkpoints+code; only images extracted via extract_glob",
}

INCLUDE = COHORTS
def by_role(role): return [c for c in COHORTS if c["role"] == role]
def to_extract(): return [c for c in COHORTS if not c.get("extracted")]
def modalities(): return sorted({c["modality"] for c in COHORTS})

	"""Verified cohort registry for the h100 cross-center new-data consolidation (2026-06-30).

	Single source of truth, encoding the 11-agent read-only recon (see memory reference_h100_newdata).
	HARD RULES baked in: originals on /nfs01 are READ-ONLY and NEVER modified/deleted. Archives
	extract ONLY to EXTRACT_ROOT (work dir on /data). Already-extracted dirs are read in place.
	Dedup/skip is done at MANIFEST level (drop rows), never by touching files.

	Each cohort dict:
	key unique cohort id (also the manifest `cohort` namespace)
	modality fundus_color \| octa \| ffa \| bscan_us \| ubm \| uwf
	center hospital/source (for multi-center splits)
	src path to the extracted dir OR the archive
	comp none \| zip \| targz \| rar5 \| 7z (archive type if not extracted)
	extracted True -> read `src` in place (0 disk); False -> extract to EXTRACT_ROOT/key
	extract_glob optional member filter for selective extraction (e.g. images only)
	exts image extensions to enumerate
	label_kind folder \| json \| jsonl \| xlsx \| none
	label_path label file (abs), or None (folder labels come from dir names)
	pii none \| low \| t1 (t1 = must strip identifiers before any export; see register)
	dedup_group cohorts sharing a group are cross-checked for duplicates
	role train \| external_test (external_test = held out, never trained)
	note
	"""
	import os

	EXTRACT_ROOT = "/data/team/lisicheng/new_data_extracted" # all extraction lands here (/data, 2.9T free); /nfs01 is read-only
	WORK = "/data/team/lisicheng"
	NFS_BACKUP = "/nfs01/datasetsBackup"
	NFS_FQ = "/nfs01/FQ_Datasets/data"
	DATASETS = "/data/datasets"
	UNRAR = "/data/team/lisicheng/miniconda3/envs/ocr/bin/unrar" # not on PATH; full path

	IMG_EXTS = (".png", ".jpg", ".jpeg", ".tif", ".tiff", ".bmp")


	def read_path(c):
	"""Where images are actually read from."""
	return c["src"] if c.get("extracted") else os.path.join(EXTRACT_ROOT, c["key"])


	# ----------------------------------------------------------------------------- COHORTS
	COHORTS = [
	# ===================== FUNDUS (color fundus photography) =====================
	dict(key="fundus90wer", modality="fundus_color", center="mixed_unlabeled",
	src=f"{DATASETS}/fundus90wER", comp="none", extracted=True, exts=(".png", ".jpg"),
	label_kind="none", label_path=None, pii="t1", dedup_group="fundus_pool", role="train",
	note="899,266 (verified). Chinese names in filenames -> t1 (filename PII only, not in pixels). md5 in cleaning_report usable as dedup key."),
	dict(key="fq_rawfundus", modality="fundus_color", center="fq_unlabeled",
	src=f"{NFS_FQ}/rawFundus", comp="none", extracted=True, exts=(".png",),
	label_kind="none", label_path=None, pii="low", dedup_group="fq_tongren_idname", role="train",
	note="208,138 (verified). <id>-<n>.png. SKIP rawFundus_predicted (same imgs, quality-resorted)."),
	dict(key="fq_testfundus", modality="fundus_color", center="fq_unlabeled",
	src=f"{NFS_FQ}/testFundus", comp="none", extracted=True, exts=(".png",),
	label_kind="none", label_path=None, pii="low", dedup_group="fq_tongren_idname", role="train",
	note="52,034 (verified). SKIP testFundus_predicted."),
	dict(key="fq_validfundus", modality="fundus_color", center="fq_unlabeled",
	src=f"{NFS_FQ}/validFundus", comp="none", extracted=True, exts=(".png",),
	label_kind="folder", label_path=None, pii="low", dedup_group="fq_tongren_idname", role="train",
	note="4,000 (verified). quality=gradable (whole-dir tag)."),
	dict(key="eryuan_t17_fundus", modality="fundus_color", center="eryuan",
	src=f"{NFS_FQ}/eryuanFundusT17RDCls", comp="none", extracted=True, exts=(".tif",),
	label_kind="folder", label_path=None, pii="low", dedup_group="eryuan_pool", role="train",
	note="31,621 (verified). 17 disease classes; has train/val/test -> reserve its test split as INTERNAL test. class = immediate parent dir."),
	dict(key="tongren_fundus", modality="fundus_color", center="tongren",
	src=f"{WORK}/Dataser/shards_tongren", comp="none", extracted=True, exts=(".png",),
	label_kind="json", label_path=None, pii="low", dedup_group="tongren_pool", role="train",
	note="~628,392 WebDataset shards (de-id product, sex/age in per-image json). USE THIS, skip 同仁/fundus.zip. WebDataset -> needs wds reader (see build_manifest stub)."),
	dict(key="tongren_external_fundus", modality="fundus_color", center="tongren",
	src=f"{NFS_BACKUP}/同仁/external.zip", comp="zip", extracted=False, exts=(".png",),
	label_kind="none", label_path=None, pii="low", dedup_group="tongren_pool", role="train",
	note="209,091 (verified). 99.5% SAME PATIENTS as tongren_fundus but 0 same-file -> OK as extra TRAIN, NEVER as eval (leakage)."),
	dict(key="tongren_95disease_fundus", modality="fundus_color", center="tongren",
	src=f"{NFS_BACKUP}/tr/tr1695L2/tongren16G95DFundus", comp="none", extracted=True, exts=(".png",),
	label_kind="folder", label_path=None, pii="low", dedup_group="tongren_pool", role="train",
	note="264,412 (verified). 16 groups / ~95 disease subclasses (folder). GOLDMINE for downstream. dedup vs tongren_pool by 编号. SKIP the tr .7z (extracted copy already here). class=parent, group=grandparent."),
	dict(key="eryuan_fundus", modality="fundus_color", center="eryuan",
	src=f"{NFS_BACKUP}/data0410/eryuan.zip", comp="zip", extracted=False, exts=(".tif", ".png"),
	label_kind="folder", label_path=None, pii="low", dedup_group="eryuan_pool", role="train",
	note="19,599 (verified): 4 classes AMD/DR/Normal/RVO (NOT 17 as md said). SKIP eryuan_split (subset). dedup vs data0410_master."),
	dict(key="dual_comorbidity_fundus", modality="fundus_color", center="data0410",
	src=f"{NFS_BACKUP}/data0410/Dual_comornidity.tar.gz", comp="targz", extracted=False, exts=(".png",),
	label_kind="folder", label_path=None, pii="low", dedup_group="data0410_pool", role="train",
	note="~12k unique (24,120 entries = 6-class scheme + 4-class scheme of SAME pool). Keep ONE scheme in manifest. hypertension comorbidity classes."),
	dict(key="hcw_fundus", modality="fundus_color", center="hcw",
	src=f"{NFS_BACKUP}/data0410/fundus_hcw.zip", comp="zip", extracted=False, exts=(".tif", ".png"),
	extract_glob="fundus/fundus_img/*", label_kind="xlsx", label_path=None, pii="low",
	dedup_group="data0410_pool", role="train",
	note="~15,503 imgs ONLY (extract_glob excludes 150G .pth + code!). dx-pairing xlsx keyed by image_name."),
	dict(key="data0410_master_fundus", modality="fundus_color", center="data0410",
	src=f"{NFS_BACKUP}/data0410/data.tar.gz", comp="targz", extracted=False, exts=(".png", ".jpg"),
	label_kind="xlsx", label_path=None, pii="t1", dedup_group="data0410_pool", role="train",
	note="104,505 (md-UNVERIFIED). master 二院 pool. reports_2020_2025_cleaned.xlsx has 姓名/PatientID/DOB (STRIP). ⚠ xlsx->image join key UNRESOLVED (datetime filenames vs patient key) -> label as none until join confirmed. dedup vs eryuan/hcw."),
	dict(key="eyfrd6_fundus", modality="fundus_color", center="eryuan",
	src=f"{DATASETS}/eyFRDCls", comp="none", extracted=True, exts=(".tif",),
	label_kind="folder", label_path=None, pii="low", dedup_group="fundus_pool", role="train",
	note="~645. 6 classes CSC/ERM/MH/RAO/RD/VKH (has train/val/test). tiny."),
	dict(key="handan_fundus", modality="fundus_color", center="handan",
	src=f"{NFS_BACKUP}/ffa/handan_ffa/only_image", comp="none", extracted=True, exts=(".png",),
	label_kind="json", label_path=f"{NFS_BACKUP}/ffa/handan_ffa/data.json", pii="t1",
	dedup_group="handan", role="external_test",
	note="6,432 fundus = filenames ending _1/_2 ONLY (must filter by suffix at enumerate). HELD OUT (邯郸 external). data.json 印象 dx; strip 姓名/年龄/检查日期/ID号."),

	# ===================== OCTA =====================
	dict(key="octbuckets_octa", modality="octa", center="octbuckets",
	src=f"{WORK}/Dataser/shards_oct", comp="none", extracted=True, exts=(".png",),
	label_kind="json", label_path=None, pii="none", dedup_group="octa", role="train",
	note="~34,519 WebDataset (de-id product; eye/scan_mode/frame). USE THIS, skip octBuckets.zip (raw, has name-in-filename). Is OCTA (Angio/en-face), NOT plain OCT."),

	# ===================== FFA (fluorescein angiography) -- NEW modality =====================
	dict(key="handan_ffa", modality="ffa", center="handan",
	src=f"{NFS_BACKUP}/ffa/handan_ffa/only_image", comp="none", extracted=True, exts=(".png",),
	label_kind="json", label_path=f"{NFS_BACKUP}/ffa/handan_ffa/data.json", pii="t1",
	dedup_group="handan", role="external_test",
	note="23,212 FFA = filenames _3 and higher suffix (filter by suffix). HELD OUT (邯郸 external). 3,226 study impressions; strip PII."),
	dict(key="chengdu_ffa", modality="ffa", center="chengdu",
	src=f"{NFS_BACKUP}/ffa/chengdu_ffa/jpg", comp="none", extracted=True, exts=(".png",),
	label_kind="jsonl", label_path=f"{NFS_BACKUP}/ffa/chengdu_ffa/visionoph4.jsonl", pii="low",
	dedup_group="chengdu_ffa", role="external_test",
	note="4,938 PNG (dir misnamed 'jpg', holds .png). HELD OUT (成都 FFA external). visionoph4.jsonl is a JSON ARRAY: scan_id/structured dx codes; normalize windows backslash paths."),
	dict(key="yinhai_ffa", modality="ffa", center="yinhai",
	src=f"{NFS_BACKUP}/ffa/yinhai_ffa/jpg", comp="none", extracted=True, exts=(".bmp",),
	label_kind="jsonl", label_path=f"{NFS_BACKUP}/ffa/yinhai_ffa/output2.jsonl", pii="t1",
	dedup_group="yinhai_ffa", role="train",
	note="1,058 BMP (dir misnamed 'jpg', holds .bmp). output2.jsonl 拟诊报告; strip 姓名+P-ID+签名."),
	dict(key="eryuan_ffa", modality="ffa", center="eryuan",
	src=f"{NFS_BACKUP}/ffa/Final_eryuan_FFA/FFA", comp="none", extracted=True, exts=(".tif",),
	label_kind="none", label_path=None, pii="low", dedup_group="eryuan_ffa", role="train",
	note="18,543 tif (verified). SKIP the redundant 72.5G Final_eryuan_FFA.zip. no labels."),

	# ===================== B-scan ocular ultrasound -- NEW modality =====================
	dict(key="yinhai_bscan", modality="bscan_us", center="yinhai",
	src=f"{NFS_BACKUP}/pretrainNewDS/BUltrasound/YinhaiBscan", comp="none", extracted=True, exts=(".png",),
	label_kind="json", label_path=None, pii="low", dedup_group="yinhai_bscan", role="train",
	note="71,289 (verified). images in image/ subdir. Use RAW json (超声所见 findings); cleanedv3 nulls findings. no name/dob."),
	dict(key="xiangya_bscan", modality="bscan_us", center="xiangya",
	src=f"{NFS_BACKUP}/pretrainNewDS/BUltrasound/XiangyaBscan", comp="none", extracted=True, exts=(".bmp",),
	label_kind="json", label_path=None, pii="t1", dedup_group="xiangya_bscan", role="train",
	note="33,740 (verified) bmp in image/. cleanedv3.json 4,939 records: findings + name/dob/examdate (STRIP)."),
	dict(key="chengdu_bscan", modality="bscan_us", center="chengdu",
	src=f"{NFS_BACKUP}/pretrainNewDS/BUltrasound/chengdu_Bscan", comp="none", extracted=True, exts=(".jpg",),
	label_kind="none", label_path=None, pii="none", dedup_group="chengdu_bscan", role="train",
	note="8,726 (verified) jpg flat. no labels."),

	# ===================== UBM (ultrasound biomicroscopy) -- NEW modality =====================
	dict(key="chengdu_shiyi_ubm", modality="ubm", center="chengdu",
	src=f"{NFS_BACKUP}/pretrainNewDS/ubm/ChengduShiyiUBM", comp="none", extracted=True, exts=(".png",),
	label_kind="json", label_path=None, pii="low", dedup_group="chengdu_ubm", role="train",
	note="236,028 (verified) png in image/. Use cleanedv3 json (clinical report; raw json has corrupted blobs/false-PII)."),
	dict(key="yinhai_ubm", modality="ubm", center="yinhai",
	src=f"{NFS_BACKUP}/pretrainNewDS/ubm/YinhaiUBM", comp="none", extracted=True, exts=(".png",),
	label_kind="json", label_path=None, pii="low", dedup_group="yinhai_ubm", role="train",
	note="83,750 (verified). cleanedv3 clinical text."),
	dict(key="eryuan_ubm", modality="ubm", center="eryuan",
	src=f"{NFS_BACKUP}/pretrainNewDS/ubm/Final_eryuan_ubm", comp="none", extracted=True, exts=(".tif",),
	label_kind="none", label_path=None, pii="low", dedup_group="eryuan_ubm", role="train",
	note="134,350 (verified) tif. truly no labels. SKIP the 2 redundant UBM .rar packs. ms-timestamp in filename (quasi-id)."),

	# ===================== UWF (ultra-widefield) -- NEW modality, single center =====================
	dict(key="yinhai_uwf", modality="uwf", center="yinhai",
	src=f"{NFS_BACKUP}/pretrainNewDS/uwf/yinhai_uwf", comp="rar5", extracted=False, exts=(".jpg",),
	label_kind="none", label_path=None, pii="low", dedup_group="yinhai_uwf", role="train",
	note="11,113 (md-UNVERIFIED). 3 RAR5 multipart sets (Secondary_1/_2/_3). extract first volume of each set with unrar. SINGLE CENTER -> no external val possible (paper limitation)."),
	]

	# Archives explicitly NOT ingested (dups / products-superseded / code). Documented for transparency; never deleted.
	SKIP = {
	"rawFundus_predicted": "dup of fq_rawfundus (quality-resorted)",
	"testFundus_predicted": "dup of fq_testfundus",
	"eryuan_split.tar.gz": "subset of eryuan_fundus",
	"handan_ffa2": "strict subset of handan (0 unique files)",
	"Final_eryuan_FFA.zip": "redundant archive of eryuan_ffa extracted dir",
	"2-UBM-SW3200L-去噪声处理.rar / 3-UBM-SW3200L2-dataset.rar": "redundant with eryuan_ubm",
	"同仁/fundus.zip": "superseded by tongren_fundus shards (de-id)",
	"octBuckets.zip": "superseded by octbuckets_octa shards (de-id)",
	"tr/*.7z": "extracted copy tr1695L2 already present",
	"BioCliFM.tar.gz": "code, not data",
	"fundus_hcw .pth/code": "150G checkpoints+code; only images extracted via extract_glob",
	}

	INCLUDE = COHORTS
	def by_role(role): return [c for c in COHORTS if c["role"] == role]
	def to_extract(): return [c for c in COHORTS if not c.get("extracted")]
	def modalities(): return sorted({c["modality"] for c in COHORTS})