File size: 14,507 Bytes
d2ce7dd | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 | """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})
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